eChapters

Precision agriculture is yet to be popularized for taking its enormous advantages. In laser land levelling and micro irrigation/ fertigation it must have made its prominence but others it is with research institutes where research is focussed on the spatial variability of production environments, development of efficient and suitable data management systems, efficiency of various types of image analyses and optical sensing, efficiency of sensors and related technologies, designs of precision agriculture equipment, optimal inputs and service uses, and their spatial allocations, potentials of unmanned aerial vehicles (UAVs), nanotechnologies and several others.The adoptions of precision-agriculture technologies have, so far, mostly been limited to parts of developed countries. Major contributions to the profitability of precision agriculture is reduced costs of plant protection inputs and higher output revenues.

Abstract
Precision agriculture (PA) represents a transformative approach to farming that leverages advanced technologies to optimize crop production, enhance resource management, and improve overall farm efficiency. At the core of PA  are advancements in GPS, Geographic Information Systems (GIS), drones, and data analysis. These technologies enable farmers to apply inputs with high precision, monitor field conditions in real-time, and make informed decisions based on comprehensive data. This chapter explores the evolution and impact of precision agriculture technologies, examining how GPS, GIS, drones, and data analytics contribute to more sustainable and efficient farming practices. It also discusses the integration of these technologies, their benefits, challenges, and future prospects in advancing agricultural productivity and sustainability.
Keywords: Precision agriculture, GPS, Geographic Information Systems (GIS), drones, data analysis, crop management, farm efficiency, resource optimization, technology integration.

Precision agriculture represents a paradigm shift in farming, leveraging advanced technologies and data analytics to optimize agricultural practices. This article explores the comprehensive role of precision agriculture in modern farming, tracing its evolution and examining key technologies such as GPS, GIS, remote sensing, and IoT. The applications of these technologies in soil management, crop monitoring, irrigation, pest control, and yield prediction are discussed. Benefits, including increased productivity, resource efficiency, and environmental sustainability, are highlighted alongside challenges such as high costs, data management complexities, and the digital divide. The article concludes by considering future trends in precision agriculture, emphasizing the potential of artificial intelligence, machine learning, and blockchain in further revolutionizing farming practices.

Precision agriculture includes information driven administration choices that further develop asset use productivity, bringing about decreased Agriculture expenses while bringing down the natural effects from agriculture. Consequently, information and information assortment frameworks, choice help devices, and information driven hardware and info changes are significant parts of Precision agriculture, participating in three vital farming advances: determination, direction, and performing separately. Before the coordination of shrewd innovations, ICT (data and correspondence innovation) was incorporated into farming devices and mechanical assembly to get certifiable data. Here, remote detecting, mechanized hardware and programming, telemetric, drones, autonomous vehicles, GPSs, and computerized developments were incorporated into cultivating rehearses. For example, the agro-tech association John Deere introduced GPSs for ranch farm haulers, anticipating extended yield and reduced input wastage. The previous status of accuracy agribusiness before brilliant cultivating can be summarized as follows.
Data Collection and Acquisition
Data, data collection, and decision support devices are critical for the distinctive verification and finish of alternate points of view in cultivating. In Accuracy agribusiness, data on individual fields and gathers are collected by seeing, assessing, and distinguishing with different sorts of sensors, yield and soil checking, and remote-detecting mechanical assemblies, such as imaging from drones, groups, airplane, or satellites. As such, “detecting” is a fundamental organization gadget of accuracy agribusiness, which is seeing distinct information and giving data on climatic circumstances, soil conditions, fertilizer necessities, water openness, nuisance and infection stresses, and other field limits. An extent of sensors is used in Accuracy farming. Biomass limits are huge in making decisions to screen the readiness and truly zeroing in on crops. Sensors for mass stream and dampness content are portions of yield screens, along with a differential global positioning system (DGPS) recipient. Appropriately aligned yield screens can produce precise constant

4.1 Introduction

In the modern era, agricultural productivity seems to have reached a maximum production due to the easy availability of fertilizers and pesticides/insecticide, which are used to improve crop productivity. Excess uses of these products and unawareness about field constraints can decrease the crop productivity and endanger the environmental balance in the cultivation area. Therefore, there is an urgent need to improve agricultural system and efficient utilization of these resources for profitable and environmentally sustainable agriculture.

Government has also an emphasis on effective and efficient use of available resources and farmers are also adopting more scientific approach during crop production such as remote sensing, global positioning system, and new equipment to make their agricultural operation more precise.

Precision agriculture is one of the many advanced farming approaches that make production more efficient. It is basically an approach of farm management with the help of information technology to confirm that the cultivated crops and soil receive desire input for optimum health and productivity. In other words, precision agriculture utilizes different tools and technologies to identify infield soil and crop variability for improving farming practices and optimizing agronomic inputs. To reduce dependency on excessive use of chemical inputs in agriculture, several technologies have been applied to make agricultural products safer and to lower their adverse impacts on the environment and human beings, a goal that is consistent with sustainable agriculture. Precision agriculture has emerged as a valuable component of the framework to achieve this goal. Further, Pierce and Nowak define the precision farming that, the application of technologies and principles to manage spatial and temporal variability associated with all aspects of agricultural production for improving crop performance and environmental quality. Precision agriculture is the combination of many different elements working together in a synchronised way with the help of Geographic Information System (GIS) as shown in Fig 4.1. Precision agriculture is basically an information technology based agricultural management system. In this modern management system, Geography information system (GIS) based analysis is extensively used to get land and crop related data accurately and timely, which help farming community to take correct decision .

Introduction

Agriculture is a vital sector in the world economy, as it helps to reduce poverty and promote sustainable development, especially in developing countries. However, agriculture also faces challenges such as increasing productivity and reducing environmental impacts. Therefore, new technologies and methods are needed to improve agricultural practices. One of these methods is precision agriculture (PA), which is based on the principle of sustainable agriculture and healthy food production. PA is a science that uses high technology sensors and analysis tools to improve crop yields and support management decisions. PA is a new concept that aims to increase production, reduce labor time, and optimize the management of fertilizers and irrigation processes.

While conventional agromet advisories have played a crucial role in informing agricultural practices, their inherent broad-scale nature often falls short of addressing the nuanced realities of modern farming. For instance, rainfall predictions at a district level can mask significant variations at the farm or even field level due to local topography, soil types, and microclimatic conditions. Similarly, generic recommendations for irrigation scheduling or fertilizer application may not align with the specific needs of different crop varieties, growth stages, or soil moisture retention capacities within a farm. This lack of granularity can lead to inefficient resource utilization, sub-optimal application of inputs, and increased vulnerability to localized weather events, ultimately impacting productivity and profitability.
9.1. Key Drivers for the Emergence of PAAS
Several converging factors are propelling the shift towards precision in agrometeorology:
• Escalating Climate Variability: The increasing frequency and intensity of extreme weather events, coupled with long-term shifts in climate patterns, demand more adaptive and localized strategies. Farmers need precise and timely information to make informed decisions in the face of unpredictable weather.
• The Imperative for Sustainability: Sustainable agricultural practices necessitate the efficient use of resources like water, fertilizers, and pesticides. PAAS can contribute significantly by providing targeted recommendations that minimize waste and environmental impact.
• Technological Advancements: The rapid development and increasing affordability of various technologies are making PAAS a reality. These include:
• Remote Sensing: Satellite and drone-based imagery provide highresolution spatial data on crop health, soil moisture, and other critical parameters.
• Internet of Things (IoT) Sensors: Networks of sensors deployed in fields can continuously monitor local weather conditions, soil properties, and plant physiological parameters.

As per 20th Livestock Census (2019), Punjab has 1.3% share of India’s total livestock. The share of buffaloes was largest (57.4%) in total livestock population, followed by cross-breed cattle and indigenous cattle. The state of Punjab has about 70.60 lakh livestock in total, comprising of 25.41 lakh cattle, 40.16 lakh buffaloes, 3.48 lakh goats, 0.85 lakh sheep, 0.53 lakh pigs, 0.15 lakh horses and ponies, 1644 mules, 471 donkeys and 170 camels. The share of crossbred cattle increased to 29.3% in 2019 compared to 25.4% in 2012 which signifies that the farmers in the state are resorting to high yielding animals for increasing the milk production. These efforts have led to the highest per capita availability of milk in Punjab which stands at 1271 grams per day compared to national average of 444 grams per day (2021-22), although the share of Punjab in country’s milk production is 6.37%. Punjab is sixth largest producer of milk in India, with production of 14.76 million tonnes (t)/year.

Climate change has emerged as one of the most pressing global challenges of our time, with far-reaching consequences for ecosystems, economies, and societies. This has led to the higher temperatures, changing precipitation patterns, sea level rise and growing frequency and intensity of extreme weather events such as drought, floods, extreme heat and cyclones resulting in disruption of food supply chains. The most critical domains that impact environmental sustainability includes energy, transport, food, waste and water , although there are disparities in the consumption patten of high-income and low income countries. Apart from the food domain (33% of total GHG emissions), meat from ruminants and dairy products constitute the major proportion of carbon footprint. Although only 18% of calories are obtained from meat and dairy products, but they together account for 60% of agriculture’s greenhouse gas emissions and occupy 83% of the world’s arable land. With 18% of anthropogenic greenhouse gas (GHG) emissions, the livestock sector has been identified as one of the primary causes of climate change and the dairysector accounts for around 3.0- 5.1% of the total GHG emissions globally.

Introduction

Agriculture faces a massive challenge: by 2050, it needs to sustainably feed over 9 billion people while supporting economic growth and protecting the environment. Meeting this demand requires a staggering 70% increase in food production from 2006 levels, mainly by making the most of existing farmland and resources.

Precision agriculture offers a promising way forward. It's about smartly managing soil and crops using technology and information to boost profits while being eco-friendly. This approach involves giving the precise amount of resources—like water, nutrients, and pesticides—at the right time and place to optimise farming. It's all about understanding and using the differences within f ields to improve crop growth and protect the environment. Traditionally, understanding these differences involved collecting tons of soil and plant samples for lab analysis. But this can be costly. So, finding better, more efficient ways to assess these differences in soil and plant growth is crucial.

Precision agriculture relies on high-tech tools like GPS, GIS, yield monitors, and remote sensing to measure differences in soil and crop growth across f ields. By integrating these technologies with tools that adjust resource usage based on these differences (called variable rate technologies), farmers can apply fertilisers and water precisely where they're needed, optimising crop yields (Ranjan et al., 2022a). This method has three main parts: collecting data, understanding that data, and then making smart decisions based on it, all tailored to specific areas and timings. New technologies like yield monitors, satellites, and handheld sensors have made data collection faster and easier. But the real challenge is making sense of all this information and using it effectively for better farming. This review delves into how these technologies can enhance agricultural production and the hurdles they bring.

Introduction

The global aquaculture and fishery industry is a substantial business, valued at USD 281.5 billion, yielding an impressive 122.6 million tons of aquatic products in 2020 (FAO, 2022). Asia is the largest aquaculture producer, contributing to almost 91.6% of the world's total production (FAO, 2022). Forecasts indicate that aquatic animal production is poised to grow by an additional 14% by 2030 (FAO, 2022). Despite this growth, there remains a pressing need for technological advancements to address critical challenges like water pollution, disease outbreaks, the quality of breeding stock and juvenile fish, and inadequate management practices (Fearghal, 2019; Michael, 2019).

1.1 INTRODUCTION

The share of agriculture in GDP is sharp declining. Its share which was 55.1 per cent in 1950-51 came down to 21.6 per cent in 2003-04 and it further reduced down standing barely at 14.6 per cent in 2009-10. Horticulture acconts for 29 per cent share in gross GDP obtained from agriculture sector from about 13.08 per cent area put forth under horticultural plantations (Anon., 2010). To achieve the targetted growth rate of 4 per cent in agriculture, horticulture has to grow at the accelerated growth rate above 7 per cent. As a result of R&D initiatives made, there has been considerable increase in horticultural produce which is acclaimed as golden revoution. Horticulture sector has emerged as the frontier area in dealing with the situation of livelihood security and the employment generation. However, looking to the pressure arising from rising population and erratic climatic variation, more attention is required towards the development of technology driven horticulture. Precision farming is being viewed as a promise in this regard.

Precision farming is the process by which exact or accurate results of farming can be obtained. It is a management strategy that relies upon detailed, site specific information to precisely manage production inputs.

Maximisation of quality yield by managing site variable specific management practices is the basic of precision farming. More emphasis needs to be given to make the farming sustainable. In order to make precision farming a reality the following area needs due consideration.

Precision farming manages spatial and temporal variability within fields through targeted, data-driven interventions. Onion is particularly suited to precision management because its productivity and quality are sensitive to moisture, nutrients, environmental stress, pests and diseases. This chapter describes applications of GIS, GPS, variable-rate technology, IoT, cloud platforms, drones, remote sensing, multispectral imaging, smart sensors, artificial intelligence, automated systems and crop-response models. These technologies support soil assessment, field preparation, irrigation, fertilization, crop monitoring, stress detection, protection, yield estimation, harvesting and storage. Their potential to reduce input waste and improve profitability is considered alongside constraints involving investment costs, technical knowledge, connectivity, accessibility and data privacy.

Food grain production has experienced a considerable jump from about 51 million tonne during 1950 to 291.95 million tonne in 2019-20 as per second advance estimates. The production of horticulture products has doubled over the past quarter century and the value of global trade in horticulture crops now exceeds that of cereals. For the first time, horticulture production surpassed food production in India during 2013-14 and continues to excel currently.

Introduction

• Agriculture with its allied activities is the largest sector in India with 70% dependency of its rural households for their livelihood and 82% of farmers in the country are small and marginal.
• Agriculture sector alone contributes about 14.5% towards gross domestic product (GDP) of India.
• Food grain production has experienced a considerable jump from about 51 million tonne during 1950 to 291.95 million tonne in 2019-20 as per second advance estimates. The production of horticulture products has doubled over the past quarter century and the value of global trade in horticulture crops now exceeds that of cereals. For the first time, horticulture production surpassed food production in India during 2013-14 and continues to excel currently.
• India is characteristically a country of small agricultural farms, where approximately 80% of total land holdings in the country are less than 2 ha (5 acres) with 30% irrigated land only.
• India produces wide variety of agricultural products because of its varied agro-climatic regions but low farm productivity is major concern, which is around 33% of the best agricultural farms world over. Indian farmers can get more remuneration from the same piece of land with fewer inputs, once the productivity gets increased.

Introduction

An revolutionary method of agricultural management, precision agriculture (or precision farming) optimises each step of the farming process using data-driven tactics and cutting-edge technology. By adapting agricultural methods to the unique requirements of each field—or even just a tiny portion of a field—precision agriculture aims to increase efficiency, decrease resource waste, and boost crop yields. To accurately monitor and control aspects including soil conditions, crop health, and environmental variables, this technique integrates technologies like as GPS technology, remote sensing, IoT devices, and data analytics.

13.1 INTRODUCTION

The hot arid regions are spread over about 31.7 million ha area mainly in the States of Rajasthan, Gujarat, Andhra Pradesh, Punjab and Haryana, which inhabit on an average 61 persons per square km making up a home for population of nearly 20 million people. The Indian arid zone is characterized by high temperature and low and variable precipitation which limit the scope for high horticultural productivity. However, these conditions greatly favour development of high quality production of fruits such as date palm, ber, pomegranate, citrus, aonla, bael, grapes, guava and vegetables such as cucurbitaceous crops, spices and some medicinal plants. The optimized technologies and inputs could increase the existing low productivity. It is now realized that there is a limited scope for quantum jump in fruit and vegetable production in the traditional production areas. The amelioration of the extreme conditions is also considered vital for life support to the inhabitants of this area. The recent awareness regarding the potential of these ecologically fragile lands for production of quality horticultural produce has not only opened up scope for providing economic sustenance for the people of this region, but also for bringing new areas to increase production through horticulture.

The arid horticultural crops have developed and/or modified to perform certain vital physiological functions even under stress. Strong deep root system (ber, bael, aonla, wood apple, jamun, etc.), moisture synchronous flowering and fruit development (ker, lasora, aonla, pilu, etc.) and other xerophytic characters i.e. leaf shedding in summer (ber), scanty foliage (ker), spiny cladode (cactus pear), mucilaginous sap in plant part (ker, gonda, pilu, bael, etc.), sunken stomata and fur/ hairiness and waxy coating on the leaf surface (phalsa, ber, gonda, fig, etc.) thorny nature, and selective or reduced absorption of cation (Na⁺) and anions (Cl^-, SO4^—) for survival under adverse arid conditions are peculiar adaptations. Therefore, cultivation of suitable species of fruit and vegetables in the arid areas will increase the sustenance of the inhabitants and provide the alternate sources of income through development of nurseries, small scale industries and exports.

The hot arid regions are spread over about 31.7 million ha area mainly in the States of Rajasthan, Gujarat, Andhra Pradesh, Punjab and Haryana, which inhabit on an average 61 persons per square km making up a population of nearly 20 million people. The Indian arid zone is characterized by high temperature and low and variable precipitation which limit the scope for high horticultural productivity. However, these conditions greatly favour development of high quality production of fruits such as date palm, ber, pomegranate, citrus, aonla, bael, grapes, guava and in vegetables such as cucurbitaceous crops, spices and some medicinal plants. The optimized technologies and inputs could increase the existing low productivity. It is now realized that there is a limited scope for quantum jump in fruit and vegetable production in the traditional production areas. The amelioration of the extreme conditions is also considered vital for life support to the inhabitants of this area.

4.1 INTRODUCTION

Ever since the man appeared on the earth, he has been harnessing the natural resources to meet his basic requirements. Reference to soil, water and air as basic resources, their management and means to keep them pure are mentioned in the Vedas, Upanishads and in ancient Hindu literature. Agriculture began sometime during 10000 BC and during 1930 AD use of chemical came into vogue (Sarswat et al., 2003). The phenomenal increase in population of both man and animal in the last century and fast growing industrialization and urbanization in last few decades have overstrained the natural resource base, which are getting degraded much faster than ever before. Thus, the attention of whole world is focused on how to increase production to feed the burgeoning population and the question uppermost in every ones mind is “how can we produce enough food without damage to the natural resource base, in a sustainable manner?” Precision farming is a promise in this regard. It appears to reduce risk of crop failure (De Boer, 1997).

What is Precision Farming?

It is information and technology based farm management system to identify, analyze and manage variability within fields for optimum profitability, sustainability and protection of the land resource without disturbing the environment. It is basically means adding the right amount of treatment at the right time and right location within a field.

3.1 INTRODUCTION

Today’s technological advancement has reached a level where a farmer can have access to information and tools to manage mechanized field operations. They can now measure, evaluate and deal variability within field (e.g. soil fertility, water availability and yield) that was known to exist previously but was not manageable, to his advantage. The ability to handle the variation in productivity in the field and maximize financial return, reduce waste and minimize impact on environment has always been objective of an enterprising farmer in horticultural sector, especially those who advocate sound horticultural practices. Since precision farming proposes to prescribe strategic farm management decision in the field operations to help to serve this purpose through mechanization, sensing and communication technology, automate data collection, documentation and utilization of this information. Such an approach in horticultural production management gives rise to what is now termed as precision horticulture, soil specific crop management (SSCM), spatially variable crop production (SVCP), smart farming (SF) etc.

2.1 INTRODUCTION

With increasing population, urbanization and continuous depletion of natural resources, there has to be a paradigm shift in farmer’s perception from production to productivity and to profitability. In the present scenario, the major challenges arising are shrinking land and depleting water and other related resources in agriculture. There is need for promoting farmer friendly location specific production system management technologies in a concerted manner to achieve a vertical growth in horticulture production duly ensuring quality of produce and better remuneration per unit of area with judicious use of natural resources. In this endeavour, precision farming aims to have efficient utilization of resources per unit of time and area for achieving targeted production of horticultural produce. With the revolution in information technology, now it has become possible to focus on the positioning of agricultural tools with high degree of accuracy and sophistication for maximizing returns. The combination of information technology and use of hi-tech equipments made it possible for the introduction of precision farming. It is a comprehensive system designed to optimize agriculture production through the application of crop information, advanced technology and management practices. With recent advances in geographic information systems, it has become possible to focus on site specific crop production technologies so as to harness the potential of horticulture through innovative technologies in a sustainable manner.

Introduction

Precision farming is one of the most scientific and modern approaches to sustainable agriculture that has gained momentum in 21^st century. Consider this situation: ‘A farmer goes to his field with his GPS (Global Positioning System) guided tractor. The GPS senses the exact location of the tractor within the field. It sends the signal to a computer on the tractor, which has a GIS, storing the soil nutrient requirement map in it. The GIS, in consultation with a Decision Support System, decides what is the exact requirement of the fertilizer for that location and then commands a variable rate fertilizer applicator, attached with the tractor, to apply the exact dose at that precise location. And all this is done within a second, before the tractor moves further.’ Sounds like an excerpt from a scientific fiction. But this is what precision farming (PF) means to the large growers of the US or European countries. Hence, the first thing that comes to mind is that, this system is not for developing countries, especially India, where the farmers are poor, farming is mostly subsistent and the land holding size is small. But, this is far from the truth as this approach has a large potential for improving the agricultural production in developing world. This article is an attempt to explain the possibility of adopting PF in India and the role of satellite based remote sensing in facilitating that.

In this Chapter the precision farming/ Agriculture is discussed along with its steps of implementation. It also includes how artificial Intelligence plays role to perform precision farming/ agriculture in various way along with its scope, limitation and challenges.

Introduction

The primary source of livelihood is agriculture in India, where two thirds of India’s population, services and the private sector account for the remainder. Agricultural land accounts for about 43 percent of the geographical area of India. In earlier days, India was largely dependent in terms of food imports, but over the years, the country’s production of grain and seeds was self-sufficient, and this effort led to the formation of the Green Revolution. In fact, there was strong effort to achieve food self-sufficiency. This trend has continued up to now and continuous improvements have occurred. Precision agriculture or precision farming means the right thing to do, in the right way, on the right place and at the right time. Precision agriculture is expected to suit the agro-climate activities to improve application precision. Farming land has declined a bit in the last 40 years but the number of farmers has increased. According to the 2010–11 Agriculture Census, a total of 138.35 million operating holdings were estimated (single farmers) and 159,59 million hectares were covered by operations.

Precision agriculture is a management technique that collects, processes and analyzes temporary, spatial and individual data and integrates this with other information in order to support management decisions based on an estimated variability to enhance efficiency of use of agricultural production resources, productivity, quality, profitability and sustainability. Precision Agriculture manages every input of crop production (fertilizer, specifically found calestone, herbicide, seeds, insecticide, etc. Reduce waste, boost income and maintain efficiency Place. Precision Agriculture adapts soil and crop management carefully to suit the different field conditions.

Precision farming is also known as precision agriculture. A farmer is not only expected to produce more for the increasing population and his family but save and protect the fragile environment while practicing it. Advanced technologies with appropriate and exact inputs such as precision farming can take care better of environment. Precision farming is right farming operations at right time leading to maximize profit. Every farming activity performed with precision by doing the right thing, in the right place, in the right way, at the right time and with right technology leading to maximize profit is known as precision farming. It is an efficient, eco-friendly and accurate management of agriculture inputs such as water, seed, fertilizers, pesticides and harvesting tools and machinery to maximize profit and reduce waste. The basic concept of precision farming is to match agricultural inputs and operations as per crop (variety/hybrid/cultivar) and agroclimatic conditions with accuracy to their better use efficiency. Perfection (nothing requisite is wanting) in farming is the key to precision farming.

Precision farming is based on global positioning system (GPS). The unique character of GPS is precision in time and space. Its use is spreading to all farm operations. Precision farming is an integrated information and production based farming system that is designed to increase long term, site specific and whole farm production efficiency, productivity and profitability while minimizing, waste and unintended impacts on the environment. This concept of precision agriculture is based on observing, measuring and responding to inter and intra field variability in crops.

Farm management strategy is the key to precision agriculture that uses integrated smart technologies such as sensors, GIS, GPS, satellite radars, drones, IoT (Internet of things) and data analytics to develop informed crop production and management decisions. The field variations are precisely measured with the help of technologies to optimize applications of inputs such as manures, fertilizers, pesticides, bio-agents and water use to increase crop yield at minimum cost. The aim of precision farming is to increase productivity, decrease production costs and minimize the environmental impact of farming.

Precision farming is an approach, where inputs are utilized precisely to get increased average yields as compared to traditional cultivation techniques. Precision farming aims to improve crop performance as well as environmental safety. In other words, precision farming is the matching of resource application and agronomic practices with soil attributes and crop requirements as they vary across the field. Precision farming can improve both yield and profits by using optimum resources, while at the same time making agriculture more sustainable and less polluting. Sustainable precision farming is the most valuable innovation in farm management that is based on using Information and Communication Technologies (ICTs). This is the most recent innovation technology based on sustainable agriculture and healthy food production. Popular definition of Precision Agriculture (PA), Satellite Farming or Site Specific Crop Management (SSCM) describe the term as ‘a technology enabled approach to farming management that observes, measures, and analyzes the needs of individual fields and crops’.

6.1 Scenario in precision farming

• India’s population is around 1366 million by 2020 which requires food grain production of 350 million tones.

• Considering the current food grain production levels, India needs to raise food grain production levels by not less than 4.00 per cent per year. As the availability of land has decreased, application of fertilizers and pesticides become necessary to increase food production.Indiscriminate use of inputs coupled with improper management practices over a long period has resulted in decline in soil productivity.

• Agriculture production system is outcome of interaction of various agro inputs. Therefore, judicious management of these inputs is necessary for the sustainability of these inputs.

• In our agriculture systems, spatial variability over the landscape is ignored and fertilizers, herbicide, pesticides, fungicides etc. are applied at uniform rate over the whole field.

• It has been recognized that crops and soils are not uniform within a given field. The management of in-field variability in soil fertility and crop conditions for improving crop production and minimizing the environmental impact is the crux of precision farming.

• Definitions • It is defined as the application of technologies and principles to manage spatial and temporal variability associated with all aspects of agricultural production (Pierce and Nowak, 1999).

• It is an integrated information and production-based farming system that is designed to increase long term, site specific and whole farm production efficiency, productivity and profitability while minimizing unintended impacts on environment.

Abstract
The aquaculture industry has undergone significant transformations in recent decades, yet issues like feed waste, pollution, and economic inefficiencies remain unresolved. One possible remedy is precision feeding, which involves providing fish with precise nutrition tailored to their physiology. Precision feeding is an innovative and sustainable approach in aquaculture to optimize feed utilization and reduce waste. Traditional feeding practices often result in overfeeding, nutrient imbalances, and environmental degradation. By matching the feed supply to the dietary requirements of various fish species, precision feeding improves feed efficiency, growth, and health. Advanced technologies, including demand feeders, acoustic systems, sonar imaging, and intelligent feeding devices, enable real-time monitoring and control of feeding processes. These systems reduce environmental pollution while improving operational profitability. Precision feeding has potential, but adoption is hampered by factors like expensive costs, complicated technology, and the requirement for specialist knowledge. As aquaculture grows, precision feeding offers a promising pathway to sustainable practices by integrating automation, artificial intelligence, and real-time monitoring systems. This approach ensures ecological responsibility and aligns with industry goals for productivity and economic viability.
Keywords: Precision feeding, fish nutrition, feed waste, intelligent feeding systems and sustainability

1. Introduction
Aquaculture, the farming of aquatic organisms, has grown rapidly over the past few decades to meet the rising global demand for seafood. As wild fish stocks continue to decline due to overfishing and environmental pressures, aquaculture is playing a crucial role in food production. However, like other forms of agriculture, aquaculture faces significant challenges, with feed management being one of the most critical. Feed represents the single largest operational cost in aquaculture, often accounting for 50-70% of the total production expenses. Efficient feed utilization is therefore central to the economic viability of aquaculture operations. Yet, traditional feeding methods are often inefficient, leading to feed wastage, nutrient pollution in water bodies, and suboptimal growth in farmed fish.
In conventional aquaculture, feed is typically dispensed at set intervals and in quantities based on estimations of fish appetite and growth requirements. However, this approach has several inherent inefficiencies. Overfeeding or underfeeding is common due to the lack of real-time monitoring and precision. Excess feed that remains uneaten sinks to the bottom of ponds or cages, decomposing and contributing to nutrient pollution. This not only represents a direct economic loss but also creates environmental problems such as eutrophication, which can lead to algal blooms, oxygen depletion, and poor water quality. On the other hand, underfeeding can result in stunted growth, poor health, and reduced productivity of fish. Both overfeeding and underfeeding, therefore, represent significant barriers to achieving sustainability and profitability in aquaculture.

Precision feeding aims to supply animals with appropriate nutrients according to their changing physiological state, growth, production and environment. Conventional feeding plans are often static and may lead to waste, increased costs and environmental losses. This chapter introduces an AI-enabled framework integrating animal data, feed composition, growth characteristics and external factors. Regression, ensemble learning and other machine-learning models predict feed intake and weight gain, while linear programming and metaheuristic optimization generate efficient allocation plans. A decision-support system provides recommendations in real time. The approach can improve feed conversion, nutrient utilization, profitability and sustainability while reducing overfeeding, resource wastage and the environmental impact of livestock production.

Precision fisheries, a concept adapted from precision agriculture, involves the application of advanced technologies and data-driven approaches to optimize fishing operations and resource management. This chapter explores the potential of precision fisheries as the future of fishing, offering benefits such as increased efficiency, reduced environmental impact, and improved sustainability. By integrating cutting-edge technologies such as remote sensing, GIS (Geographic Information Systems), IoT (Internet of Things), and machine learning, precision fisheries aim to enhance decision-making, maximize yield, and minimize resource wastage. The chapter discusses key components of precision fisheries, including real-time monitoring, predictive modeling, and targeted harvesting, and examines their implications for the fishing industry, ecosystems, and coastal communities. Furthermore, the chapter highlights challenges and opportunities associated with the adoption of precision fisheries and provides recommendations for policymakers, researchers, and stakeholders to harness its full potential for sustainable fisheries management and food security.

Precision irrigation and fertilizer management in floriculture are revolutionizing the way flowers are cultivated, bringing together the latest advancements in technology to optimize resource use and enhance crop quality. In an industry where the aesthetics and health of flowers are paramount, precision agriculture techniques offer a tailored approach that meets the specific needs of each plant. Utilizing state-of-the-art tools such as soil moisture sensors remote sensing technologies and GPS-guided systems, precision irrigation ensures that water is delivered efficiently, reducing wastage and ensuring plants receive the exact amount of moisture required for optimal growth. Similarly, precision fertilizer management employs techniques like variable rate application (VRA) and soil nutrient mapping to provide plants with the necessary nutrients in precise amounts and at optimal times, avoiding the over-application that can lead to nutrient runoff and environmental harm. This method not only enhances the health and vibrancy of the flowers but also significantly contributes to sustainability in horticulture. By closely monitoring and adjusting water and nutrient inputs, growers can reduce their environmental footprint, conserving water and reducing chemical use. This is particularly important in floriculture, where the visual quality of the product is directly influenced by the growing conditions. With precision techniques, growers can achieve more consistent and high-quality blooms, which are essential for maintaining competitive advantage in the market.

Agriculture sector alone uses nearly seventy per cent of fresh water in the world. Therefore, water use economy in the agriculture sector is becoming a necessity. Increasing flood production is possible reducing water used for irrigation if we can adopt precision irrigation (Fig.11.1).
The precision irrigation has to be practiced by providing water to the crop plants in its root zone to meet the water requirements. The precise irrigation can result in-
• Water saving
• Avoid weed control
• Preventing loss of water due to infiltration
• Preventing loss of water due to surface evaporation from the soil
• Minimise salinity problems
• Improving water use efficiency of crops
Traditionally farmers were practicing flood irrigation. It is a method of watering crops delivering to a field and allowing it to flow over the ground among the crops. It is a simple and inexpensive technique followed in less developed areas. Flood irrigation is not very efficient way as nearly 50 per cent of the water applied in the field will be lost through run-off, soil evaporation and other factors. Flood irrigation is only suitable in wet climates and not effective in sandy soils. It leads to water logging in clay soils. There are several types of flood irrigation such as basin, border, furrow and contour irrigation. Although flood irrigation is not very efficient, the water wastage can be avoided to some extent using some techniques

Rearing animals has been amongst the most ancient professions of humanity and since the advent of domestication, the livestock keepers have been involved with them to a certain extent on financial, social and emotional fronts and thus have been curious about certain key aspects of the livestock under their care. The curiosity can be defined with the ‘five W and one H’ of questions as What needs to be done (requirements)? When to do that (timing)? Where are they (location)? Why is there a need (rationale)? How will that meet my purpose (state)? The farming community has traditionally been using these attributes for making farm decisions by observing the animals constantly, or periodically at key occasions such as milking. Such monitoring, aided by the wisdom of experience of the generations of farmers, led to the identification of the key events or problems in the health and productivity of individual animals. However, such kind of monitoring requires a deeper engagement in livestock keeping than that is desirable in commercial farming and the decisions/ detections are severely limited by the capability of the observer.

Precision Livestock Farming (PLF) is a term that encompasses the deployment of sensors for the capture of key biological information, the use of algorithms for processing the biological information (data science), systems for making inferences and making decisions based on the processed data (artificial intelligence), interfaces that allow for making use of these data (reporting) and implements for automation (robotics). PLF is intended to optimize the process of animal production, health care and welfare by generating vast real time information to be used in various ways. There have been many systems being tried and found fit for practical applicability; whereas, few systems like geolocation and motion detection are extensively applied.

Meeting food demand and escalating food prices are serious issues, as current food scenario indicate that crop demand will increase by 100 to 110% from 2005 to 2050 at global level (Tilman et al., 2011). Precision use of fertilizer is the crucial for achieving sufficient food production for growing population. It is costly input to produce and apply, both financially and environmentally. Fertilizer consumption measures the quantity of plant nutrients used per unit of arable land.

Precision farming involves the collection of data by observation and measurement, and the subsequent response. Sensing is key to the collection of data; whether this is remote from space by satellites, by aircraft or drones, or by proximal sensors used on the ground, mounted on vehicles or otherwise. Responses are increasingly automated and data are often integrated into wholefarm management systems. Early commercial applications were directed towards improving crop nutrition, however, utilising precision technologies to control weeds, pests and diseases is now a reality.

Precision crop protection requires detailed data on the occurrence and distribution of problems at the earliest possible stage of detection. An early detection is the key for successful management of pests in precision farming. Some of the key aspects are:

Identification: Identifications of crops, targets and beneficial; temporal and spatial distribution: presence of the target before, during and after the crop growth period;

Sampling techniques: statistically valid sampling regimes must be used to permit an accurate view of any problem

Thresholds: under particular circumstances, the point at which action to ensure levels of infestation or damage are kept to those deemed to be acceptable;

Prediction: the speed and pattern of development of a problem must be capable of being modelled, e.g. spread and severity of insect pests and fungal diseases; weed emergence according to species, dormancy and environmental conditions.

The development of a new drug begins typically in the chemistry laboratory and is followed by a wide range of biological studies in vitro (cells/tissues) and in vivo (animals), which forecast what the agent would do in humans. The focus of these studies moves from evaluating the safety of a drug for use in humans to looking at its clinical relevance. Pharmacodynamics involves a set of biological studies both in vitro and and in vivo to get a better understanding of the mechanism of action of a drug. Preclinical studies mainly include safety and toxicity to ensure its safety for use in humans. Typically, preclinical in vitro, in vivo, and Phase I testing may not be necessary if preliminary data on safety are available. However, preclinical data or clinical observations are still necessary to justify a rationale for use and safety in initial clinical studies.

Three main screening approaches are generally used in drug development. Simple Screening evaluates substances that have a particular property by one or two tests. There is no need for a series of tests in which the interpretation of results of one test may depend on others. Blind Screening is used for a new plant extract where no adequate pharmacological information is available. Blind screening may give clues for potential activity and worthiness to probe further for pharmacological activities. Programmed screening involves a new plant extract or substance with known pharmacological effects and results in more precise information.

Radiation combined injury (RCI) is complex condition that arises from simultaneous exposure to ionizing radiation and other types of physical trauma, such as blunt force injuries, thermal burns, or chemical exposure. This combination significantly complicates diagnosis and treatment, leading to increased morbidity and mortality. The past experiences of Hiroshima, Nagasaki, and Chernobyl have revealed that a large fraction of victims of such nuclear accidents or attacks suffer from combined radiation injuries. The possibility of a nuclear attack seems very far-fetched, but the destruction that would occur in such events will be massive, with a huge loss in terms of lives. Therefore, preparedness for the same should be done beforehand. Ionizing radiation damages cellular structures, primarily through direct ionization of DNA, leading to a range of acute and chronic health effects.

The severity of radiation damage is influenced by the dose, type of radiation, and duration of exposure. Simultaneously, traumatic injuries can result from various mechanisms such as combat, accidents, explosions that may involve blunt trauma, lacerations, or burns. These injuries can lead to significant blood loss, organ failure, and infections, which may be aggravated by the immune suppression caused by radiation exposure. The interaction between radiation effects and traumatic injuries can create a unique clinical challenge, as each type of injury can exacerbate the other’s impact on the body. The pathophysiology and prognosis of RCI are more intricate and challenging to manage compared to simple burns, wound or radiation injuries. Although RCI is uncommon, it poses significant risks during unfortunate events like nuclear or radiological terrorist attacks and nuclear accidents. Historical nuclear incidents have shown that 65-70% of victims experienced varying degrees of thermal burns and other traumatic injuries alongside radiation exposure. These victims often sustain damage to nearly every organ system, particularly the gastrointestinal, cardiovascular, neurological, and hematopoietic systems, all crucial for survival.

The domestication of oxen in Mesopotamia and the Indus Valley Civilization, perhaps as early as the 6th millennium B.C. provided mankind with the draft power necessary to develop the early plough into the larger, animal-drawn true ard (or scratch plough). Some of the earliest evidence of ploughing from around 2800 B.C.was found at the Indus Valley site of Kalibangan. Subsequent archaeological finds in Prague, Czech Republic, push the oldest known ploughed field even further back to 3500-3800 B.C. All food produce was planted and harvested by hand: seeds were scattered by hand and planted grains were brought from the field to be threshed on the dooryard. Milking a small herd of cows or goats was also done by hand. This continued for centuries. Then man discovered electricity.

In 1746, just a few years before Benjamin Franklin of Philadelphia sent a kite to catch lightning in a storm, a Doctor von Maimbray gave talk to the Royal Society of London where he described his experiment of electrifying the myrtle bush (Morella cerifera) which then put forth new branches in October, something which had never happened before. This news inspired others in Europe to carry out similar experiments. In 1749, Abbot Jean Antoine Nollet of the Royal Academy of Sciences in Paris France, inventor of the electroscope, published a pamphlet on “The Harmful Effects or Advantageous of Electrical Phenomena on Plants.”¹Isolated experiments continued until in 1783 Father Pierre Bertholon de Saint-Lazare of Montpellier published “About the Electricity of Plants”, a work in which he dealt with the effect of atmospheric electricity on plants, taking in their medicinal and nutritive-electrical virtues and but in particular the practical means of applying it usefully to agriculture.²He even built an electro-vegetometer, a device for collecting atmospheric electricity to distribute it in the soil and then to the plants, but he met with little success. Bertholon also conceived of subjecting plants to “electric rain” by connecting a large syringe to an electric source. A gardener, standing on a slab of insulating material in a wagon, carried a sprinkling can electrified by means of a wire leading to a static machine. “By means of this process, strange for the times, the good Abbé Bertholon, who was considered something of a sorcerer, obtained lettuces of an extraordinary size.”3

Abstract
 
The definition of what drought is and what drought is not, has profound implications for the environment and all segments of society, yet it may be different for each. Many attempts have been made to develop a comprehensive and meaningful definition. A generic definition provides a starting point:  Drought is a persistent and abnormal moisture deficiency having adverse impacts on vegetation, animals, and people. The public perceives “drought” as a serious departure from normal water conditions, a departure that requires a public response to reduce negative impacts. About two thirds of the geographic area of India receives low rainfall (less than 1000 mm), which is also characterized by uneven and erratic distributions.

Dry matter intake (DMI) is crucial for animal performance, especially in cattle. It ensures that animals receive the necessary nutrients and energy for metabolic functions. Cattle have complex digestive systems with unique metabolic functions. Variations in feed intake are essential throughout the growth cycle to maintain a balance in response to metabolic and environmental challenges.
Limitations in feed intake can lead to nutrient deficiencies, impacting growth rates and overall profitability. Feed stress can also result in health issues and digestive disturbances.
Importance of Predicting Dry Matter Intake (DMI) in Cattle Using NRC Guidelines
Predicting Dry Matter Intake (DMI) in cattle is crucial for managing livestock health, productivity, and economic efficiency. The National Research Council (NRC) offers guidelines to estimate DMI, aiding producers in optimizing feeding strategies. Here’s why predicting DMI is essential:
1. Optimizing Nutrient Intake
• Meeting Nutritional Needs: Accurate DMI prediction ensures cattle receive sufficient energy, protein, vitamins, and minerals for maintenance, growth, lactation, or reproduction.
• Avoiding Deficiencies: Inadequate feed can result in poor health, reduced production, and reproductive issues.
• Preventing Overfeeding: Excessive intake can lead to health problems like acidosis, obesity, or metabolic disorders.
2. Improving Productivity

Despite the tremendous advances in agriculture, the agrarian output is still weather and climate-dependent. Frost or low-temperature stress is a single weather-related phenomenon that causes losses greater than any other environmental or biological hazard. As the plants are immobile, they are bound to undergo this stress and suffer damage under natural condition. For avoiding and managing the adverse effects of frost-induced freezing in plants, a reliable early warning/prediction of intensity and duration of frost is necessary.

Abstract

Gastrointestinal parasitic infections particularly Haemonchus contortus infection in goats is very common and found as major nematode infections in farm condition. To chalk out the control programme and to prevent the initiation of anthelmintic resistant parasites, it is essential to know well in advance the prevalence of H. contortus infection. During the year 2009, a study was undertaken on H. contortus infection in the goat farm of ICAR Research Complex for NEH Region, Umiam, Meghalaya, which is a subtropical hill area and bioclimatograph was prepared to predict the prevalence of H. contortus infection in goats of this area. Meteorological data of this area was collected to visualize the effect of temperature, rainfall and humidity. Bioclimatographs were prepared in which total rainfall (TRF) or relative humidity (RH) is plotted against the maximum temperature (T-max) or minimum temperature (T-min) for the months and the resultant points are joined by a closed curve. The lines constituted the windows on these bioclimatographs, indicating the limit of the climatic conditions most suitable for development and survival of exogenous stages of gastrointestinal nematodes.

According to Fereshteh et al. (2020) microorganisms use biofilm to protect themselves against the harmful effects of the host body immune system and the surrounding environment. This helps them to survive against the anti-microbial agents. Inhibition of bacterial biofilm formation is very time consuming and expensive. Newer computational tools have been found to be a good option to predict the inhibition of biofilm peptides. In a computational prediction tool a large number of peptide sequences can be screened to select the peptides for validation. Vectors are extracted from the peptide primary structure and classification algorithms are carried out which includes SVM, random forest and k-nearest neighbour to evaluate their performance. Using a combination of features, helps in good evaluation. National Institute of Health (NIH) has found out that 80% of bacterial pathogens form biofilms and their multicellular structure makes them resistant to the hosts’ defense systems and traditional antimicrobials than planktonic bacteria. Recently, the use of Antimicrobial peptides

Predictive analytics estimates future health risks and outcomes, while prescriptive analytics recommends appropriate interventions and resource-allocation strategies. This chapter explains how statistical models, machine learning and healthcare data can support disease-risk assessment, prognosis and personalized treatment. Applications involving cardiovascular disease, diabetes, cancer, chronic-disease management and early-warning systems are examined. The integration of clinical, genomic, lifestyle and electronic health-record data is discussed alongside survival analysis, disease-progression modelling and treatment-response prediction. The chapter also addresses bias, fairness, transparency, privacy, regulation and interoperability. Future developments involving real-time analytics, IoT-enabled monitoring and precision medicine illustrate the transition toward proactive, preventive and data-driven healthcare.

Predictive modelling of plant growth can help farmers and researchers improve crop management, resource utilization and production planning. This chapter examines machine learning approaches for predicting important plant growth characteristics. It introduces plant growth fundamentals and the environmental and management factors that influence development. The chapter covers the machine learning workflow, including data acquisition, preprocessing, feature engineering, model development and evaluation. Supervised learning, unsupervised learning and deep learning approaches are considered for predicting plant height, biomass, canopy development, leaf area and yield-related characteristics. Data from IoT devices, drones and satellite imagery can strengthen prediction models. The chapter also discusses precision agriculture applications, limitations and emerging technologies such as explainable AI, digital twins and edge AI.

Abstract

The study of computational toxicity prediction is very crucial and necessary, as it is quite time consuming and expensive to measure the toxicity via experimental techniques (Quan Liao et al., 2006). The use of computational approaches particularly to reduce the time and cost for the evaluation of the kinetic and toxic behaviour of any drug has been increased tremendously in recent time period (Richard Judson et al., 2004). Machine learning is a powerful tool to discover new complex combinatorial relationships in various in vitro and in vivo data information (Richard Judson et al., 2004) and has been successfully used in many diverse fields with numerous applications such as pharmacodynamics properties prediction (Czerminski et al., 2001; Livingstone et al., 2003), protein function prediction (Cai et al., 2003), medical decision making (Veropoulos, 2001), spam categorization (Drucker et al., 1999), detection of oil spills (Kubat et al., 1998), and speech recognition (Nuttakorn et al., 2001).

6.1 Age

Age is one of the most important risk factors for developing mammary tumors in dogs. A comprehensive study carried on dogs has found that the risk of mammary tumors become significant by 8 years of age and increases linearly thereafter. The age of onset of tumors, however, may vary depending on the life span of the dog breed. An average age of CMT onset is 8-11 years, indicating as a condition of the middle-aged or older dogs. Mammary tumors are generally rare in dogs under 3 years of age, unless the dogs have been given exogenous hormones such as progestins

6.2 Breed

Most consistently listed breeds in which mammary tumor occur include, English springer spaniel, Brittany, Cocker spaniel, English setter, Pointer, Afghan hound and German shepherd, in addition to the smaller breeds such as Poodle, Maltese, Chihuahua, Beagle, Dachshund and Terriers. These studies clearly show that the risk for mammary tumors has some breed association and, therefore, having a genetic component. Pure breeds of dogs are generally at a greater risk i.e., known to account for 80% of the submitted cases. Some studies have further shown that certain lines within specific breeds are at more risk than others.

SUMMARY

Multiplicity of cropping systems has been one of the main features of Indian agriculture and it is attributed to rain-fed agriculture and prevailing socio-economic situations of farming community. It has been estimated that more than 250 double cropping systems are followed throughout the country and based on rationale of spread of crops in each district in the country, 30 important cropping systems have been identified. Cropping systems of a region are decided by and large, by a number of soil and climatic parameters which deter mine overall agro ecological setting for nourishment and appropriateness of a crop or set of crops for cultivation. Much of the Madhya Pradesh area also coincides with the rainfed semi-arid agro-ecological region and sub-regions which are upland, forested with low agricultural productivity and predominately tribal population. It would appear that over the centuries – including in recent history tribals have been inexorably moved off the better lands, into areas which from an agricultural perspective have low productive capacity and little potential for growth. Low agricultural productivity is one of the main causes of persistently high poverty rates in the state and the poor masses not only depends upon the farming system but also chosen different alternatives for their sustanability.

The present study was carried out to assess the role of endogenous cannabinoids in regulating aortic vascular response during early and late phases of sepsis. Polymicrobial sepsis was induced by caecal ligation and puncture (CLP) in mice. Besides, recording the isometric tension in arterial rings, estimation of haemato-biochemical parameters, histopathological examinations of vital organs, mRNA expression of CB1 receptor and MAGL enzyme and their role in the vasoconstrictor response to noradrenaline in the aorta of septic mouse were studied. Sepsis significantly reduced RBC, Hb and WBC counts during both early (CLP-6h) and late (CLP-20h) phases of sepsis whereas neutrophil count was increased during early phase. There was also a marked fall in lymphocyte count during late phase of sepsis indicative of immunosuppressive state. Significant rise in the plasma ALT, AST, BUN and creatinine levels during early and late phases of sepsis were suggestive of liver and kidney dysfunctions which were further substantiated byhistopathological examinations of these vital organs. Sepsis produced a state of hypoproteinaemia with significant reduction in plasma albumin level.