eChapters

Introduction

1. Serious outbreaks of thrush have been observed in chickens, pigeons, geese, turkeys, pheasants, quail, peacock and many other species of birds.
2. Candida albicans is the most commonly isolated fungus from the outbreak. Mucor spp. and Aspergilli were also found in some cases.
3. The outbreaks are associated with unhygienic conditions and nutritional deficiencies. Infection is more common in young birds up to 10 weeks of age. Morbidity is very high and mortality ranges from 10-75%. Clinical signs are not specific. Poor growth and ruffled feathers are only clinical signs.

Etiology

Cryptococcosis (KRIP-toe-cock-co-sis) is caused by the fungus, Cryptococcus neoformans (KRIP-toe-cock-kus knee-O-for-mans). It is found worldwide and primarily affects cats and people with weak immune systems.

It is a fungal infection of skin and the term favus is used for the disease in poultry. The disease is world wide in distribution.

Etiology

Microsporum gallinae, a fungus. The fungus is zoonotic. Lesions of ring worm are produced in humans in contact with infected birds.

Diseases caused by toxic metabolites of fungi are called mycotoxicosis. It affects man and animals including birds. Fungi which grow on feeds and grains in an unfavorable environment mostly cause mycotoxicosis in birds.

The disease is worldwide in distribution and remains the major parasitic problem in the poultry industry. The problems occur principally in poultry maintained on deep litter.

Etiology

Coccidiosis of all classes of poultry is caused by one or more of the many species of coccidia of Eimeria species. In chickens nine main species are encountered. Eimeria tenella, E. necatrix, E. maxima, E. acervulina, E. mitis, E. mivati, E. hagani, E. brunetti and E. preacox.

Cryptosporidiosis is a protozoan disease.

Species

1. C. meleagridis: Infects small intestine.
2. C. baileyi: Infects digestive tract (bursa of Fabricius and cloaca), kidneys and respiratory tract.

Histomoniasis is a protozoan disease.

Etiology

Histomonas meleagridis.

Hosts

Mainly disease of turkey but has also been reported in chickens, peafowl, grouse, quail and gallinaceous birds.

Etiological Agent

Trichomonas gallinae, a flagellate protozoan. Strains vary in pathogenicity. The disease continues to be of significance in pigeons and doves. It has also been reported in raptors.

Introduction

Chickens are considered one of the most important hosts in the epidemiology of Toxoplasma gondii infection because they are an efficient source of infection for cats that excrete the environmentally resistant oocysts and because humans may become infected with this parasite after eating undercooked infected chicken meat.

Poultry meat is an important part of cuisine, consumed widely all over the world; therefore, consumption of uncooked or not properly cooked poultry meat may pose a risk factor for T. gondii infection in humans or animals.

Introduction

A. Common species of nematodes encountered in poultry are:

1. Syngamous trachea (in the respiratory tract) large 2.00cm size live in the trachea and larger bronchi. Many species of domestic and wild birds are affected. Dyspnea, gasping and head shaking are main clinical signs. Treatment is difficult.
2. Ascaridia galli (in the intestine) They parasitize chicken, turkey, pigeon, quail and guineas. In younger birds parasites cause enteritis. In heavily infected birds clinical signs are emaciation and diarrhea.

Introduction

1. Various genera of tapeworms are found in poultry. They are:
   1. Raillietina spp.
   2. Choanotaenia spp.
   3. Davainea spp.
   4. Amebotaenia spp. and
   5. Hymenolepis spp.
2. Tapeworms mainly parasitise the small intestine. Tape worms deprive the bird of its nutrition and cause weight loss and decreased egg production.
3. Out of 3 species of Raillitina, Raillietina echinobothrida is one of the pathogenic species as it produces granuloma at its attachment site (nodular disease).
4. They all require intermediate host for their development.
5. Intermediate hosts are common houseflies, ants, beetles and grasshoppers, which makes control of tapeworms difficult.

HAEMOPROTEUS

Host

Infection with Haemoproteus species is host specific.

H. meleagridis – domestic poultry and pet birds

H. olumbae in pigeons

H. nettionis in waterfowl.

LICE

Introduction

Lice are common ectoparasites of birds. There are many species of bird lice and are found on chicken, turkey, guinea fowl, duck, goose and pigeon. Lice species prefer a different part of the body. Most of them are straw colour and measure 1-10 mm. Lice from one host can parasitise another host if they are in close contact. For example, a louse from chicken can parasitise guinea fowls kept on the same premises. Hence, the species of lice is determined if cross contamination from another species is suspected. Lice are not highly pathogenic to mature birds. However, severe lousiness may lead to weight loss, as well as low production and death.

Vitamin A

Source of Vitamin

Berseem, legumes, alfalfa or other grass meal, fish oils, yellow corn, palm oil, commercial Vitamin A concentrate

CALCIUM & PHOSPHORUS

Source of mineral

Soluble grits (Limestone grits containing chiefly CaCo3) which is soluble in acid medium of crop. It differs from insoluble grits (gravel, granites, sand etc.) which acts only mechanically in the gizzard.

Pododermatitis or bumble foot has many causes including excess weight bearing from obesity or unequal weight bearing between the two feet as a result of lameness of one foot, causing less weight to be placed on that foot and more on the contralateral foot, or from abnormal abrasions of the plantar surface from inappropriate substrate (too sharp or rough, wire, etc.), decreased blood supply to the foot (sometimes from lack of exercise), trauma, or standing for prolonged periods of time, especially in ducks that are not provided with adequate swimming opportunities.

Bumble foot is commonly seen in waterfowl under one or more of the following conditions; obesity, hard substrate, lack of access to water to swim in, malnutrition and underlying disease.

This is localized infection of the feet. It is seen as bulbous swelling of the footpad and surrounding tissues. It appears usually after an injury to the foot pad and causes lameness in one or both feet. Pododermatitis is divided into varying grades depending on the literature source used, but generally includes mild, moderate, and severe grades with the severe grades including osteomyelitis. Infection by various bacteria commonly occurs and superficial wounds may not be present. Sometimes ulcers are formed on the planter surface of the foot. The layers become lame and production lowers, sometimes completely stops. On opening the swelling a foul smelling thick pus is found. The condition is more common in heavy breeds. Early surgery and antibiotic therapy may control the condition. Removal of high roosts reduces the incidence.

Cage layer fatigue is a term used to describe leg weakness and acute deaths in chickens in cages, and is caused by inadequate calcium, phosphorus and Vitamin D levels in the blood stream. Calcium is required for muscle function, bone formation, and egg shell formation. This condition may be seen even in floor birds under certain conditions. It is seen most often in young hens early in production.

Introduction

Although the causative agent for the condition has not been identified, experimental studies indicate the association of parvovirus and or Astrovirus. Under experimental conditions, the virus induces clinical signs onlyin fast growing broiler chickens. The clinical signs, based on experimental inoculation of chickens, consist of watery or mucoid mustard yellow colour diarrhoea within 3-5 days after infection. Chickens become stunted, and a variable number of such chickens die during 10 and 20 days. Pronounced weight loss is in around four week’s age. The mortality is low (5 - 10%) however, the morbidity is very high (50 - 80%).

Introduction

1. The condition has been reported the world wide in growing broiler chickens.
2. The affected birds show clinical signs usually during 4 - 5 weeks of age. The clinically affected birds are smaller, listless with ruffled feathers, pale head and shrunken comb. Severely affected birds have distended abdomen, which restricts their normal movement. Death may occur suddenly, and not all the broilers which die of the condition have ascites. The incidence of the disease varies between 2 to 20%.

Introduction

Hypoglycemia Spiking mortality syndrome is a disease in broilers, characterized by low mortality with sudden onset of high mortality. The affected birds are clinically hypoglycemic.

There is no known aetiology.

The condition is characterized by proventriculitis and dilatation of proventriculus.

Etiology

The cause of transmissible viral proventriculitis (TVP) is a birnavirus referred as Chicken proventricular necrosis virus (CPNV). TVP is a disease of 3-8 week-old broiler.

Seen in mature chickens. Condition results from myocardial degeneration which causes bilateral ventricular hypertrophy and dilation. Etiology is not known.

Round heart disease in turkey causes mortality in 1-4 weeks of age. Lesions are severe cardiomyopathy, ascites, hydropericardium and generalized congestion of tissues. No aetiology is known.

Introduction

Progress in plant biotechnological research has opened many avenues for basic and applied research in the field of crop plants. Plant tissue culture is an important component of biotechnology that involves in the improvement of crops. Besides this, plant tissue culture provides a good system for many basic studies in plant breeding, plant physiology, genetics and cell biology. Further, manipulation of cells through the modern technique of genetic engineering may lead to an introduction of new gene(s) to a host plant and the techniques of genetic engineering rely on plant tissue culture techniques. The regeneration of plants through plant tissue culture and their subsequent acclimatization and delivery to the field possesses many problems to make tissue culture technology a viable alternative proposition. The successful development of encapsulation technique for preparation of plant propagules in a nutrient gel has initiated a new line of research on artificial seeds. Application of this technology has been well recognized in several agronomically important crops and forest trees. Artificial seeds make a promising technique for propagation of transgenic plants, non-seeds producing plants, polyploids with elite traits and plant lines with problems in seed propagation.

2.1. LABORATORY SETUP

The setting up of a tissue culture laboratory needs proper planning. It depends on the nature of research works to be under taken and/or commercialization of tissue culture technology and the availability of space. The ideal tissue culture laboratory should have the following components:

1. Store facility for chemicals
2. Media preparation room
3. Autoclave room
4. Storage facilities for autoclaved laboratory-ware and media
5. Washing facilit
6. Culture room
7. Inoculation room
8. Instrumentation room
9. Hardening facility

To mimic the natural seeds, the embryos from cultures are encapsulated in a nutrient gel containing essential organic/inorganic salts, carbon sources, plant hormones and antimicrobial agents and coated completely to protect the embryos from mechanical damage during handling and to allow the development and germination without any undesirable variation. When looking at the history of synthetic seed technology, a wide number of encapsulating agents have been tested in time for their capacity to produce beads like, potassium alginate, sodium alginate, carregeenan, agar, agarose, guargum, polyox, gelrite, sodium pectate, carboxymethyl cellulose, nitrocellulose, ethylocellulose, polyethyleneamine (Kersulec et al., 1993), chitosane (Tay et al., 1993), tracanth gum etc. have been tested as hydrogels (Table 3.1), out of which alginate emasculation was found to be more suitable and practical for synthetic seed production. Some plants exudates of arabic (Acacia senegal), karaya (Stereculsia sp.), or seed gums of gaur (Cyamopsis tetragonoloba), tamarind (Tamarindus indica) or microbial products like dextran, xantham are also being used for encapsulation.

The artificial seed production technique was first used in clonal propagation to cultivate somatic embryos placed into an artificial endosperm and constrained by an artificial seed coat. Today artificial seeds represent capsules with a gel envelope, which contains not only somatic embryos but also axillary and apical buds or stem and root segments (Vdovitchenko and Kuzovkina, 2011).

The prime requirements for preparation of artificial seeds are high frequency somatic embryogeniccalli, synchronous embryo development and maximum conversion of embryos into plants. Success in production of synthetic seeds mainly depends on how best callus development and plantlet regeneration are achieved. However, over time the morphogenetic competence of differentiated cultures declines (Lynch and Benson, 1991). Therefore, the primary goal of synthetic seed production is to produce somatic embryos that resemble more closely to the true seed embryo in storage and handling characteristics so that they can be utilized as a unit for clonal propagation and germplasm conservation. Synthetic seeds may be hydrated or dehydrated and may be quiescent or not. Encapsulation of micropropagules enables to satisfy the requirements of synthetic seed development. The gelling agents used for encapsulation for production of synthetic seeds act as protective cover. The encapsulated synthetic seeds also contain growth nutrients, plant growth promoting microorganisms (mycorrhizah, Rhizobium, etc.), and/or other biochemical constituents necessary for optimal embryo-to-plant development (Fig. 4.1). Fungicides may also be used in the liquid gelling medium to protect them during storage and germination in vitro and in vivo.

As stated by Murashige’s definition, artificial seed (or synthetic/synseed) technology was initially restricted to species in which somatic embryogenesis was possible. Later, Bapat and co-workers (1987) proposed to broaden the technology to the encapsulation of various in vitro-derived propagules, and they used axillary buds of Morus indica as a first example of this new application. The new concept paved the way for the encapsulation of explants other than somatic embryos, and to the formulation of a new definition of artificial seeds (Aitken-Christie et al. 1995) as “artificially encapsulated somatic embryos, shoots, or other tissues which can be used for sowing under in vitro or ex vitro conditions”. In other words, the “new” artificial seed, lost the original bond of containing an embryo (zygotic or somatic), can now contain any kind of explant from tissue culture (such as axillary buds, shoot tips, nodal segments, bulblets, protocorms, callus samples, cells) which, following ‘germination’, will evolve into a plantlet or a shoot (artificial seed conversion) or will produce new cell proliferation (artificial seed regrowth).

Realization of synthetic seed technology requires manipulation of in vitro culture system for large-scale production of viable materials for preparation of synthetic seed, subsequently conversion into complete plants under in vitro as well as in vivo conditions. Somatic and gametic embryogenesis that enhanced axillary bud proliferation are the efficient techniques for rapid and large-scale multiplication of desirable plant species. Plant tissues such as somatic embryos, apical shoot tips, axillary shoot buds, embryogenic calli, and protocom-like bodies are potential micropropagules that have been considered for creating synthetic seeds. Encapsulations of somatic embryos, apical and axillary shoot buds, and regeneration of whole plants from them have been reported for a number of plant species (Redenbaug et al., 1986; Mathur et al., 1989; Ganapathi et al., 1992; Lulsdorf et al., 1993).

In fact, as the concept of synthetic seed involves the use of small propagules and enables the direct sowing of this material in vitro or in vivo, the technology could provide great flexibility to the breeders by not only reducing the costs when large quantities of propagules are required for handling, shipping and planting, but also eliminating the acclimation step when direct sowing in vivo is applied (Onishi et al. 1994).

The artificial seed technology has been applied to a number of plant species belonging to angiosperms. The exact application of artificial seeds will vary from species to species. In self-pollinated crops that currently have good seed production systems, synthetic seeds will not have any practical applications, but in cross pollinating species, especially those where seed production is difficult and expensive, synthetic seeds offer many advantages and opportunities. The importance of encapsulation technique as an aid to propagation in various crop plants has been discussed by many workers. By combining the benefits of vegetative propagation system with the capability of long-term storage and with clonal multiplication, synthetic seeds have many diverse applications in agriculture (Gray and Purohit, 1991; Redenbaugh et al., 1991; Redenbaugh, 1993). In some species seed propagation is not successful. The reason behind this may be due to heterozygosity of seed, small sized seed, presence of reduced or insufficient endosperm, and seedless varieties of crop species like grapes, watermelon, mango, guava etc. Sometimes association of mycorrhizal fungi is essential for seed germination, for example orchids. The germination of these seeds under natural conditions or under control environment is not possible. Propagating of these species through synthetic seed is the added advantage. The possible implications of synthetic seed technology have been detailed hereunder.

Plant tissue culture is mainly referred to as mass-multiplication of in vitro derived plantlets grown under controlled environment. Subsequently, the production of artificial seeds from in vitro grown plant propagules is also a method of mass-production of true-to-type plantlets. Micropropagation allows rapid and mass production of high quality, disease free and uniform planting material irrespective of the season and weather. Micro-shoots on being transferred to ex vitro conditions are exposed to abiotic (altered temperature, light intensity and humidity conditions) and biotic stress conditions i.e. soil micro-flora, so there is need for acclimatization for successful establishment and survival of plantlets (Deb and Imchen, 2010). In order to increase growth and reduce mortality in plantlets at the acclimatisation stage, research has been focused on the control of the environmental conditions (both physical and chemical) and to acclimatize the plants to compete with soil microflora (Mathur et al., 2008). In this chapter methods of acclimatization of plantlets derived from simple plant tissue culture or from synthetic seeds are discussed.

Many researchers successfully prepared synthetic seeds of crop plants. The major success of this technology is being elaborated in this chapter.

8.1. FIELD CROPS

Rice is the world’s most important food crop and a primary food source for more than one third of the world’s population. This crop has received considerable attention in biotechnology research progammes. Research in artificial seeds in rice is not scanty and this technology through somatic embryogenesis would offer a great scope for large-scale propagation of superior elite hybrids (Brar and Khush, 1994). However, success on artificial seeds research in field crops, especially cereals is still in infancy. However, many workers successfully encapsulated somatic and androgenic embryos of cereal crops (Datta and Potrykus, 1989; Suprasanna et al., 1996; Roy and Mandal, 2004 & 2008). Brar et al. (1994) emphasized the need for research on artificial seeds in rice through embryogenesis and outlined its impact on mass propagation of true-breeding hybrids. Somatic embryogenesis can also be used in the regeneration of genetically transformed plants (Vicient and Martinez, 1998). Subsequently those somatic embryos could be economically and successfully propagated though synthetic seed technology.

Artificial seeds have been widely used for micro-propagation of many plant species. Establishment of gene banks for ex situ conservation of plant germplasm in the form of field gene banks, seed gene banks, in vitro collections, and cryogenically preserved tissues is a common practice (Withers 1983; Rao, 2004; Borner, 2006). Alginate encapsulation provides a viable approach for in vitro germplasm conservation as it combines the advantages of clonal multiplication with those of seed propagation and storage (Standardi and Piccioni, 1998; Ara et al., 2000).

The main aim of artificial seeds is to obtain true to type plants to maintain the germplasm, but, however, during tissue culture, there is a chance of genetic aberration which is commonly known as somaclonal variations. Larkin and Scowcroft (1981) for the first time used the word ‘somaclonal variation’ to describe these variations as displayed among the plants derived from tissue culture. It is clearly understood that genomes modify themselves when exposed to unfamiliar conditions, and these modifications are best described as programmed loss of cellular control (Phillips et al., 1994).

Results of intensive researches in the field of synthetic seed technology seem promising for propagation of crop plants. However, as described in literature, the major stumbling block in establishing artificial seed production as a viable technology is a lack of understanding of the somatic embryo process and an inability to consistently produce high-quality propagules that can germinate in a soil environment with an acceptably high success rate7. Several aspects of the techniques are still underdeveloped and hinder its commercial application.

10.1. LACK OF STANDARD PROTOCOLS

The synthetic seed technology application in a commercial scale is still hampered by the low efficiency of the protocols and logistic behind it. Till today standard protocol for synthetic preparation is available for a limited number of plant species.

Micropropagation through artificial seeds may be commercially exploited on a large scale, generating millions of plantlets within a few days, and this may become a profitable multibillion rupees industry in near future. This technology would be feasible and even competitive economically with the other seed production methods.

In the near future, the synthetic seed technology could be the key to overcome these problems, e.g., through automated procedures of artificial seed production (Aitken-Christie et al. 1995) followed by autotrophic micropropagation (Jeong et al. 1995). The synthetic seed systems coupled with artificial intelligence and microcomputer systems like the most advanced robots, which can mimic the motions, and functions of living beings (i.e. automated encapsulations). It would tremendously increase the efficiency of encapsulation and production of artificial seeds, and revolutionized the plant propagation method in the years ahead. This technology is gradually moving towards the commercial propagation of high value crops. However, there is a great need for refinement of this technology by tackling of certain technical problems such as the need to produce high quality and high fidelity somatic embryos and to avoid the genetic instability and variability of tissue culture derived plants. The somatic variation can be overcome if the process and regulation of somatic embryogenesis and origin of somaclonal variation are well understood. The understanding about the storage, transport, handling growth habit and harvest index of artificial seeds is essential. The efforts are also needed to increase the output of embryos or plants per gram of callus tissue and conversion frequency of artificial seeds are also needed.

The artificial intelligence (AI) world is quickly growing as it reaches several different industries. You currently will see AI used for many different purposes from agriculture to automotive; with the passage of time you will see even more of it. Agriculture is one of the most important branches of the IT industry. Agriculture is an important industry and is a big part of our economy’s base. In terms of annual sales, the agricultural industry contributes to our economy almost 17%.AI is becoming a technical advancement that boost and protects crop yield, as climate change and populations rise. Here in this chapter basics of Artificial Intelligence have been discussed how it can be related to agriculture.

1. Introduction

Artificial Intelligence has become most prominent in current generation. Every field is utilizing Artificial Intelligence for their better results and which also result in saving a lot of time. As Artificial intelligence is boosting different sectors to increase the productivity and efficiency and its utilization across different sectors like in the field of CIVIL Artificial Intelligence can be used to analyze the structures of buildings, dams etc., and Artificial Intelligence solutions are assisting in so many ways to overcome the traditional challenges in every field.

I. What is Artificial Intelligence

Artificial Intelligence is one of the most prominent field of Computer Science Engineering which attempts to redefine the tasks which are carried out by humans in their daily life with better results and maximum accuracy. Artificial Intelligence is actually meant to ease the work of human where the word artificial states the work done without any human interference and intelligence states the capabilities like human brain to perform tasks. The field artificial Intelligence has given so many definitions from time to time which can be typically stated as

As we have seen how artificial Intelligence can be useful in agriculture but the nest question always come in mind is that from where and how we can start to learn and what are the model of learning an artificial Intelligence. As Artificial Intelligence is a huge part of Computer Science which means we need an language as medium to perform it and we always prefer Python for that which is very dynamic and easy to learn. In this chapter we will learn about python basics so that in further implementation of artificial Intelligence it will be helpful to us.

I. Introduction

Python is a remarkable language that can claim to be easy and strong. You will be amazed to see how simple it is not to focus on the language you are programming, but on solving the problem. Guido van Rossum was the creator of the Python language which is named after the BBC show “Monty Python’s flying Circus”.

Python is formally introduced as:

“Python is an easy to learn, powerful programming language. It has efficient high-level data structures and a simple but effective approach to object-oriented programming. Python’s elegant syntax and dynamic typing, together with its interpreted nature, make it an ideal language for scripting and rapid application development in many areas on most platforms.”

As we know there are different branches of artificial Intelligence and machine Learning of one of them which has become a most advance and widely used branch of artificial Intelligence in every field of this generation whether it may be either electronics, civil, mechanics, Bioengineering, medical or agriculture etc., In this chapter we will study about machine learning and its types with the most common algorithms of machine learning which are being mostly used in different scenarios and their implementation using python language has been discussed in the implementation part of this book. Here we will also see where machine learning can be used.

i. Introduction

Machine learning is a subset of Artificial Intelligence and one of the most important part of artificial Intelligence. It is a field of Computer science which is totally differs from traditional computational approaches. In traditional approaches, a set of instruction is explicitly provided to the algorithm to solve any problem. Whereas in Machine Learning is a domain of an AI which uses a statistical analysis and can enables a system to always learn from its data without any external inputs in order to give an essential output. Due to this machine learning makes computers in advance to make model which provide decision based on input data.

Machine Learning involves a lot of algorithms which usually based on their requirements and functionalities to use where and when. To make a machine learning model we always require the data on which model can be made. Machine learning uses the algorithm which continuously learns from the data available as training data. A machine learning model is generated after the training of algorithm from the data. After the training completes, when trained model is provided with other real-life data then you can get an essential outcome. For example, in figure 3.1 Machine learning Model we use a predictive algorithm and make a model by providing it with some data, and later on we can receive prediction’s based on real data as input.

Deep learning emerged as a serious contender in the field from a 10-year explosive computational development. Deep learning is therefore a special form of machine learning, the algorithms of which are based on the structure and function of the human brain.

Machine learning VS Deep learning

Deep learning is arguably the most efficient form of machine learning. This is so important because they know the best way to solve the problem and how to fix everything. The following is a description of deep learning and machine Learning:

Introduction

Agriculture has played an important role in the global economy in recent years. The further growth of the population is leading to a gradual decrease in the area of the agricultural land and further growing stresses on the farm system. Agricultural food production methods are rising in demand for successful and healthy. Innovative sensoring and motive technologies and advanced information and communications systems and Artificial Intelligence must supplement conventional agricultural management methods to speed up farm productivity growth more accurately, thereby promoting the production of high-quality and high-performance agriculture. Computer vision monitoring systems have become valuable tools in farm operations over the past decades, and their use has increased significantly.

What is Computer Vision?

Computer vision can be characterized as a field of AI for extracting information from digital images. The type of data obtained from an image may be different from recognition, navigational space measurements or applications of increased reality.

The applications of computer vision can also be described. Computer vision builds algorithms that recognize and use the quality of pictures for other purposes.

In this chapter we will look at the Expert system which can guide and assist an individual once it is provided with the enough Knowledge to do so. Here we will discuss about expert system and its architecture with proper steps of making an expert system in any field by several tools which are available to do so. For expert systems the most important is Knowledge Engineering which is the collection of Knowledge from an expert and Engineering it in such a form that a machine could understand it.

Introduction

One of the pioneering fields of AI study is Expert Systems (ES). It is proposed by researchers in the computer science department of Stanford University. An expert system is a computer system which emulates a human expert’s decision-making skills. Expert systems are developed by reasoning through information structures, representing laws, rather than through traditional procedural code, in order to overcome complex problems.

Expert Programs use professional expertise systematically to address human expert problems. An expert is an individual with experience in a specific field. The expert has expertise or special credentials which most people do not learn or care about. An expert can solve or solve problems more effectively than people can solve. Expert System actually emulates all these qualities of expert and solve the problem in a better way and fast.

Today, Expert System (ES) is used in the fields of finance, research, engineering, technology, medicine and many others where there is a well-defined problem area. The fundamental principle is that an expert method should also define the steps to illustrate why a problem can be solved. Unless an individual can justify his reasoning for Expert System.

In this chapter we will talk about the tools which are required to make project in artificial intelligence. All this bellow discussed tools are uses python programming language for making any project in any respective field. We have discussed about how anyone can install and use the tools to built a real time project in artificial Intelligence.

Using Python IDE

To download and install Python visit the official website of Python https://www.python.org/downloads/and choose your version. We have chosen Python version as per requirement all the codes in this book were on python version 3.6 as you can see in the Fig. 7.1.

In this chapter we will cover all the basics libraries and some frameworks of machine learning, deep learning and computer vision. We will discuss all about their use with an example and instruction which will states the commands to install to those libraries for building any project in our system. So let’s begin

What is a Library?

Library is an aggregate of features and methods that allow you to take many actions without getting your own code written.

For instance, the libraries that you have to know are if you work with data, numpy, scipy, pandas, etc. They have very simple data processing functions that save you time for small tricks.

In this chapter of machine learning algorithms we will cover the basic algorithms of machine learning with their implementation using dataset and discuss their pro’s and cons of algorithms for better understanding and how to use them in any project or with any dataset to get an correct result which can useful for research or study.

Key Component

We defined a data set composed of audio, images and binary labels that provided an understanding of how we could train a model from snippets to classifications. This sort of problem is one of many kinds of machine learning problems when we try and predict a specified unknown label given known inputs, provided that a dataset of examples for which labels are known is called supervised learning. Let’s discus what are the major key components we have to get there

Introduction

In agriculture, plant diseases have always been a major problem, as they cause crop quality to decrease, resulting in yield. Plant disease has a range of effects from minor symptoms to serious damages in whole crop areas, which lead to high financial costs and significantly impact the agricultural economy, particularly in developing countries dependent on a single crop or on a few crops.

Various approaches have been developed to detect disease in order to avoid significant losses. Causal agents have been precisely defined by methods developed in molecular biology and immunology. However, for many farmers these techniques are inaccessible and require detailed knowledge of the field or substantial time and energy to be used. According to the United Nations Food and Agriculture Organization, most farms in the world are small and family-owned in developed countries. Such families provide food for a significant proportion of the population of the planet. However, hunger and food insecurity are not rare and there is restricted access to markets and services. For the above reasons, a great deal of work has been undertaken in order to establish methods that are sufficiently reliable and available to most farmers. Where Plant disease is an essential factor contributing to a major decline in plant production quality and quantity. Plant disease identification and classification are a significant role in improving plant production and economic development.

Introduction

The conventional approach of the human person for plant classification is comparing the color and shape of the leaves, but by using Artificial Intelligence analysis can provide quicker and more detailed results by analyzing the morphology of the leaves and provides more details about the leaf properties. Species Recognition has been a very important part of agriculture and there are thousands of species in plants which can be identified by a single person and even if it does so It will take a huge lot of time to do it.

In this chapter we will discuss and built a project related to identify species in flower with an image. In this project we will be using Artificial Intelligence different branches like machine learning, deep learning and computer vision to build such an amazing project. So, let’s begin

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.

The chemical composition and digestibility of feedstuffs influence their nutritive value for animal production. Therefore, assessment of nutritive value of feedstuffs continues to be one of the top priority areas in animal production research. Although traditionally the feedstuffs were evaluated by performance studies and digestion trials, such techniques are expensive and requires more time and labour. Continued search for alternatives to animal feeding experiments have led to the development of feed evaluation systems that are rapid and economical.

In general, there are two kinds of laboratory analyses — Chemical analyses and Biological tests with rumen micro-organisms or purified enzymes. Biological tests give direct estimates of digestibility, but are lengthier and more expensive. Chemical analyses are cheaper and rapid, but do not give direct estimates of nutritive value. The statistical association between the chemical components and quality need to be established to predict the nutritive value.

The estimation of digestibility by feeding trials with sheep or other ruminants is laborious and requires large quantities of feed. Therefore, the chemical composition of feedstuffs are used to predict digestibility by making use of established mathematical relationship between digestibility and composition. However, since the relationship between digestibility and chemical composition varies with the type of feedstuffs, for each group of feedstuffs, mathematical equations will have to be developed by relating chemical composition with in vivo digestibility. In spite of this, the errors associated with this approach of predicting digestibility is large. This has led to the development of techniques that would mimic digestive function of the ruminant in the laboratory.

In vitro methods of feed evaluation using rumen fluid and/or digestive enzymes are the most widely used techniques in ruminant feed evaluation.Although,the technique adopted in different laboratories differ, the principle of the technique is essentially a partially simulated rumen environment in vitro by incubating rumen fluid anaerobically in buffered medium at 39°C for variable period of time. Currently the most commonly used rumen in vitro techniques are,

Although in vitro rumen studies of digestibility determination simulates rumen fermentation to some extent, it is almost impossible to incorporate all dynamics of rumen environment in these techniques. Therefore, the in situ Dacron bag technique was developed as an alternative to the in vitro rumen studies (Ørskov and McDonald, 1979). This technique involves placement of feedstuffs in bags made of indigestible fabrics such as nylon, Dacron or silk, directly in the rumen. The loss of dry matter or of any feed component after specific incubation period are measured gravimetrically to determine the rate and extent of degradation.

This technique is widely used, but the procedure differ among the laboratories.Improved methods utilise bags of specific pore size and control the ratio of sample weight to surface area of the bag.Bags with a large ratio of surface area to sample size minimise the error.Optimum pore size is about 40 microns. Smaller pore size retards the entry of organisms and thus inhibit optimum fermentation while larger ones permit transit of lignified particles.

Although the Dacron bag technique for protein degradation measurement is relatively simple and not too expensive as compared to in vivo measurements, the technique is not easily adaptable in laboratories that are not fully equipped to maintain cannulated animals. Therefore, different alternative approaches such as measurement of protein solubilities in buffer, enzymes and detergent solutions have been tried (Pichard and Van Soest, 1978; Krishnamoorthy et al., 1982, 1983, 1995; Aufrere et al.,1991; Prabhu et al., 1996; Licitra et al., 1996 and 1998). The advantages of these approaches are that, they are simple, rapid, economical and repeatable. However, since the enzymes used are not of rumen microbial origin, degradation obtained from these enzymes may not be a true .reflection of degradation occurring in the rumen. Nevertheless, because of their simplicity and rapidity, in situations where large number of samples are to be compared, in vitro techniques serve as useful tools.

Different enzymatic methods have been proposed as potential alternatives to rumen inoculum studies to determine dry matter digestibility and protein degradation. However, the type of enzyme used and the procedure adopted differ (Marten and Barnes, 1979). The enzyme methods offer distinct advantage under circumstances of the difficulties in maintaining rumen fistulated animals as a source of rumen inoculum. Since, the enzymes used in these techniques are commercially available enzyme preparations, and are not of rumen microbial origin, the digestibility obtained from these techniques are related to in vivo digestibility by means of mathematical equations. Therefore, equations developed to predict energy content using enzyme techniques differ with the type of feedstuff and type of enzymes used in the technique (Table 5.1.) (Martens and Barnes, 1979). The enzyme methods are popular among the feed compounding industries to evaluate raw materials and compound feeds.

The method used by DeBoever et al. (1986) to determine digestibility of mixed feeds is given below.