eChapters

The role of farmers in Citizen Science is a rapidly evolving field that holds tremendous promise for transforming the way we approach agricultural research and development. By leveraging the knowledge, expertise, and experiences of farmers, Citizen Science initiatives can help address some of the complex challenges facing agriculture today, from climate change and soil degradation to water scarcity and pest management.
One of the key benefits of involving farmers in Citizen Science is that it allows for the integration of local knowledge and expertise into the research process. Farmers have a deep understanding of their land, crops, and ecosystems, which can provide valuable insights into the complex relationships between agriculture, environment, and society. By tapping into this knowledge, researchers can develop more effective and sustainable solutions to agricultural challenges.
Another advantage of farmer-led Citizen Science is that it can help to increase the relevance and effectiveness of research. By involving farmers in the design and implementation of research projects, researchers can ensure that their work is addressing the real-world needs and concerns of agricultural communities. This can help to bridge the gap between research and practice, and ensure that scientific knowledge is translated into practical solutions that benefit farmers and their communities.
Furthermore, the involvement of farmers in Citizen Science can also help to promote greater equity and inclusivity in agricultural research. By providing opportunities for farmers to participate in research, Citizen Science initiatives can help to empower marginalized communities and promote social justice. This can be particularly important in developing countries, where smallholder farmers often lack access to resources, information, and decision-making power. In addition, farmer-led Citizen Science can also contribute to the development of more sustainable and environmentally-friendly agricultural practices. By working together with researchers and other stakeholders, farmers can help to identify and promote practices that reduce environmental degradation, conserve natural resources, and promote ecosystem services.
Overall, the role of farmers in Citizen Science has the potential to transform the way we approach agricultural research and development. By leveraging the knowledge, expertise, and experiences of farmers, we can develop more effective, sustainable, and equitable solutions to the complex challenges facing agriculture today.

The scientific community has long relied on the expertise of trained researchers and professionals to advance our understanding of the world. However, with the increasing complexity of global challenges, such as climate change, biodiversity loss, environmental degradation, and public health concerns, it has become clear that a new approach is needed. The traditional model of scientific research, where experts work in isolation to collect and analyze data, is no longer sufficient to address the scale and urgency of these challenges. Citizen Science, which involves the active participation of the public in scientific research, has emerged as a powerful tool for addressing these challenges. By leveraging the collective efforts of citizens, researchers can tap into a vast pool of knowledge, skills, and perspectives, ultimately leading to more inclusive, equitable, and effective scientific inquiry. From monitoring water quality and tracking climate patterns to identifying species and analyzing satellite imagery, citizens are making meaningful contributions to scientific research (Fig. 3.1).
The benefits of Citizen Science are numerous. It provides opportunities for citizens to engage with scientific research, promoting a deeper understanding of the scientific process and its applications. It also enables researchers to collect data on a larger scale and in more diverse locations, leading to more comprehensive and accurate findings. Furthermore, Citizen Science has the potential to increase public trust in science, promote community engagement, and foster a sense of ownership and responsibility among citizens for environmental and societal issues.

Agriculture is the backbone of human civilization, providing food, fiber, and livelihoods for billions of people around the world. Crop yields, which are a critical component of agricultural productivity, are influenced by a complex array of factors, including genetics, soil quality, water availability, and management practices. However, among these factors, weather and climate stand out as two of the most significant drivers of crop yields.
Weather and climate are intimately linked, with weather referring to short-term atmospheric conditions, such as temperature, precipitation, and wind, and climate describing the long-term average atmospheric conditions in a particular region. Together, they form a dynamic duo that shapes the fate of crops, influencing their growth, development, and productivity.
The impact of weather and climate on crop yields is multifaceted. Weather events, such as droughts, floods, and heat waves, can have devastating effects on crops, leading to reduced yields, lower quality produce, and even complete crop failures. Climate, on the other hand, influences the overall growing conditions, determining the suitability of a region for specific crops and the timing of planting, growth, and harvest.
Understanding the complex interplay between weather, climate, and crop yields is crucial for ensuring food security, improving agricultural productivity, and promoting sustainable farming practices. This chapter aims to explore the intricate relationships between weather, climate, and crop yields, examining the ways in which these factors interact and influence each other. By delving into the latest research and findings, this chapter will provide insights into the following key topics:
The impact of weather events, such as extreme temperatures, precipitation, and wind, on crop yields
• The influence of climate on crop growth, development, and productivity
• The role of climate change in altering weather patterns and crop yields
• Strategies for mitigating the negative impacts of weather and climate on crop yields, such as climate-smart agriculture and weather-based crop insurance

1.1 Weather Forecasting
Farmers have always been at the mercy of the weather, and accurate forecasting has become an essential tool for their decision-making process. Weather forecasts play a critical role in supporting farmers’ decision-making processes, from crop planning and management to harvest planning and economic decision-making. Crop planning and management is one of the key areas where weather forecasts are crucial. Farmers use weather forecasts to determine the best time to plant seeds, taking into account temperature, moisture, and sunlight requirements. This enables them to choose crops that are suitable for the predicted weather conditions. Additionally, forecasts help farmers optimize irrigation, reducing water waste and ensuring crops receive adequate moisture (Fig.1.1).
Weather forecasts also help farmers mitigate weather-related risks. Extreme weather events like floods, droughts, heat waves, and storms can have devastating effects on crops. Accurate forecasts enable farmers to prepare for and respond to these events, reducing crop damage and loss. For instance, forecasts help farmers take measures to protect crops from frost and freeze damage, which can be catastrophic for certain crops.

1. Introduction
Pharmacognosy, formerly known as materia medica, is the study of unrefined medications derived from minerals, plants, and animals. Although C. A. Seydler coined the phrase in 1815, J. A. Schmidt used it in 1811 to refer to the study of medicinal plants and their characteristics. The word refers to the study or knowledge of medicines and is derived from the Latin terms gignoso, which means “to acquire knowledge of,” and pharmakon, which means “a drug.” Plants, animals, or their parts that have just been dried, sliced longitudinally or transversely, or infrequently peeled after collecting are considered crude pharmaceuticals. The majority of the basic drugs used in medicine are derived from plants, with only a minor portion coming from the animal and mineral kingdoms. Drugs made from plants are full plants, however cinchona bark, ginger rhizome, nux vomica seeds, and senna leaves and pods are plant components. Some medications, such as orange and lemon peels and colchicum corm, are used fresh, but the majority are dried after harvesting. Crude pharmaceuticals can also be obtained by straightforward physical processes like drying or water extraction. Aloe is the dried juice of Aloe species’ leaves, opium is the dried latex from poppy capsules, and black catechu is the dried aqueous extract from Acacia catechu’s wood. Plant exudates such as gums, resins, balsams, volatile oils, and fixed oils are also considered crude medicines. Additional drugs that physicians, surgeons, or pharmacists use directly or indirectly include diatomite for filtering gums or turbid liquids; cotton, silk, jute, and nylon for surgical dressings or kaolin; wax, gelatin, and agar for use as pharmaceutical auxiliaries of flavoring or sweetening agents; and drugs used as insecticides or vehicles. Animal medications can be extracts, like liver extracts, glandular products, such thyroid organs, or whole animals, like can-tharides. Similarly, goods that come from animals include fish liver oils, bee wax, musk, certain hormones, enzymes, and antitoxins. Drugs can be classified as either organized or disorganized. Organized medications come directly from plants and are composed of biological tissues. Unorganized pharmaceuticals are generated by physical intermediary procedures such as cutting, drying, or extraction with water, and although being derived from plants, they do not contain cellular tissue. Thus, unstructured medications include gums, resins, aloe, catechu, opium, and other plant exudates. Kaolin, chalk, diatomite, and other Ayurvedic bhasmas are examples of mineralbased medicines.

1. Introduction Plants naturally contain compounds called phytochemicals. Due to their numerous therapeutic applications, these phytochemicals are becoming more and more well-known these days. Phytochemicals are essential in the fight against a variety of illnesses, including cancer, arthritis, and asthma. These phytochemicals have no side effects like pharmaceutical compounds do. Because the phytochemicals treat illnesses without harming people, they can also be referred to as “man-friendly medicines.” The primary topics of this study are phytochemical collection, extraction, and qualitative and quantitative analysis. In order to protect themselves against herbivores, many plants secrete chemical substances. Examples of therapeutic plants that include the main groups of pharmacologically active phytochemicals are provided below. Weeds that contain phytochemicals, like chickweed, dandelion, and nettle, are frequently found surrounding human settlements. Many phytochemicals, including curcumin, epigallocatechin gallate, genistein, and resveratrol are pan-assay interference compounds and are not useful in drug discovery.

1. Definition and Scope Pharmacognosy is the study of drugs and other economically valuable materials derived from natural sources, encompassing both plant and animal origins. This field examines a wide spectrum of aspects related to these materials, including their history, production, commerce, collection, selection, identification, valuation, preservation, and ultimately, their use. The term “pharmacognosy” was first formally used by C. A. Seydler in his 1815 dissertation, Analecta pharmacognostica. The word itself is derived from the Greek terms pharmakon, meaning drug or medicine, and gnosis, meaning knowledge. Thus, pharmacognosy literally translates to “knowledge of drugs.” It’s crucial to differentiate pharmacognosy from pharmacology due to their distinct areas of focus within the realm of drugs. Pharmacology, aptly defined as “a discourse on drugs,” centers on investigating the effects of drugs and other chemical substances on living systems, be it organs or entire organisms. In contrast, pharmacognosy directs its attention to the origins, identification, and inherent properties of natural products – often from plants, but also including other biological sources – that exhibit potential medicinal or economic significance. Pharmacognosy, by its very nature, is an interdisciplinary science, integrating knowledge from a variety of scientific domains. A strong foundation in botany is fundamental for the accurate identification and classification of medicinal plants. Chemistry is indispensable for elucidating the chemical constituents of these natural products and understanding their characteristics. Zoology provides insights into drugs and materials derived from the animal kingdom. Moreover, the principles of physics and genetics are applied to tackle diverse issues concerning the identity, purity, quality control, and effective preservation of these important natural resources.

For millennia, humans have forged an intimate relationship with the natural world, recognizing and harnessing the therapeutic potential of plants for healing. This unit, “Introduction to Herbal Medicines,” embarks on a comprehensive exploration of plant-based therapies, delving into their historical evolution, fundamental terminology, integration within traditional healing systems, and the practical aspects of their cultivation, processing, and utilization. We aim to provide a solid bedrock of knowledge, enabling a deeper understanding of the science and practice of herbal medicine, and appreciating its enduring significance in the modern world."
1. History and Scope of Herbal Medicines
1.1 Ancient Beginnings: The Dawn of Plant-Based Healing
• Paleolithic Roots: Our most ancient ancestors, from the Paleolithic period, are believed to have harbored a refined comprehension of the pharmacological attributes of vegetation. Excavations have unearthed indications of specific plant utilization for addressing physical trauma and maladies, underscoring an early grasp of their remedial efficacy. This knowledge was likely passed down through oral traditions, forming the foundation of future herbal practices.
• Civilizational Emergence: With the rise of civilizations, medicinal plant use became more structured and recorded. In ancient Sumeria, clay tablets inscribed with cuneiform script, dating back to 3000 BC, record the use of various plants for medicinal purposes, showcasing organized knowledge of herbal remedies. The Egyptian Ebers Papyrus (c. 1550 BC), a remarkable medical text, provides detailed prescriptions for treating a wide array of ailments using plants like aloe vera, garlic, and opium, evidencing a sophisticated understanding of pharmacotherapy. In India, the ancient system of Ayurveda, with its foundational texts, the Charaka Samhita and Sushruta Samhita, meticulously documents the use of numerous medicinal plants, emphasizing a holistic approach to health. Similarly, Traditional Chinese Medicine (TCM), with the Shennong Bencao Jing (c. 1st century AD), provides a comprehensive pharmacopeia, demonstrating the profound integration of herbal remedies into Chinese medical practice.

1. Introduction
Drought’ which apparently means lack of water, is one of most vulnerable natural hazards. It alone causes a huge crop loss annually. But history says it not only affects crop but destroys human lives.in the year 1972 almost 600000 people died due to starvation caused by huge drought in Northern India. In simple language drought means shortage of water mainly caused by abnormal climatic condition and damaging to the environment. Ideally there is no such proper definition of drought because it should be region specific and circumstances specific. A per McMahon and Diaz Arenas (1982) define drought as “a period of abnormally dry weather sufficiently prolonged for the lack of precipitation to cause a serious hydrological imbalance, carrying connotations of a moisture deficiency with respect to man’s usage of water”. Apart from these several general definitions have been provided by many authors. Beran and Rodier (1985) gave a definition based on hydrological aspect of drought that, ‘The chief characteristic of a drought is a decrease of water availability in a particular period and over a particular area’. India meteorological department (IMD) has defined drought as a situation in a subdivision in a year when the actual rainfall is less than 75% of the normal annual rainfall. Basically, to define drought the impact on environment and society and their complex interaction must be considered. Drought is defined as a state of total water capacity being within the range of 12-20% for a period of 16 days and can be distinguished from water deficit, which is the state of water capacity falling below 30%. Drought affects crop development, production, productivity and quality and on other hand soil physical, chemical and biological properties.
2. Classification of Drought
As per the hydrological point of view drought can be classified into different Categories: i) On the basis of source from which water is available: -a. Meteorological Drought b. Hydrological drought c. Agricultural Drought. ii) On the basis of time of occurrence: - a. permanent drought area b. Seasonal Drought Area contingent drought. iii) On the basis of medium: - a. soil drought atmospheric drought.

1. Introduction
The amount of water present in the soil is called as soil moisture. The soil moisture availability is an important topic to study as it affects plant growth, agricultural outputs and the water resource management as well. The amount of soil moisture present in a place and in a time depends on the factors like soil type, Land use, vegetation cover of the place and evaporation taking place in that area at that time. The amount of water hold by the pores in the soil determines the soil moisture. This water holding by the soil depends on various factors like temperature, moisture levels, organic matters and the soil living organisms (Rasheed et al., 2022). Essential Climate variable (ECV) is very important to our planet which can be recognized by GCOS (Global Climate Observing System). Important sustainable development issues say drought, flood management, food security, ecological health and water resources are being impacted by the soil moisture. Soil moisture changes have also a role in evapotranspiration, rainfall, and the runoff distribution and intensities. (Zeng et al., 2023).
Soil moisture content (SMC) have a huge impact in shaping the water, carbon cycles of earth and energy balance in different scales of time and space. It is an important factor for the effective water management due to its influence on the precipitation, surface temperature and evaporation which will be affecting the processes say river runoff, vegetation health, and irrigation practices. The highcost traditional methods make to overlook for SMC in environmental modelling (Edokossi et al., 2020). The atmospheric and the land surface process are blinked by the key facto of SMC as said by Robinson et al. (2008). For gaining the knowledge on water, carbon cycles and energy as well as for studying extreme weather conditions, understanding soil moisture is very much needed. (Vereecken et al., 2014; Li et al., 2007; Seneviratne et al., 2010; Robock & Li, 2006).
Due to the soil moisture’s impact on the factors say when to plant, when to blossom and when to harvest the crop, soil moisture should be considered as the important factor as it is in improving the water resources and boost the agricultural output. Remote sensing methods like microwave remote sensing plays vital role in creating maps depicting the distribution of soil moisture spatially and temporally high resolutions. These maps help in effective crop planning, in-season drought assessment, irrigation and soil managements. The traditional methods like TDR probe, Soil moisture utilizing neutron probe and gravimetric methods are inefficient in studying the soil moisture with high spatial and temporal resolutions due to factors like Variations in terrain, soil type and land use pattern. We can overcome these and enhance our ability to map soil moisture in different scales through the recent advancements of microwave remote sensing (active and passive microwave remote sensing) which are having advantages like clear, accurate and timely measurements of soil moisture content.
Irrigation water sources are controlled through the soil moisture studies by executing the sustainable water management plans and by monitoring high yields despite the environmental changes, increasing water demands and climate changes. To predict the agricultural output, irrigation management, drought early warning, creating vegetation indices, soil moisture study with high temporal and spatial resolutions will be a key factor as it improves the crop stress reduction also which are the major driving forces of yield reduction (Doraiswamy et al., 2004). So, the soil moisture has a direct impact on the crop yield which are studied using variable rate technology say selective application of pesticides which can improve farmers financially as well as environmentally (Harris et al.,1964).

1. Introduction
Soil salinity is a global threat which affects crop production and poses challenges to sustainable development in various regions worldwide. Worldwide, salinity and sodicity impact 20% of cultivated land and 33% of irrigated land, respectively (Kumar and Sharma, 2020). Global map of salt–affected soil (SAS) indicated that 0.424 billion hectares of upper soil layer (0–0.3 m) and 0.833 billion hectares of subsoil layer (0.30–1 m) are salt-affected (FAO, 2021) and by 2050, salinization will degrade 50% of agricultural land (Wang et al., 2003). In India, SAS currently occupy 2.1% of total geographical area. Among the SAS, 2.96 million ha is saline and 3.77 million ha is sodic (Narjary et al., 2024). Due to salinity and sodicity, yearly 16.8 million megagram of agricultural produce are wasted in India, costing of ? 230.2 billion (Sharma et al., 2015).
Salt–affected soils are a significant concern in several regions of India. These soils contain high level of sodium salts (NaCl, Na2SO4, NaHCO3, Na2CO3) which adversely affect soil fertility and crop productivity. Here are some areas in India known to have salt–affected soils:
1. Indo–Gangetic Plain: The Indo–Gangetic Plain, spanning across states like Punjab, Haryana, Uttar Pradesh, Bihar, and parts of West Bengal, is prone to salinity issues. Over–irrigation, poor drainage, the presence of salt containing parent materials in the lithology and use of saline irrigation have contributed to soil salinity.
2. Coastal Regions: Coastal regions in India, such as parts of Gujarat, Maharashtra, Tamil Nadu, Andhra Pradesh, Karela, Odisha and Andaman and Nicobar Islands experience salinity problems due to sea water intrusion and the presence of coastal saline soils. These soils are affected by high levels of sodium chloride and other salts, making them unsuitable for cultivation of most arable crops.
3. Arid and Semi–Arid Regions: Arid and semi–arid regions, including areas in Rajasthan, Gujarat, and parts of Madhya Pradesh, face the risk of soil salinization due to low rainfall, high evaporation rates, and poor– quality irrigation water. Waterlogging in these regions can also lead to soil salinization.
4. Irrigation Command Areas: Certain irrigation command areas in different states, including Punjab, Haryana, and Uttar Pradesh, have witnessed salinity problems as a result of improper water management, excessive irrigation, and inadequate drainage infrastructure.

1. Introduction
As climate change advances, risk of managing soil and atmosphere with regard to emission and pollution is going out of hand. Many researches and reports say that currently the global warming is 1.1°C more than the pre-industrial levels, which is not far behind the target of 1.5°C which was agreed by nations under the Paris agreement in 2015 (Janki M, 2023). Achieving that will likely involve removing carbon dioxide from the atmosphere, according to the Intergovernmental Panel on Climate Change. But strategies like capturing and storing the carbon emissions from biofuel-burning power plants, or planting new forests to absorb carbon, can create their own problems. Recently UN security general said we are shifting to the “Era of global boiling” from global warming. Globally countries are witnessing unbearable heat waves, forest fires, irregular rainfall, flashfloods, coral bleaching and unpredictable weather patterns which often resulting in devastating effects on the coastal and hilly regions of the country. Around nine tipping points have been identified so far globally. Climate tipping points are threshold that when crossed leads to irreversible changes in the climate system and will have a severe impact on the living organisms on the earth. All these variations in climate are driving by increasing greenhouse gas (GHG) emissions and global warming. We cannot change the state of atmosphere to as existed thousands or millions of years ago. Only option left with us is to reduce emissions and prevention of further global warming and climate change. As we are already existing in some level of changed climate, responding to climate change involves a two-pronged approach, reducing emissions of and stabilizing the levels of heat-trapping greenhouse gases in the atmosphere through mitigation and secondly, adapting to the climate change. Because climate change is irreversible process.

1. Introduction
Fish and fisheries remains to be an important sector for Indian economy providing food, nutrition as well as employment to a large share of the population. Initiating with a production of merely 7.7 lakh ton of fishes in 1951-52, the country was on its way of increasing the production steadily and has now attained the phenomenal success of producing 162.5 lakh ton during 2021-22. Of this total amount, 41.3 lakh ton has been obtained from marine fisheries and 121.2 lakh ton from inland resources (Anon, 2022). For the inland fisheries production, the contribution of fish culture sector was much lower than that from capture fisheries for several decades during the initial phases. However, the production of culture fisheries maintained a consistently increasing trend and has now surpassed in magnitude the production from capture fisheries sector (Fig.1). With the potential areas for fish culture being gradually used up for different purposes, maintaining such high production rates in coming days will be possible only through optimum management of various factors which govern the productivity of the ponds. Among such factors, one important aspect is nourishing the quality of pond soil and water.

1. Introduction
Studies on soil health and agricultural growth are increasingly using artificial intelligence (AI) and machine learning (ML) techniques. With the help of these technologies, it is possible to analyse vast amounts of data and gain insightful knowledge that will help to boost crop yields and optimise agricultural practises (Neethirajan, 2020). Applications of AI and ML to research crop development and soil health have enormous potential to revolutionise agriculture. In order to increase soil health and crop yield, farmers and researchers can use these technologies to analyse massive datasets, generate precise forecasts, and optimise farming practises. Overall, artificial intelligence (AI) and machine learning (ML) provide useful tools that can support efficient and sustainable agriculture, solving global concerns in food availability and the sustainability of the environment. Unquestionably, technical developments have significantly increased the productivity and efficiency of many industries, including agriculture. The advent of words like “big data,” “data analytics,” “artificial intelligence,” “Internet of Things,” “erosion modelling,” “smart farming,” and “machine learning” are just a few examples of this technological revolution (Almoussawi et al., 2022). Digital soil mapping (DSM) uses computational models to infer regional and temporal shifts of soil types and properties based on soil observations, prior knowledge, and relevant environmental variables in order to create and populate spatial soil information systems. The supply chain for agricultural production is extremely intricate (Lamichhane et al., 2019). The way our food is produced, distributed, and consumed is changing as a result of AI. When planning crop rotations, planting times, water and nutrient management, pest and disease control, optimal harvesting, food marketing, product distribution, food safety, and other agriculture-related tasks throughout every step of the food supply chain, researchers use powered by AI tools to supply advice and expertise. In “Harnessing AI to transform agriculture and inform agricultural research,” Peters et al. (2020) presented a summary of the most recent developments, difficulties, and prospects for AI technology in agriculture. They use the four main elements of the food system—production, distribution, consumption, and uncertainty—to show the possibilities of AI. They come to the conclusion that agricultural businesses are excellent candidates for using AI and other technologies. In “AI down on the farm,” Sudduth et al. (2020), examined a number of case studies in which machine learning (ML) has been used to model various aspects of agricultural production systems and give data that can be utilised to make management decisions at the farm level. These research efforts involve providing data, essential for creating precise and effective irrigation systems and improving tools for suggesting the best nitrogen fertilisation rates for maize. Traditional crop health monitoring techniques need a lot of work and time. Using AI to monitor and detect potential crop problems or nutrient deficits in the soil is an effective method. Applications to analyse plant health trends in agriculture are being created with the aid of deep learning. These AI-powered tools are essential for improving our understanding of soil quality, crop pests, and disease in plants (Virnodkar et al., 2020).

1. Introduction
Sustainable crop production describes farming practices that do not negatively impact the environment, biodiversity, natural agroecosystem, or the quality of the crops produced. Growing crops sustainably improves the system’s capacity to sustain long-term, steady, and uniform levels of food production and quality without raising the need for or increasing the demand for agricultural inputs necessary for effective management. Sustainable crop production deals with keeping the soil healthy with the integrated approach of maintaining organic matter, protecting biodiversity while reducing use of pesticides, ensuring food safety and food quality. It is an approach to grow food to feed our population without adversely affecting the natural ecosystem, soil and biodiversity, natural resources, environment etc. Since the soil is one of the key natural resources for food production, establishing and preserving soil health is crucial for agricultural sustainability and ecosystem function. The distinction between soil quality and health is quite narrow, and the phrases are sometimes used interchangeably. The “fitness for use,” “capacity of a soil to function,” or “the ability of the soil to serve as a natural medium for the growth of plants that sustain human and animal life” are some basic definitions of soil quality (Karlen et al., 1992; Pierce & Larson, 1993; Doran & Parkin, 1994; Acton and Gregorich, 1995; Karlen et al., 1997). Assessment of soil health by analyzing different associated attributes is necessary to monitor its status. However, soil health or quality evaluation focuses on the physical, chemical, and biological parameters which are highly interlinked to one another. All the parameters have their significant effect in governing overall soil health. This chapter will discuss the chemical parameters only which are very much important for the sustainable crop production and have very crucial role in determining the soil health. It will cover important chemical parameters, essential plant and soil nutrients, heavy metals, agrochemicals etc. which having significant effect on soil health determination

1. Introduction
Global food security threatened by climate change is one of the most important challenges in the 21st century to supply sufficient food for the increasing population on diminishing agricultural land while sustaining the already stressed environment. Global agricultural production is highly vulnerable as key crops such as maize, wheat, rice, and legumes will be negatively affected by climate change. The study showed that each degree Celsius increase in global mean temperature is estimated to reduce the average global yield of cereal crops by 6-15%. Further, predicted changes in climate may affect water availability and key soil processes such as respiration and nitrogen (N) mineralization, thus key ecosystem functions such as carbon (C) storage, nutrients, and water availability. Although crop growth simulation models are being utilized to understand the impact of climate change scenarios on aboveground biomass, the roots and their architecture, which are essential for plant adaptation and productivity, need to be studied more due to the difficulty of observing them during the plant life cycle. The root system and its architecture are highly responsive to external environmental conditions such as soil moisture, nutrients, temperature, pH and microbial communities; therefore, it is high time to study below-ground responses to future climate scenarios. The study of plant roots represents a highly promising yet underexplored area of research related to plant growth. While the aerial parts of plants have received significant attention due to their visibility and accessibility, the underground portions have been largely overlooked due to the challenges in observing and sampling them and the disruption caused when roots are removed from the soil. Plants’ efficiency of water and mineral nutrient uptake largely depends on how effectively new root tips penetrate soil horizons and how these roots eventually die and decompose. The plant’s ability to access soil resources depends on the size of its root system and the activity of its growing root tips. Root growth is influenced by both genetic factors and environmental conditions. For example, fine root dynamics vary with the seasons, with increased vegetative growth and nutrient release occurring during the monsoon months. (Kumar et al., 2020). Further research is needed to assess how different tillage and water management practices affect rooting behaviours, soil water extraction, and nutrient uptake.

1. Introduction
Soil is an extremely complex ecosystem and performs many functions, i. e, biomass production; food contingency, fuel and fibre; buffering and transforming actions; breakdown of organic matter; nutrients recycling; carbon sequestration; providing a biological habitat; source of raw materials; and regulation of water quality and supply (Weil and Brady, 2017; Telo da Gama, 2023). However, because of natural as well as anthropogenic reasons, there is a widespread degradation of soils. Soil degradation means deterioration in overall soil functioning which leads to slowdown of distribution of ecosystem services because of the impairment of soil health through anthropogenic perturbations. Therefore, farmers and researchers are very much concerned about soil health and crop productivity. Soil health status and its quality are often used interchangeably and have been defined by research community differently but recently the word soil health is acquiring popularity due to its flexibility in allowing diverse stakeholders, including policymakers to use the term in their own way. Recently, soil health is a topic of much discussion and research (Bünemann et al., 2018). Soil health is described as “the continued capacity of soil to function as a vital living ecosystem that sustains plants, animals, and humans” (USDA-NRCS, 2019). A broader, ecologically based approach is “the capacity of soil to function as a vital living system, within ecosystem and land-use boundaries, to sustain plant and animal productivity, maintain or enhance water and air quality, and promote plant and animal health” (Doran et al., 2000). Soil health refers to the actual capacity of a soil to function, contributing to ecosystem services. Many have defined it as “fitness for use” while the rest regard as “capacity of the soil to function”. Lehmann et al. (2020) stated that the concept of soil health “connects agricultural and soil science to policy, stakeholder needs and sustainable supply-chain management” and compared the soil health concept to soil fertility, soil quality and soil security. Furthermore, they came to a conclusion that soil health is narrower concept than soil security, but more comprehensive than soil quality and even broader than soil fertility, in relation to the current sustainable development goals (SDGs) as proposed by United Nations. From an overall perspective. Soil health comprises of refinement and unification of the chemical, physical, physico-chemical, biophysical, biochemical indicators of soil that supports productivity and environmental quality (Figure 1). Soil can be classified as healthy or unhealthy based on its ability to sustain plant and animal productivity while maintaining air and water quality.

1. Introduction
Global food insecurity is a prominent concern today, and this concern continues to haunt with exponential population growth, coupled with significant climate changes. The global population has increased at a rapid rate to around 8 billion in number which has led to a corresponding rise in food demand worldwide (UNFPA, 2023). Consequently, ensuring ample crop production has emerged as a primary objective for the entire world. According to World Bank (2023), an important strategy to bring down extreme poverty levels, fostering shared prosperity and providing sustainability for the expected population rise of 9.7 billion is agricultural development by 2050. Nevertheless, optimizing the productivity of a crop is never an easy task. Crop plants continuously face exposure to different abiotic and/or biotic factors, which retard growth and development processes ensuing their reduced productivity and diminished crop quality. Agricultural crops are vulnerable to biotic stresses such as bacteria, nematodes, fungi and herbivores. In addition to biotic factors, these challenges are further exacerbated by abiotic stresses like flooding/waterlogging, drought, temperature variations, soil salinity/alkalinity, heavy metals, nutrient deficiencies and CO2. They have a detrimental impact on crop at different stages of plant growth, ranging from entire plant to tissue and sub-cellular structures. Moreover, the abiotic stressors cause a direct impact on crops by inducing protein denaturation and aggregation, while also increasing the fluidity of membrane lipids (Kumari, 2022). According to Oshunsanya et al. (2019) major abiotic stresses i.e. extreme temperatures, drought, and nutrient deficiency/toxicity, contributes to significant annual crop yield losses ranging from 51-82% worldwide. On the other hand, yield penalties due to waterlogging merely is supposed to increase from 3–11% today to around 10–20% by 2080 (Liu et al., 2023). Daryanto et al. (2016) reviewed the published literatures from 1980-2015 and reported global yield reductions due to drought estimating around 21 % and 40%, respectively in wheat and maize.

1. Introduction
Nanotechnology, which involves manipulating and controlling matter at the nanoscale, offers innovative solutions for enhancing soil quality, nutrient management, and remediation of contaminated soils (Lal et al., 2019). According to the Food and Agriculture Organization (FAO, 2015), approximately 33% of global soils are degraded due to erosion, nutrient depletion, and chemical contamination. This degradation poses a severe threat to food security and environmental sustainability. Moreover, the world population is projected to reach 9.7 billion by 2050, placing further pressure on agricultural systems to produce more food without degrading soil resources (Tripathi et al., 2019). In this context, nanotechnology presents a promising avenue for sustainable soil management. The unique properties exhibited by nanomaterials enable precise monitoring, targeted delivery of nutrients, and effective remediation of soil contaminants. Nanosensors equipped with nanomaterials and nanoparticles allow real-time monitoring of soil parameters, enabling farmers to make informed decisions regarding irrigation, fertilization, and pest management (Gogos et al., 2017). For instance, a case study conducted by Smith et al. (2021) demonstrated the effectiveness of nanosensors in monitoring soil moisture levels and optimizing irrigation practices, resulting in a 20% reduction in water usage while maintaining crop yield.
Furthermore, nanotechnology offers innovative solutions for nutrient management in agricultural soils. Nanoencapsulation techniques allow controlled release of fertilizers, minimizing nutrient leaching and improving nutrient uptake by plants (Lal et al., 2019). In a case study by Johnson et al. (2020), the application of nanoencapsulated fertilizers resulted in a 15% increase in nutrient use efficiency and a reduction in fertilizer runoff by 30%, thus improving soil health and reducing environmental pollution.
Moreover, nanomaterials exhibit high sorption and catalytic properties, enabling efficient removal of heavy metal contaminants and organic pollutants from soils, thus mitigating soil contamination risks (Wang et al., 2022). A case study conducted by Chen et al. (2019) demonstrated the successful remediation of a heavily contaminated soil site using iron oxide nanoparticles, resulting in a significant reduction in heavy metal concentrations and improvement in soil health indicators.

1. Introduction
Precision farming, often referred to as precision agriculture, is an advanced agricultural management approach that leverages technology and data-driven strategies to optimize various aspects of farming operations. At its core, precision farming involves the precise measurement and control of variables like soil conditions, crop growth and environmental factors to enhance productivity, sustainability and resource efficiency. This approach encompasses use of a wide range of automated technologies, including Global Positioning System (GPS), Geographic Information Systems (GIS), remote sensing, sensor networks and automated machinery.
The significance of precision farming lies in its potential to revolutionize agriculture by addressing sustainability and conservation of resources. Precision farming is able to tailor farming practices to specific field conditions. This sitespecific management approach minimizes resource wastage, incumbent costs and environmental hazards. It enhances crop management through real-time monitoring and data analysis. By the use of GPS fitted automated machinery, drones and robotic harvesters and advanced decision support system labour costs are reduced and efficiency is increased (Gebbers and Adamchuk, 2010).By employing soil sensors, and satellite imagery, farmers can gain valuable insights of soil moisture, nutrient levels and pest infestations which helps in irrigation scheduling and adequate fertilizer application (Srinivasan et al., 2019). In contemporary agriculture, the need for sensor-based approaches in soil management has become increasingly apparent. These approaches harness cuttingedge technology and data-driven solutions to optimize soil health, crop production and resource utilization. One of the primary advantages of sensor-based approaches is their ability to monitor soil moisture levels. Soil moisture sensors, such as those based on time-domain reflectometry (TDR) or capacitance technology, provide continuous data on soil moisture content at different depths (Dzikiti et al., 2012). In addition to monitoring moisture and nutrients, sensors can assess other vital soil parameters. Soil pH sensors help maintain the optimal pH level for crop growth, as soil pH can affect nutrient availability. Compaction sensors identify areas of soil compaction, allowing for targeted tillage practices to alleviate compaction and improve root growth. These are just a few examples of how physical parameter sensors can address soil health challenges, as documented in studies by Marchetti et al. (2018) which emphasizes the role of sensors in mitigating soil erosion. Chemical parameters sensors like soil nutrient sensors, such as ion-selective electrodes, can measure key nutrients like nitrogen, phosphorus and potassium. This information enables farmers to tailor their fertilization practices with pinpoint accuracy through variable rate technology (VRT) (Zhao et al., 2018). Moreover, sensor data can be integrated with Geographic Information Systems (GIS) and global positioning systems (GPS) to create detailed soil maps, aiding in sitespecific management strategies.

1. Introduction
Soil hydrothermal regime refers to the distribution and movement of water and heat within the soil. It describes the patterns of soil moisture and temperature variation over time and space. Understanding the hydrothermal regime is important in agriculture, ecology, and engineering, as it affects various soil processes, plant growth, and the behaviour of soil-related structures. The hydrothermal regime of soil is influenced by several factors, including climate, topography, vegetation, soil properties, and land use practices. The movement of water within the soil is driven by precipitation, irrigation, evaporation, and plant uptake (Aggarwal et al., 2009). It can be affected by soil physical properties such as soil texture, structure, porosity, and permeability. Water moves through the soil in response to hydraulic gradients, which are determined by the difference in soil water potential. Soil temperature is influenced by solar radiation, air temperature, and heat fluxes at the soil surface. It varies with depth and can be affected by factors such as soil color, moisture content, vegetation cover, and the thermal properties of the soil. The hydrothermal regime of soil has significant implications for plant growth and development. It affects processes such as seed germination, root growth, nutrient availability, and microbial activity. It also plays a crucial role in soil erosion, groundwater recharge, and the movement of pollutants within the soil profile. Soil’s thermal properties are pivotal in determining the microclimate, which subsequently influences critical stages like seed germination, the growth of seedlings, and the successful established of a crop stand. These properties are vital for modelling the movement of energy, water, and nutrients in soil. Soil thermal regimes are shaped by static properties such as texture and mineralogy and dynamic properties including water content, compaction, organic matter, and porosity. These dynamic properties including water content, compaction, organic matter, and porosity. These dynamic properties, significant to significant change during various farming operations can be adjusted with suitable soil and crop management strategies. Soil moisture content has a significant effect on the soil thermal conductivity. The relationship between soil moisture and thermal conductivity

1. Introduction
Water is the most crucial input for agricultural production. Globally, more than 80% of all freshwater used by humans are consumed in agriculture and most of it are for crop production (Morison et al., 2008). Currently about 60% of production in developing countries is derived from rainfall. Though irrigation provides only 10% of agricultural water use and covers just around 20% of the cropland, it can contribute about 40% of total food production since productivity of irrigated land is almost three times higher than that of rainfed land. The Food and Agriculture Organization (FAO) has predicted a net expansion of irrigated land of about 45 million is hectares in 93 developing countries (for a total of 242 million hectares in 2030). It projected that water withdrawals by the agriculture sector will increase by about 14% during 2000 – 2030 to meet the growing food demands (FAO, 2006). It is estimated that by 2050 in India, about 22 % of the geographic area and 17 % of the population will be under absolute water scarcity. The per capita availability of water which was about 1704 cubic meter in 2010 is projected to be 1235 cubic meter in 2050 (Anonymous, 2011). Infrastructure to increase the command area and minimize the gap of irrigation potential created and utilized is the dominant component of overall investment in agriculture (Anonymous, 2011). Besides this, judicious agricultural water management assists in ensuring good returns through efficient use of fertilizers and high yielding crop varieties. Above all, water will remain as a critical input for attaining sustainability in agricultural production. India’s current water supply is approximately 740 billion m3, which necessitates implementation of judicious water management technologies to enhance crop water productivity. In recent decades the world’s population has shown tremendous growth from an estimated 2.5 billion in the 1950’s to approximately 6.7 billion till date. It is expected that reduction in the average size of land holding, declining per capita water availability, deterioration of water quality etc. will seriously affect the sustainable use of water resources and will make it difficult to accomplish the target of producing 345 Mt of food grain in 2030 and 494 Mt in 2050 AD. To achieve the targeted food production, increase in the crop productivity from the present 2.3 to 4.0 t ha-1 under irrigated conditions and from 1.0 t ha-1 to 1.5 t ha-1 in rainfed area, productivity need to be increased at individual field level. This demands efficient use of water in agriculture, which can be achieved by reducing the losses of water and increasing the water uptake by the crops and it requires quantification of different water budgeting parameters like evapotranspiration and deep percolation.

1. Introduction
Ecosystem Services (ES) are actually the benefits human beings derive from their interaction with nature (Reyers et al., 2013; Ernstson, 2013). Around 400 BC, Plato observed how important service provided to Attica by the forests and subsequent loss of forests lead to drying spring and soil erosion (Mooney and Ehrlich, 1997). Thus, the work of Plato highlights that how the people were even then aware of the critical services provided by nature much before the industrial revolution (Rapidel et al., 2011). These ES are categorised into mainly provisioning, supporting, regulating and cultural services as per the Millennium Ecosystem Assessment (MEA, 2005). In fact, the increasing population warrants more and more set of ES (Biggs et al., 2015). A combination of factors responsible for delivery of ES like geology, climate, ecosystem, management scenarios, society, techniques, skills etc. that led to ES in an agricultural ecosystem (Kroll et al., 2012; Swinton et al., 2007). The ordinary nature largely comprised of agricultural lands (i.e., intensive crops, species poor, artificial pasture) that covers around 37.4% of total Earths’ land surface (FAOSTAT, 2016; Leff et al., 2004). Converting large tract of pristine lands into agricultural land and other competing uses considered to be one of the main reasons for biodiversity loss and land degradation (Tilman et al., 2001). Thus, meeting the challenge of working with limited arable land to feed our population in an eco-friendly manner requires our proper understanding of processes/mechanisms involved in nutrient availability/dynamics including fixation/ release pattern and refining the best management practices (Kumar et al., 2022a). The production of food has been targeted at the cost of other ES like supporting, cultural and non-marketable provisioning services (Muller et al., 2016; Foley et al., 2005; MEA, 2005). There is complex interaction between ecosystem and societal changes in an agricultural landscape. One is intermittently connected to other. Thus, it requires thorough understanding of the processes and biophysical factors that affect ES research, in particular and social-ecosystem, in general (Reed
et al., 2013; Carpenter et al., 2006; Plieninger et al., 2013). The ES that contribute to the agricultural production are mostly linked to aspect of soil fertility, biological control as well as pollination (Bommarco et al., 2013; Duru et al., 2015).

1. Introduction
Soils stand out as an intricate and diverse ecosystem on our planet. Their significance extends beyond supplying 98.8% of the world’s food, encompassing a wide array of essential services such as carbon storage, regulation of greenhouse gas emissions, flood prevention, and offering support to burgeoning urban populations. However, it is crucial to recognize that soil is a finite natural resource. The unprecedented growth of the human population, escalating from 250 million in the year 1000 to 6.1 billion in 2000, with projections reaching 9.8 billion by 2050, along with the intensified practices of agricultural production, is exerting immense pressure on the sustainability of soils.
For long-term sustenance the concept of ‘Soil quality’ was propagated around three decades back from now in the United States of America, and further expanded to the scientists of European and Asian countries. Soil health and soil quality are two interweaving term that signifies the potentiality or capacity of soil towards mankind. These two words complement each other in such a way that one term automatically comes with another to determine both aspects i.e. agricultural sustainability and environmental quality in terms of plant, animal and human health (Haberern, 1992; Doran, 2002). Idowu et al. (2007) remarked that the term “soil quality” is more favoured by scientists, whereas “soil health” is a term favoured by farmers as it connotes a holistic approach to soil management. The inception and application of soil quality term was officially approved by Soil Science Society of America (SSSA). They defined soil quality as “The capacity of a specific kind of soil to function within natural or managed ecosystem boundaries, to sustain biological productivity, maintain environmental quality, and promote plant and animal health” as illustrated by Karlen et al. (1997). It is a complex composition of certain properties associated with physical (texture, bulk density, water holding capacity, infiltration capacity etc.), chemical (pH, EC, organic carbon, extractable macronutrient and micronutrient etc.) and biological (microbial activity and related parameters) conditions of soil (Prabha et al., 2020). There are primarily two key methodologies for assessing soil quality: the descriptive approach, which focuses on characterizing various aspects or attributes of soil quality, and the Indicative approach, which aims to identify the ability or capacity of an attribute in a desired manner. The comprehensive process of developing a soil quality index is guided by the 3-Is framework: the Identification of indicators, Interpretation, and Integration of selected soil quality indicators. It is important to note that the specific set of indicators utilized can vary between different soils, diverse agroecosystems, and from one crop to another. Additionally, climatic conditions and other abiotic factors may also significantly influence the assessment of soil quality.
The USDA has categorized soil quality indicators into four primary groups: visual, physical, chemical, and biological indicators. However, in the current context, the dominant indicators are physical, chemical, and biological. For a systematic assessment of soil quality, it is crucial to collect individual parameters and integrate them meaningfully. Consequently, integrated soil quality indicators

1. Introduction
In the realm of modern agriculture, achieving sustainability is paramount. The judicious use of resources and the maintenance of soil and crop health are vital components of this endeavor. Remote sensing is a powerful technique used to gather information about the Earth’s atmosphere and surface without direct physical contact (Lillesand et al., 2015). It involves the acquisition, measurement, and analysis of data from sensors aboard platforms such as satellites, airplanes, drones, or ground-based instruments. These sensors record electromagnetic radiation, providing valuable insights into various aspects of the Earth’s environment. Remote sensing technology has emerged as an invaluable tool for achieving these objectives, offering a range of benefits for sustainable agriculture (Jensen, 2009). With the advent of modern technologies such as hyperspectral remote sensing (HRS) that collects information in narrow contiguous bands, many new avenues of applications have come up. This detailed spectral information enables the precise identification and classification of objects based on their unique spectral signatures. Further, the narrow spectral reflectance provides significant information regarding the biophysical, biochemical, and physiological parameters of crops. These characteristics of HRS provides opportunities for improved feature identification, anomaly detection, and quantification. Often HRS data also complement to the remote sensing data, such as LiDAR or SAR (Synthetic Aperture Radar), allowing for a more comprehensive understanding of the Earth’s surface and its features. All these features of HRS enables precision agriculture, allowing the management practices as per the specific needs of the fields. By assessing soil and crop conditions remotely, one can apply resources like water, fertilizers, and pesticides precisely where and when they are needed. This minimizes waste and reduces the environmental impact of agricultural operations. Hyperspectral remote sensing can also assess critical soil parameters such as moisture content, pH levels, and nutrient concentrations. Monitoring these factors over time helps farmers make informed decisions about soil management and the choice of suitable crops. It also aids in preventing soil degradation and erosion. Early detection of stressors like pests, diseases, and nutrient deficiencies is crucial for maintaining crop health and maximizing yields. Remote sensing provides a timely and accurate means of monitoring crop conditions across large areas, enabling rapid responses to threats and reducing yield losses. In regions with water scarcity, efficient water management is essential. Remote sensing can track soil moisture levels and assess irrigation needs. By optimizing irrigation practices, farmers can conserve water resources while maintaining crop productivity. Remote sensing, particularly hyperspectral remote sensing can detect changes in

1. Introduction
Crop health valuation plays a vital part in modern agriculture as it positively stimulates food security, economic constancy, and ecological sustainability. This process involves monitoring and evaluating the growth and development of crops, finding possible stressors or diseases, and executing suitable management practices. Judicious and precise evaluation of crop health permits early exposure of diseases, pests, and other irregularities. Detecting these problems at their early growth stages can aid prevent extensive occurrences and decrease the need for unnecessary pesticide use, indorsing justifiable crop management practices. Assessing crop health on a regular basis help in enhancing yields by detecting factors that could inhibit crop productivity. Agricultural systems can be made resource-efficient by incorporating tools, modern technologies and information management systems that come under the precision agriculture concept. Modern scientific improvements, comprising Global Positioning System (GPS), grain yield monitoring system, Variable Rate Technologies (VRT), sensor networks, and Femote Sensing (RS), have assisted farmers to detect in-field heterogeneity of crop and soil and adopt site-specific farm management practices. Varying the application of inputs with VRT can decrease the cost of inputs and labor, increase productivity, and decrease the potential environmental influences from the overuse of farm inputs. However, the success rate of VRT needs precise and consistent information on the location and extent of crop and soil health conditions, such as crop nutrient shortages, soil and crop water deficiency, and injury caused by insect pests, weeds, or pathogens. So far, the dominant remote sensing technologies that the farming sector has used are visible (VIS), Near-Infrared (NIR), and short-wave infrared (SWIR) sensors. The use of thermal sensors in agriculture is to some extent narrow despite its wide use in the areas of medical science (Ring and Ammer, 2012), intelligence/military (Hinz and Stilla, 2006), and the food processing sector (Vadivambal and Jayas, 2011). By providing temperature variations of soil and crop canopy, thermal remote sensing has the prospective to be used for various

Introduction
With advancements in molecular biology, DNA-based technologies have shown great potential in enhancing the efficiency of crop breeding programs, preserving germplasm resources, improving the quality and yield of agricultural products, and protecting the environment. As a result, their role in modern agriculture is becoming increasingly important.
DNA is the main genetic materials of all cellular organisms; preserving DNA itself is one way of preserving germplasm resources (Geptset al.,2006). The advancement of biotechnology and molecular biology has made it possible to regulate or even control plant traits using DNA sequence information, including its structure, function, and mechanisms.DNA technologies based on DNA molecular markers, transgenic technology and gene expression have been widely used in agricultural production which have showed great potential in improving agricultural yields and quality, reducing the loss that various biotic and abiotic stress caused, promoting the utilization of germplasm resource, improving breeding efficiency and strengthening the regulation of plant growth. (Garciaet al.,2005)
These modern DNA technologies, with their high feasibility and necessity, play a crucial role in ensuring the sustainable development of agriculture. Although agriculture encompasses both plant and animal production, DNA technologies in these fields share similar technical purposes and types. Therefore, this study focuses on reviewing the agricultural applications of DNA technologies by exploring their utilization in plant production. Numerous reports have documented the application of various DNA technologies in agricultural production. However, as these technologies continue to evolve and advance, it becomes challenging to cover every DNA technique in detail..DNA technology holds immense promise for the future of crop identification, enabling precise and efficient breeding, accelerating the development of improved varieties, and contributing to sustainable food systems. Here’s a breakdown of the key future prospects:

Introduction
Identification of crop varieties is one of the major tasks required in modern agriculture, plant breeding, and seed certification systems. The correctness of identification ensures that the varietal purity is sustained, advocates the rights of plant breeders, and guarantees quality seeds that will perform expectedly under specified agro-climatic conditions to farmers (Yu and Chung, 2021). The growing number of crop varieties due to the intensive breeding programmes all over the world makes it equally important to be able to differentiate between cultivars. Variety identification serves several purposes, such as:
• Seed Quality Assurance: Used to prevent the sale of spurious or mislabeled seeds.
• Intellectual Property Rights (IPR): Legal protection of new plant varieties by acts like the Plant Variety Protection (PVP).
• Biodiversity Conservation: Tracking and documenting existing genetic resources.
• Regulatory Compliance: Aids in Distinctness, Uniformity and Stability (DUS) testing protocols.
• Traceability in Food Supply Chains: Ensures safety for consumers and compliance with labeling standards.
Thus, crop variety identification forms the basis of integrity in agricultural systems and is the coordination mechanism for protecting innovations in plant breeding with regulatory frameworks both at national and international levels (Jones et al., 2013; Kage et al., 2016; Cockram et al., 2012).

Introduction
The accurate identification of crop varieties is crucial for agriculture, plant breeding, and intellectual property protection. DNA-based techniques have emerged as powerful tools for distinguishing between crop varieties due to their precision and reliability. However, several challenges persist in the adoption and implementation of these techniques. Conventionally, morphological descriptors are routinely used for establishing the identity of varieties. But these morphological descriptors suffer from many drawbacks such as influence of environment on trait expression, epistatic interactions, pleiotrophic effects etc. Furthermore, the paucity of sufficient number of these descriptors for unequivocal identification of increasing number of reference collection of varieties enforces to look for alternatives. Electrophoresis of seed proteins and isozyme analysis have overcome these limitations to some extent but now many powerful DNA based techniques are available. Since all genetic differences between individuals are laid down in the primary sequence of their genomic DNA, the straightforward method of identification of crop varieties and parental lines would be to determine this sequence for the genome under comparison. Sequencing requires huge monitory investment and it is practically impossible to sequence the whole genomes of all varieties. But with the automation in sequencing now possible, short stretches of amplified DNA can be sequenced. However, a more practical strategy would be to limit the comparisons to specific regions of the genome, which frequently differ between individuals.

Introduction to Molecular Markers in Vegetable Breeding
As global demand for food continues to rise, the agricultural sector faces mounting challenges from climate change and resource limitations. These pressures necessitate an urgent transformation in farming practices, making molecular markers vital for enhancing crop improvement strategies (Tester and Langridge, 2010). Traditional breeding techniques, which often rely on phenotypic selection and protracted breeding cycles, find it increasingly difficult to produce resilient, high-yield cultivars (Bernardo, 2016). Molecular markers, including Single Nucleotide Polymorphisms (SNPs), Simple Sequence Repeats (SSRs), and Amplified Fragment Length Polymorphisms (AFLPs), provide direct access to plant genomes, allowing for the precise identification and manipulation of desirable traits regardless of environmental variability (Varshney et al., 2005). This approach is essential for expediting breeding programs and enhancing genetic gains, ultimately leading to crops better suited for diverse and evolving climates (Jannink et al., 2010).Additionally, the integration of genome-wide association studies (GWAS) and genomic selection (GS) harnesses high-density marker data to deepen our understanding of complex trait genetics, fostering sustainable agricultural practices and securing future food supplies (Hickey et al., 2019).

Introduction
In the realm of modern agriculture and plant breeding, the integration of advanced biotechnological tools has revolutionized the traditional methods of crop improvement. Traditional approaches to genetically improving horticultural crops that are primarily propagated asexually encounter multiple challenges. These include an extended juvenile phase, the absence of seeds, a high level of intraand interspecific incompatibility, frequent heterozygosity, seed sterility, and, most importantly, the restriction of certain valuable traits to wild species (Mehlenbacher, 1995). Conventional or classical breeding techniques rely on natural processes and traditional tools to develop new plant varieties or cultivars. However, these methods are often time-consuming, labor-intensive, and less efficient. The lengthy process, coupled with the challenges of maintaining desirable traits, makes it difficult to achieve rapid genetic improvements. As a result, there is a growing need for more advanced and precise breeding methods to enhance crop development (Jain and Kharkwal, 2004). A molecular marker is a specific DNA segment that signifies genetic variations at the genome level, aiding in genetic analysis and breeding (Dhutmal et al., 2018). These are composed of nucleic acids as well as proteins and used on basis of naturally occurring polymorphism (Thottappilly et al., 2000; Ahmad et al., 2020). These markers are closely related with the target gene and they act as sign or flags (Nadeem et al., 2018), aiding in the identification of specific regions associated with advantageous characteristics such as disease resistance, yield potential, nutritional content, and environmental adaptability (Meena et al., 2023).The advancement of high-throughput molecular markers and genetic maps has enabled the precise identification of genes responsible for agronomically important traits, potentially enhancing breeding through markerassisted selection (MAS). Compared to traditional breeding methods, MAS overcomes the challenges of phenotypic selection and significantly improves selection efficiency. This approach accelerates crop improvement by ensuring the effective transfer of desirable traits with greater accuracy and reliability (Wani et al., 2022). The incorporation of next-generation sequencing (NGS) technologies and high-throughput genotyping platforms has also facilitated genome-wide association studies (GWAS) and the discovery of single nucleotide polymorphisms (SNPs) linked to important traits, further enhancing the resolution of genetic analysis. In recent years, molecular markers have been extensively used to characterize and enhance genotypic and phenotypic traits in temperate fruit and nut crops. Studies have focused on assessing genetic diversity in almonds, identifying genes responsible for red skin color in apples, developing sharka disease-resistant apricots, and sequencing the almond genome. These markers have been successfully used for genetic studies in many horticultural crops including vegetables (Gulsen et al., 2007; Kumar et al., 2008 a and b; Schafleitner et al., 2013; Yadav et al., 2015; Sharma et al., 2016; Kumar et al., 2017 and Kumar et al., 2019) and flowering crops (Kumar et al., 2019; Kumar et al., 2016 and Kumar et al., 2018a and b). These advancements highlight the crucial role of molecular markers in improving breeding strategies, accelerating genetic improvements

Introduction
DNA fingerprinting envisages a very significant molecular methodology employed for identification and characterization of wheat varieties and germplasm. It is of utmost importance in varietal authentication, rights protection of breeders, and analysis of genetic diversity. Currently employed molecular markers like SSRs, SNPs, and DArT are applied extensively for wheat fingerprinting. Such markers precisely differentiate closely related varieties and aid in breeding programs (Babu et al., 2020; Kiss et al., 2021).
The current standing of wheat DNA fingerprinting shows a trend toward high-throughput genotyping and genomic selection strategies to accelerate crop improvement. Future prospects would entail the integration of advanced sequencing technologies such as Next-Generation Sequencing (NGS) and machine learning for genetic analysis. These technologies will ease wheat breeding and dramatically contribute to food security and climate-resilient agriculture. Expanding the global DNA fingerprinting database would further enhance global wheat germplasm conservation and utilization (Sandhu et al., 2021; Ahmadi et al., 2020). Wheat (Triticum spp.) is one of the major staple crops of the world, supporting the sustenance of over one-third of the total world population. It is the main source of carbohydrates, proteins, fibers, and other important micronutrients, making it very important for human nutrition. Wheat is cultivated mainly in all types of agro-climatic zones, with major producers being China, India, Russia, United States, and the European Union.
Wheat is highly valuable not only in terms of food security, but also economically with millions of farmers involved in its production and trading worldwide. Wheat is an important raw material for many food products, such as bread, pasta, and baked goods. Wheat production faces challenges from climate change, pests, and emerging pathogens. Therefore, continuous research on wheat, genetic improvement for desirable traits, and sustainable agricultural practices are needed to enhance yield, resilience, and in turn global food security. Genetic diversity in wheat is pertinent for fulfilling food security at the global level and for the success of breeding programs, apart from giving diverse traits for resistance against pests, diseases, and environmental stresses like droughts

Introduction
Plant genetic resources (PGRS) encompasses everything that is on the earth related to the plants which are beneficial to mankind (Appazov,2008). So, it is the basic raw material for crop improvement (Somsenet al., 2002). In the present regime of transgenic crops, you can have ‘designer crops’- crops of your willingness (Wisner et al., 2001). From the heritage of mankind to the sovereign rights of the states this is the path that PGRs have traversed, earlier before CBD (Conventional Biological Diversity)(Chakraborty et al., 2018) PGRs were considered as a heritage of mankind freely available to everybody but after signing that they became the sovereign rights of the states with sovereign rights of the nation (Brahmiet al., 2015). In such a scenario where states have their rights on those plants’ genetic wealth, protection becomes very important (Clevelandet al., 1997). DNA fingerprinting is one of the technologies which can facilitate that protection (Wanget al., 2019).Rapeseed-mustard (Brassica spp.) is a vital oilseed crop cultivated extensively in India and other parts of the world (Kumaret al., 2008). It plays a crucial role in ensuring edible oil security and contributes significantly to the agricultural economy(Pahariyaet al., 2007). The genetic diversity within rapeseed-mustard species is a key factor in improving yield potential, disease resistance, and oil quality through breeding programs (Chandet al., 2021). However, accurately identifying and characterizing varieties is a challenge due to phenotypic similarities, environmental influences, and potential genetic erosion(Chhikaraet al., 2024). To address these challenges, DNA fingerprinting has emerged as a powerful molecular tool for precise varietal identification, genetic purity assessment, and germplasm conservation (Hosseinpouret al., 2024).DNA fingerprinting involves the use of molecular markers to generate unique genetic profiles of plant varieties, enabling breeders and researchers to differentiate between closely related cultivars (Singhet al., 2022; Singhet al., 2014). This technology is instrumental in breeding programs, ensuring the registration and protection of plant varieties under legal frameworks such as the Protection of Plant Varieties and Farmers’ Rights Act (PPV&FRA) in India (Williamet al., 2024). Additionally, DNA-based markers provide valuable insights into genetic relationships, aiding in the efficient selection of parent lines for hybridization and improving crop resilience to biotic and abiotic stresses (Nadeemet al., 2018; Haqueetal., 2021).Over the past two decades, several DNA fingerprinting techniques, including Random Amplified Polymorphic DNA (RAPD), Amplified Fragment Length Polymorphism (AFLP), Simple Sequence Repeats (SSR), and Single Nucleotide Polymorphisms (SNPs), have been widely applied to characterize rapeseed-mustard varieties (Nybomet al., 2014; Panjabietal.,2019) Advances in high-throughput genotyping and nextgeneration sequencing (NGS) technologies have further enhanced the resolution and accuracy of genetic profiling, paving the way for marker-assisted selection (MAS) and genomic-assisted breeding strategies (Sinhaet al., 2023).

Introduction
Legumes that produce dry seed suitable for human consumption are known as pulses, and they are agronomically valuable plants in the field and in the food chain. In the past ten years, grain legumes—particularly pulses—have become more popular as a means of addressing global agricultural concerns. (Bohra et al. 2015, Varshney et al. 2015).The best plant-based sources of dietary protein, as well as other minerals including iron, zinc, magnesium, and dietary fiber, are pulses. (Bohra et al.2014, Kouris-Blazos and Belski 2016, Maphosa and Jideani 2017). According to Simpson and Campbell (2015), a plant-based agricultural diet high in fruit or legume fiber helps to improve microbial diversity and has a beneficial effect on short-chain fatty acid levels, both of which are critical for preserving gut health. Because of their unique ability to fix nitrogen symbiotically with Rhizobium spp. in their root nodules, legumes not only improve nutritional health but also increase soil fertility (Graham and Vance 2003, Stagnariet al. 2017). The Leguminosae, often known as the Fabaceae, are the third largest family of flowering plants, behind the orchid (Orchidaceae) and sunflower (Asteraceae) families (Walters 1960), with 750 genera and 20,000 species (Polhill 1981).With a productivity of 1016 kg ha-1, 93.23 million tons (m t) of pulses are harvested worldwide from 91.77 million (m ha) of land (http://faostat.fao.org/). With a cumulative average productivity of 1030 kg ha-1, the major pulse crops—dry bean (primarily common bean), chickpea, dry pea (pea), cowpea, pigeonpea, lentil, and faba bean—share 92.58% (84.05 million ha) of the world’s total pulse production, or 92.82 percent (86.53 m t) (http://faostat.fao.org/). According to their ability to adapt to tropical and temperate agroclimatic conditions, pulse crops can be broadly divided into two groups: 1) warm season crops, such as common bean, pigeonpea, and cowpea, and 2) cool season crops, such as pea, chickpea, lentil, and faba bean (Cannon et al. 2009, Young et al. 2003, Zhu et al. 2005). Chickpea, pea, and lentil are regarded as one of the founding grain crops and helped start modern agriculture because of its early domestication (around 11,000 years ago) (Zohary and Hopf 2000).Over the past 60 years, the global area under pulses has increased from 64 to 91.77 million hectares (http://faostat.fao.org/), and numerous high-yielding varieties have been developed and released as a result of extensive research on conventional breeding for pulse improvement due to their high agricultural value (Singh 2005, Saxena 2008, Pérez de la Vega et al. 2011, Torres et al. 2011, Gaur et al. 2012).Pulses are less productive than other crops, particularly cereals, due to factors such growing in risky settings, unpredictable rainfall, extended dry spells, and susceptibility to a range of pests and diseases (Borlaug 1973, Varshney et al. 2013a).

Introduction
Maize (Zea mays L.) is one of the most widely cultivated cereal crops globally and holds significant economic and food security value across diverse agroclimatic regions. In India, its multifaceted utility as food, feed, and industrial raw material underscores its relevance in national agriculture. With the expansion of hybrid cultivation, climate-resilient breeding, and commercial seed markets, the need for precise and dependable varietal identification has become increasingly critical. Traditional methods of cultivar identification, which relied primarily on morphological characteristics evaluated under the Distinctness, Uniformity, and Stability (DUS) framework, have shown considerable limitations due to their environmental dependence, subjectivity, and limited resolution among closely related genotypes (Verma et al., 2023; Jamil et al., 2021). Morphological traits, although historically pivotal in classification, are often confounded by genotype-environment interactions, multigenic inheritance, and developmental plasticity. Consequently, accurate varietal authentication using such traits becomes impractical, particularly in crops like maize where morphological similarity among hybrids is frequent (Poets et al., 2020; Verma et al., 2023). This challenge has driven the development and adoption of molecular techniques such as DNA fingerprinting, which offers a robust, reproducible, and environmentindependent alternative for cultivar and germplasm identification (Jamil et al., 2021).
DNA fingerprinting involves the use of specific DNA markers to distinguish and characterize plant genotypes based on their genetic makeup. The concept, introduced by Paul Hebert in 2003, was originally proposed as a supplement to morphological taxonomy and has since evolved into a cornerstone of modern plant breeding and germplasm management. This technique has been instrumental in multiple domains of crop improvement, including genetic diversity analysis, heterotic grouping, marker-assisted selection, and varietal purity testing. Moreover, in the context of intellectual property rights and seed certification, DNA-based identification plays a vital role in enforcing breeder rights and ensuring seed quality in both public and private breeding sectors (Poets et al., 2020). Its utility is further underscored by its ability to detect essentially derived varieties (EDVs), resolve disputes in seed authenticity, and support biodiversity conservation efforts through accurate germplasm cataloging (Jamil et al., 2021; Verma et al., 2023).
Given these wide-ranging applications and advantages, the present chapter aims to comprehensively assess the evolution and current status of DNA fingerprinting technologies, with specific emphasis on their application in maize variety and germplasm identification.

Introduction
Rice is a key crop that directly feeds about half of the global population, with most of it consumed in developing countries (Wang et al., 2018). It is cultivated in many different types of environments and is always being enhanced by the creation of new varieties and the assessment and conservation of germplasm collections. To improve rice diversity and increase its genetic range, numerous researchers and organizations have been attempting to bring long-forgotten types back into production.This variation among varieties is essential for breeders as it provides genetic traits that can be incorporated into commercial varieties, such as resistance to diseases and pests, tolerance to abiotic stresses and improvements in grain nutritional quality, among others. In some cases, specific new traits areintroduced through crosses made with distantly-related genotypes. As a result of these efforts, despite being one of the first domesticated crops, cultivated rice continues to tap into a vast gene pool (Vieira et al., 2022).
There are over 500,000 rice accessions stored in gene banks around the world (Genesys 2021). To further explore the molecular diversity of this germplasm International Rice Research Institute (IRRI), Chinese Academy of Agricultural Sciences and BGI-Shenzheninitiated the 3K Rice Genome Project. This project focused on the most important rice species, Oryza sativa, and involved resequencing 3,000 rice genomes selected from the available genetic diversity, creating an invaluable resource for large-scale genetic variation discovery in this species (Vieira et al., 2022).
Varietal identification and classification in breeding programs are typically assessed using the distinctness, uniformity, and stability (DUS) approach, which relies on morphological descriptors (Tiwari et al., 2013). However, using morphological data/markers to determine genetic variation has limitations due to the labor-intensive, time-consuming nature of the process, as well as theagronomical traits and season-specific that are heavily influenced by ecologicalaspects. The development of genetically and morphologically similar varieties has made the task of clearly distinguishing between them increasingly complex. These environmental challenges necessitate the use of new techniques that are fast, reliable and environmentally independent.Biochemical and chemical tests fail to accurately estimate genetic distances among closely related genotypes. PCRbased molecular markers, however, are fast, reliable, and effective for determining the genetic purity of crop varieties (Rana, 2006). Hypervariable microsatellites (Teymnykhet al., 2011) offer a valuable source of polymorphic DNA markers, connecting genetic maps to genomic sequences and, ultimately, to phenotypic variation. SSR-based “DNA barcodes” are unique patterns of SSR polymorphism that assist in the precise identification of varieties, providing stability and resistance to environmental influences, epistatic interactions, and pleiotropic effects.

Introduction
As the seed market has become more globalized, seed trade has seen a rise in competitiveness as well as stricter quality requirements. Throughout the entire seed manufacturing process, thorough management is necessary to ensure high seed quality, from initial planning to final distribution. Among the various seed quality parameters, genetic purity is particularly critical in determining the authenticity and planting value of a seed lot. Genetic purity refers to the degree of purity within a seed lot, the genetic integrity of an F1 hybrid in a hybrid seed lot, or the difference between a submitted seed sample and the stated variety. During manufacturing, achieving complete (100%) varietal purity is challenging, even when recommended protocols are strictly followed. Outcrossing, partial off-type removal, and physical mixing during harvest, storage, or seed handling are a few examples of these factors can result in unintended varietal impurities (Bradford, 2006). Seed firms, producers, and suppliers conduct laboratory-based evaluations for internal quality control or post-control grow-out testing on seed lots to guarantee the necessary levels of purity. Assessments for genetically modified trait purity and the accidental inclusion of genetically modified (GM) seeds in non- GM seed lots are now necessary due to the introduction of transgenic varieties into the worldwide market, which has further broadened the scope of seed purity testing. Thus, seed purity testing is a cornerstone of agricultural production, ensuring that the seeds sown in the fields conform to their designated genetic identity. This process is essential for maintaining varietal integrity, meeting regulatory requirements, and ensuring high agricultural productivity (Sandra et al., 2023). The quality and genetic purity of seeds all directly impact crop productivity, pest and disease resistance, and overall farm profitability. Traditional seed purity assessment methods rely heavily on morphological, physiological, and biochemical traits. These methods include visual examination of seed characteristics, seedling grow-out tests, electrophoresis-based protein profiling, and chemical marker-based identification. However, these approaches often suffer from several limitations, such as the influence of environmental conditions on phenotypic traits, subjective assessments, and the need for significant time and labour. The accuracy and reproducibility of such conventional techniques can be further affected by variations in experimental conditions and the expertise of personnel conducting the tests.The advent of molecular biology has brought a paradigm shift in seed purity testing. DNA-based technologies provide precise, reliable, and high-throughput solutions that overcome the limitations of traditional methods. These molecular approaches allow for direct examination of genetic material, offering greater accuracy in varietal identification, hybrid seed purity assessment, and detection of genetic contamination. DNA markers can distinguish even closely related varieties, enabling seed companies, regulatory bodies, and farmers to ensure the authenticity of seed lots with high confidence. This chapter provides an overview of various DNA-based techniques employed in seed purity testing, including polymerase chain reaction (PCR)-based markers, single nucleotide polymorphism (SNP) markers, and high-throughput sequencing (HTS) technologies.

Introduction
Information on genetics is also crucial in agriculture research-related advancements in crop and livestock improvement, sustainability, and food security. Genetic information is analyzed by scientists so that important traits such as disease resistance, drought tolerance, and improved yield can be bred into plants and animals with many attributes. Selective breeding programs and subsequent genetic manipulation to improve agricultural production while reducing dependence on chemical inputs like pesticides and fertilizers are then developed (Bohra et al., 2020; Li & Yan, 2020).
Precision agriculture also boasts genetic data for informed decisions on the genetic composition of crops and livestock. Genetic data are thus used to optimize resource use as global demand for food resources continues to grow with increasing population needs against the backdrop of climate change resilience of crops and nutritional quality of agricultural products. Genetic studies also serve purposes of biodiversity conservation through ex-situ preservation of native and rare species presumed to have important genetic assets worthy for future breeding programs (Chaudhary et al., 2023; Varshney et al., 2021; Thudi et al., 2021).
Overview of Challenges in Analyzing Genetic Data
It may prove difficult for agricultural research to analyze genetic data because of its wide and varied capabilities:
Data Complexity and Volume
Advances in genomic sequencing technologies produce huge amounts of data that necessitate specialized computational machinery for processing. The most complex aspect of large-scale data is reliable storage, retrieval, and analysis.
Data Integration and Standardization
Genetic data comes from different sources and different sequencing techniques and institutes of research. Variations in format, methods, and terminologies complicate comparison of findings across studies. There are standardized protocols for collection and analysis of genetic data that ensure its consistency and reliability (Lassoued et al., 2021; Mulder et al., 2017).
Computational and Bioinformatics Challenges
Genetic data processing needs very high-performance computational resources and sophisticated bioinformatics tools. Most agricultural research institutions, particularly in developing countries, are poorly equipped with such technologies,

Introduction
Bioinformatics software plays an important role in the identification of plant varieties through employing genetic and genomic data to distinguish various kinds from each other. The software uses such approaches to DNA sequencing, molecular markers, and computational algorithms to increase precision in crop identification. Techniques such as SNP genotyping, DNA barcoding, and machine learning models are helpful in easily classifying and validating crop varieties. Farmers and breeders, for instance, will benefit from bioinformatics because it ensures genetic purity, improves genetic selection in crops, and provides more yields. It also incorporates bioinformatics tools that are revolutionizing plant breeding and conservation and allow the accurate identification of diverse highquality crop varieties for sustainable agriculture and food security (Derreper et al., 2011; Heo et al., 2024).
Crop variety identification has prime importance as per agriculture, food security, and conservation of biodiversity it maintains genetic purity for crops resulting in more yields with disease resistance and adaptability to climate. Correct identification will help farmer’s select superior seeds and maximize resources for increased productivity; further crop variety identification would participate in breeding programs ensuring superior crops varieties with desirable characteristics like drought tolerance, pest resistance, and better nutritional content. Accurate crop variety identification, in terms of food security, creates a steady supply of food through supporting continuous cultures of high-yielding and hardy varieties for the farm. Welfare in crop variety identification would help prevent fraud and misrepresentation in the food because they would be able to ensure that what customers receive is the type of crop they were looking for both in planned nutrition and costs. Facilitate seed certification schemes for protection of farmers from counterfeit seeds that would result in low yields.
Maintenance of genetic resources through proper identification of crop varieties ensures conservation of biodiversity. Crop variety identification and registration is a huge stroke towards conservation of wild and traditional crop species, which are very important for breeding and climate resilience. Such an event becomes important in climate change where genetic diversity ensures the abilities of crops to adapt to changing environmental conditions (Kim et al., 2021; Nguyen et al., 2025; Pavan et al., 2020).

Introduction
The identification of agricultural crops with accuracy determines how precision farming works and affects biodiversity protection and food safety standards in addition to pest control practices. In modern agricultural practice, crop species identification relies on the combination of three traditional methods: morphological characteristics and biochemical markers, and remote-sensing technologies. These methods face a large variety of constraints such as environmental differences, high costs, and time-consuming operations. Genome-editing tools, especially CRISPR, a modern molecular identification tool for crops that increases efficiency and accuracy, have largely solved the problems traditional methods faced. Identifying agricultural crops correctly is an essential operation for resource management to be used efficiently in the hope that businesses meet regulatory standards and do their share in stopping trade-related fraudulent activities with incorrect labeling. The early and exact identification process helps pest and disease control because different crop species need different levels of resistance management (Rahayu et al., 2025). A reliable identification methodology remains important because sustainable agricultural practices and climate-ready crop varieties reach higher popularity in current times. The identification method based on morphological classification takes too much time and gets misdirected because of environmental conditions (Mascellino et al., 2024). High-resolution satellite data together with advanced computational tools needed for remote sensing-based identification cannot easily be accessed by small-scale farmers (Sharma et al., 2024).
The high specific identification of crop species becomes achievable through CRISPR-based technology which utilizes genetic markers as its primary feature. CRISPR enables fast and exact and affordable crop classification through its ability to detect and analyze specific genetic patterns. The accuracy of crop identification improves while real-time evaluation of plant diversity alongside disease. Alert traits becomes achievable through this method (Kanapiya et al., 2024). CRISPR-based biosensors have experienced recent advancements that enabled portable field-deployable identification tools which eliminate the need for complex laboratory setups (Lagener et al., 2025). Crop monitoring and breeding programs will undergo revolution through CRISPR technology integration in agricultural systems which will support the creation of climate-adaptive highyield crop varieties.
The evolution of CRISPR application research demonstrates its expanding function in farming efficiency together with food security establishment. The chapter explores CRISPR-based methods’ identification potential for crops using an evaluation of their superiority over conventional identification techniques and their expected contributions to future agricultural developments

Introduction
DNA fingerprinting is a powerful molecular technique used to identify individuals based on their unique genetic makeup. First developed by Sir Alec Jeffreys in 1984, this method has revolutionized fields such as forensic science, paternity testing, genetic research, and biodiversity conservation. We present here the account of variations in DNA sequences, particularly of hypervariable regions like minisatellites and microsatellites in reference to the DNA fingerprinting. Advances in PCR, hybridization-based markers, and next-generation sequencing (NGS) have further refined its accuracy and efficiency enabling precise identification of genetic relationships. Additionally, genetic distance analysis and clustering methods help in understanding evolutionary relationships, population structure, and genetic diversity among species. These methods serve as tools to quantify the magnitude and order of DNA sequence polymorphisms which exist at population level.
DNA Fingerprinting
History of DNA Fingerprinting: It was 10th date of September 1984, when geneticist Sir Alec John Jeffreys (the father of DNA fingerprinting) wrote three words - “33 autorad off” - in his red desk diary. The implicit phrase suggested the completion of an experiment by him pertaining to the study about inherited illnesses descended through families. He was one of the first to discover inherited variation in human DNA that can be utilized to resolve issues of disputed parentage and forensic investigations.

Introduction
Pathogenic organisms that cause diseases in plants are generally referred to as plant pathogens. Fungi, bacteria, viruses, viroids, algae and protozoa are the important phytopathogenic organisms responsible for plant diseases and they cause immense economic losses to growers. Recent estimates indicate that crop losses due to diseases range from 17-30%, primarily caused by fungal, oomycete, and nematode pathogens (Agrios, 2005). Beyond biotic factors, crop losses can also be attributed to abiotic causes, such as poor agricultural practices, nutrient deficiencies, changes in temperature and rainfall patterns, inadequate/overirrigation and weather extremes, all of which exert selection pressure on plant pathogens, potentially leading to new host infections. Hence, for effective disease management strategy, accurate identification and diagnosis of plant pathogens are of paramount importance before the pathogen induces any epidemic leading to immense loss of crop plants which is certain. Often, identification can be accomplished either visually through microscopic observation of disease-causing micro-organisms or molecular diagnosis based on nucleic acid detection methods. Microscopic observations alone are inadequate for species-level identification of plant pathogens and can often lead to taxonomic errors due to the potential for misidentification of organisms. Nucleic acid-based diagnostic tools allow for the sensitive detection, precise quantification, and specific differentiation of pathogens.Although numerous nucleic acid-based methods are available for plant pathogen detection, PCR-based diagnostics, especially those employing DNA barcodes, are still considered highly reliable and accurate. Scientists, especially plant pathologists, can now easily identify and diagnose most plant diseases as many standardized phytopathogen barcodes facilitate rapid, early, and accurate identification of plant diseases.

Introduction
DNA barcoding is a molecular taxonomic method used to identify species by analyzing variations in conserved DNA sequences that differ among species. The concept of “barcoding” refers to pooling multiple samples and sequencing and analysing them collectively. This technique has been widely applied to identify organisms ranging from simple to complex forms. Unlike other genetic markers, such as short tandem repeats (STR), which are typically used to identify individual organisms, DNA barcoding enables broader species identification. However, identifying species-specific DNA sequences across different taxa can be complex and time-consuming. For accurate species differentiation, the variation in DNA sequences must be sufficiently high to clearly distinguish between closely related species (Antil et al., 2023).
DNA barcode sequences are stored in databases like the Barcode of Life Data Systems (BOLD), which is an online repository containing reference DNA sequences for comparison with unknown samples. These databases store speciesspecific genetic sequences, specimen images, and related data, and also track updates to barcode libraries (Jin et al., 2020). DNA barcoding has progressed alongside genomics research, with large online libraries serving as reference databases for identifying samples from various environments like gardens, forests, and even commercial products (Seethapathy et al., 2019).
As research advances, the number of conserved genomic regions used for DNA barcoding continues to grow. For phylogenetic analysis, the rbcL gene locus is commonly used, along with other regions such as the trnL-F intergenic spacer, matK, ndhF, and atpB (Shaw et al., 2005). The CoxI gene has been wellestablished for barcoding animals and plant species. The trnH, psbA region of the plastid has been proposed as a universal barcode for terrestrial plants (Ward et al., 2005). Several other conserved genomic regions have been established by a “plant working group” for molecular taxonomy (Singh et al., 2012). Despite these advancements, further critical evaluations of the authenticity of these DNA barcoding markers in assessing biological diversity are still needed. Over the last few decades, DNA barcoding has become a widely used tool for confirming species identification, as evidenced by the growing body of research publications on the topic (Rob et al., 2019).Publications on DNA barcoding have seen a notable increase, as shown by a search using specific keywords in the CabDirect database (http://www.cabdirect.org/). This trend highlights the growing importance of DNA barcoding in taxonomic research

Introduction
The distinctions that differentiate one genotype from another are encoded in their hereditary material i.e., DNA (deoxyribonucleic acid). DNA is packaged into chromosomal pairs, with one strand of genetic material derived from each parent. The genes that govern a plant’s traits are situated on distinct portions of each chromosome (King and Stansfield, 1990). Depending on the context, a genetic marker may be defined as: (a) a chromosomal landmark that facilitates the tracing of a specific DNA region; (b) a distinct DNA segment with a known genomic position; or (c) a gene whose phenotypic expression is easily observable, and used to identify an individual and used as a probe to spot a nucleus, chromosomes, or locus. Molecular markers are distinguishable DNA sequences located at specific loci within the genome, inherited according to the established principles of inheritance from one generation to the subsequent generation. The presence of several molecular approaches, along with their distinct concepts and procedures, necessitates meticulous evaluation when selecting one or more marker types or loci (King and Stansfield, 1990). Various molecular markers have been described, based on mode of transmission, mode of gene action and method of analysis. Single nucleotide polymorphisms (SNPs) are variations in DNA sequences characterized by a single nucleotide (A, T, C, or G) that differs among individuals, serving as biological markers for the identification of genes linked to phenotypes. The genome sequences in public domain from various organisms have facilitated the examination of sequence variations among individuals, among cultivars, and among subspecies. These investigations demonstrated that single nucleotide polymorphisms (SNPs) are prevalent and dispersed across the genome across numerous species, including plants (Drenkard et al., 2000; Nasu et al., 2002).The prevalence of SNPs polymorphisms in plant genomes renders the SNP marker system a valuable marker technology for mapping, marker-assisted breeding, and map-based cloning (Rafalski, 2002a; Batley et al., 2003).A single nucleotide polymorphic (SNP) marker is a single base alteration in a DNA sequence, with a typical alternative of two possible nucleotides at a specific position. In recent years, numerous SNP genotyping methodologies have been established, utilizing diverse techniques for allelic discrimination and detection platforms (Rafalski, 2002b; Vignal et al., 2002; Sobrino et al., 2005; Tost and Gut, 2005).At now, SNPs are preferred for DNA fingerprinting, investigations of genetic diversity and association studies, phylogenetic linkages, studies of evolutionary history of species and population, genome-wide association mapping, and genomic selection. Single nucleotide polymorphisms (SNPs) are the most prevalent and reliable markers, suitable for automated high-throughput genotyping, highly reproducible, and capable of identifying variants, therefore superseding older markers due to their high throughput, efficiency, and cost-effectiveness (Alkan et al, 2011; Amdadul et al, 2016; Close et al., 2009; Muñoz-Amatriaín et al, 2014; Li et al., 2020; Qiuetal., 2012). The swift advancement and use of NGS technology have led to the creation and application of diverse reduced-representation genome sequencing methods for SNP genotyping. The methodologies encompass GBS (Elshire et al., 2011),

Introduction
Crop improvement is critical for tackling global food security challenges caused by population growth, resource scarcity, and climate change (Singha and Singha, 2024). Traditional breeding techniques, such as Sanger sequencing, have been around for a long, but their accuracy, speed, and usefulness are typically limited. Sanger sequencing is useful for small-scale inquiries, but its laborintensive procedures and limited throughput make it unsuitable for large-scale genome analysis. Molecular marker-based breeding strategies have proven to be effective tools for addressing these difficulties by increasing plant growth and overall quality (Bohar et al., 2020). In this respect, molecular markers like as single nucleotide polymorphisms (SNPs), insertions/deletions (indels), simple sequence repeats (SSRs), and structural variations (SVs) are expected to be useful for future breeding operations (Panahi et al., 2024). The discovery and characterization of these molecular markers is strongly reliant on advances in sequencing technology, namely next generation sequencing (NGS). Furthermore, NGS technologies have shown to be crucial for functional genomics, allowing for large-scale transcriptome and whole genome sequencing across plant taxa (Singh et al., 2024). These vast databases give a comprehensive picture of gene expression and genetic variation, making it easier to identify functional markers linked with certain features (Figure 1). The integration of NGS and bioinformatics technologies has simplified the study of these datasets, enabling more efficient discovery of markers and improving the use of marker-assisted selection (MAS) in breeding programs (Panahi et al., 2024).
The development of high-throughput genotyping technology has enabled substantial SNP-based research, which has improved our understanding of genetic diversity, population structure, and disease association (Yirgu et al., 2023). With the progress of genotyping by sequencing (GBS), SNPs have emerged as the most extensively utilized DNA markers in the twenty-first century (Thomson, 2014). SNP markers have significantly advanced plant breeding across a variety of crops, including tomato (Peterson et al., 2012), maize (Junta et al., 2020), rice (Razak et al., 2020), wheat (Wei et al., 2011), soybean (Kim et al., 2010), oil palm (Maryanto et al., 2020), cassava (Karim et al., 2020), and taro (Helmkampf et al., 2018). Overall, SNP markers have emerged as critical tools for genetic improvement and precision breeding across a wide range of crop species. SNPs initially trailed behind SSRs in DNA fingerprinting because they have fewer polymorphisms

Introduction
In recent years crop yields have increased significantly due the development of new varieties and cultivars. Certain characteristics, like disease resistance, plant height, yield potential, and maturity period, set one variety of a cultivated crop species apart from other variations of the same species. A key element of contemporary agriculture is the precise identification of crop types, which is essential for plant breeding, seed certification, and the defence of intellectual property rights. The increasing demand for improved crop varieties with increased yield, disease resistance, and environmental adaptability has demanded the development of accurate methods for differentiating cultivars (Hannah, 2024). Traditional techniques of variety identification rely on morphological, biochemical, and agronomic features, which can be altered by environmental conditions and are generally time-consuming and labour-intensive. Various molecular marker systems have been developed to differentiate plant varieties and species of agricultural and horticultural significance. For effective seed quality control programs and the preservation of breeders’ rights, it is essential to define the identity, genetic purity, and stability of plant varieties. According to Yuan et al., (2022), precise variety identification is essential for determining genetic purity, analyzing genetic diversity, improving germplasm for better agronomic qualities, and getting rid of duplicate accessions.
For plant protection and production systems of Intellectual Property Rights (IPR) and Plant Biodiversity Register (PBR), accurate cultivar identification is crucial. According to Ghosh et al. (2001), molecular marker approaches are pertinent to PBR registration in the following ways. (1) In order to establish a set of comparison cultivars, genetic distance analysis between the candidate cultivar and the current pool of cultivars is performed. (2) The contribution of the comparison cultivar to PBR registration is then determined, and (3) the use of DNA markers to identify cultivars in cases where PBR infringement is alleged is investigated. The capacity of PCR-based markers to amplify particular DNA sequences has made them popular among molecular marker techniques, enabling accurate identification of genetic variations. Although conventional approaches are still employed for Plant Variety Protection (PVP), molecular techniques can be useful adjuncts that increase the effectiveness of accomplishing this goal. During variety certification, additional information from isozyme analysis, RFLP, SSR, SNP, or other genetic fingerprinting methods can be added to cultivar descriptions. Plant varieties can be legally protected on the basis of precise and unique molecular marker data. Crops with little morphological variation benefit greatly from molecular methods. To guarantee complete protection of cultivars, it is ideal to use both botanical and molecular evaluations (Yu and Chung, 2021). Amplified Fragment Length Polymorphism (AFLP), Random Amplified Polymorphic DNA (RAPD), Simple Sequence Repeat (SSR), and Inter Simple Sequence Repeat (ISSR) are the four main PCR-based markers used in crop variety identification that are the subject of this chapter. In plant genetics, each of these marker systems has special qualities, benefits, and uses. While AFLP markers give high resolution and genome-wide coverage, RAPD markers offer a rapid and economical method of polymorphism detection. Due to their high polymorphism and co-dominant inheritance, SSR markers—also referred to as microsatellites—are perfect for genetic mapping

Introduction
The identification of crop varieties is crucial in improving agricultural productivity, ensuring quality control, and supporting breeding programs. The correct identification helps improve food security, conserve biodiversity, and manage pests and diseases. Traditionally, farmers were relying on visual traits and phenotypic characteristics, but these methods had their own limitations. Modern molecular techniques, such as DNA fingerprinting and genotyping, have revolutionized variety identification, rendering it both more accurate and efficient. This has allowed for early identification of varieties, assisting in the selection of desired traits and enhancement of crop resilience, thus contributing to more sustainable and productive agricultural practices (Azizi et al., 2021; Khan et al., 2024).
Importance of Crop Variety Identification in Agriculture
Crop variety identification remains one of the most important components in agriculture since it can improve crop production, enhance food security, and improve adaptability of the crops to diverse environmental conditions (Hasan et al., 2021). The major aspects related to correct identification of crop varieties include:
• Quality control and standardization: It is often to measure the quality of crops that could meet market demand, for optimum consumption, and for export.
• Biodiversity conservation: This helps to conserve native crop varieties and regards the preservation of genetic diversity that is key for breeding and in future agricultural resilience.
• Breeding program: It allows the identification of a variety for selecting traits such as disease resistance, drought tolerance, and yield potential for improving agricultural productivity through its effectiveness.
• Pest and disease management: It contributes to pest and disease management too since different crop varieties may express varying levels of resistance which also reflects in pest control strategy.
• Market preferences and economic value: This helps the farmers to supply some specific markets depending on the market demand of certain varieties so as to gain a profit (Hussain & Nisar, 2020; Sharma & Singh, 2021; Tazeb et al., 2018; Dheer et al., 2020; Jamali et al., 2019).

For applying spectral data models with other machine learning and regression algorithms we have to follow all steps upto Chapter 4 till the partition of whole datainto calibration and validation data set. After that different algorithms is used to develop others models so that,the best model among the different algorithms can be identified and used for better perdiction of soil properties.
5.1 Principal Component Regression (PCR)
Principal Component Regression (PCR) is a regression technique that combines Principal Component Analysis (PCA) and multiple linear regression. It is particularly useful when dealing with multicollinearity in the predictor variables, as it transforms the predictors into a set of uncorrelated components before performing the regression.
set.seed(123)
pcr.CaCO3.pc.223SGS<- train(CaCO3.pc ~., Train_Data.CaCO3.pc.223SGS,
method =”pcr”, trControl = custom,
tuneLength=25, importance = TRUE)
Explaination for code train(CaCO3.pc ~ ., data = Train_Data.CaCO3.pc.223SGS, ...)
Formula (CaCO3.pc ~ .): Specifies the model formula where CaCO3.pc is the
dependent variable and . indicates that all other columns in Train_Data.CaCO3.
pc.223SGS are independent variables.
Data (data = Train_Data.CaCO3.pc.223SGS): The dataset used for training the model.
method = “pcr”:Specifies that the model to be used is Principal Component Regression (PCR).
trControl = custom:Specifies the training control method. custom should be an object of class trainControl created earlier, defining the resampling method (e.g., cross-validation).This example sets up 10-fold cross-validation. tuneLength = 25:Specifies the number of different values for the number of principal components to consider. The package caret will try 25 different values to find the optimal number of components.

4.1. Data Set Generation for Modelling
We have generated 54 transfomed spectra by applying different combination of parameter. Now this spectrum is to be tagged with respective soil sample and desired soil properties for which spectral modeling has to be done. Here we are generaring data tagged with CaCO3.
For that we have to use cbind() fuction. Here we are keeping soil properties in first column and spectra after that column. We are storing data in R memeory and naming data in such way that we can identify properties and transfoemed spectra attached with that data. After generating data for modeling, data is divided into calibration and validation data set by using random sampling method or Kedard-
Stone method
# Data set for CaCO3 with raw spectra ####
Data.CaCO3.pc.113=cbind(data10nm_$CaCO3_equivalent_per, First_ derivative10nm113) #case 1st
Here in case 1st, CaCO3 indicating properties and 113 indcating without smoothed 1st derivative, 1st order polynomial 3rd point spectra is tagged with these soil samples. Same was followed with other cases also

Data pre-processing is a crucial step in data analysis and machine learning that involves preparing raw data for further processing and analysis. Proper preprocessing helps in improving the quality of the data, which in turn enhances the performance of machine learning models. Smoothening VIS-NIR spectra enhances signal quality by reducing noise, crucial for better spectral data interpretation. Key methods include Savitzky-Golay and moving average filters, wavelet transform (WT), and frational derivatives.
Savitzky-Golay Filter: The Savitzky-Golay filter is widely used for smoothing spectral data. It preserves the original shape and features of the signal better than simple averaging filters.Savitzky-Golay Filter fits low-degree polynomials to successive subsets of data points using linear least squares, effectively preserving the signal’s original shape and features, making it ideal for spectroscopy. Moving Average:Moving Average is a simple method that reduces random noise by averaging neighboring data points. While effective, it can sometimes blur sharp features.
Wavelet Transform: Wavelet transform is effective for denoising spectral data, especially when dealing with non-stationary signals.Wavelet Transform (WT) decomposes spectral data into multiple scales, providing both time and frequency information. It is particularly useful for identifying features like peaks and trends. WT includes Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT), and Maximal Overlap Discrete Wavelet Transform (MODWT). Fourier Transform Filtering: Fourier transform can be used to remove highfrequency noise components from the spectra.
Median Filter: Median filtering is useful for removing outliers and preserving edges in the spectral data.
Fractional derivative:Fractional derivative generalizes traditional derivatives to non-integer (fractional) orders, allowing for arbitrary order derivatives. This provides a flexible, nuanced approach to modeling and analyzing complex, nonlinear, or memory-dependent behaviors.