eChapters

Body condition scoring (BCS), although subjective in nature, is the only practical method of evaluation of body energy stores in dairy cows. The method of body condition scoring, originally proposed by Edmonson et al. (1987) and recommended by the NRC (2001) is described here. This method uses a 5 point scale, with BCS of 1 being extremely thin and a score of 5 being extremely fat. This system includes a combination of both visual appraisal and manual palpation on 8 separate body locations. Figure 1 shows the suggested BCS chart based on these locations. The assessor can score the cows using this chart with a fair accuracy, without much previous experience of scoring the cows. The scoring method is recommended for HF and Holstein crossbred cows.

Loss of BCS is expected during early lactation when a cow is mobilizing body fat in support of energy needs for lactation. Typical observed changes in BCS range from 0.5 to 1.0 condition score units during the first 60 days postpartum. A 1-unit decrease in BCS for a cow weighing 650 kg at calving (BCS 4) would provide 698 MCal of ME.That amount of ME is sufficient to support 564 kg of 4 per cent fat corrected milk.

Sampling of Milk for Analysis

A careful and accurate sampling is of utmost importance for the analysis of milk. It is a fundamental principle of sampling that the quantities withdrawn, must be proportional to the quantities contained in the respective containers and uniform throughout in its composition.Thorough mixing of milk must first be ensured either by stirring or by shaking gently. If milk from an individual animal is to be sampled, the sample should be taken from the middle of vessel containing the whole amount of milk. If the sample bottles have been standing for sometime, resulting in the separation of fat in the cream layer or lumps of fat appear, the bottles may be heated in a water bath upto 40°C to melt the fat before mixing. Milk should not be shaken very violently under any circumstance as it may result in an uneven distribution of fat.

Determination of True Protein

Apparatus

Wide mouth stoppered bottle, water bath plus those used for total nitrogen (crude protein) determination.

Reagents

1. Saturated solution of potash alum.
2. Stutzer reagent-Dissolve 40g of CuSO₄ in 2 litres of water in a wide mouth stoppered bottle. Add 10ml of glycerine and sufficient quantity of 30 % NaOH to make the solution alkaline (Litmus paper). Shake the contents and allow to settle. Discard the supernatant liquid. Collect the precipitate on a filter paper and transfer it to glass mortar with the help of spatula. Add 3ml of glycerine and a little water in the mortar and triturate the precipitate with a glass pastle. Transfer the triturated material to a wide mouth stoppered bottle, add 600ml of water, shake well and filter. Repeat the process till the precipitate (filtrate) is free from alkali and sulphate. Collect the precipitate and add 30ml glycerine. Dilute the emulsion to 320ml with water and preserve in a container.

The amount of feed or nutrient intake by an animal which is not excreted in the faeces is considered to be digested by the animals. The depiction of digested feed/nutrient as percentage of intake is known as digestibility coefficient. However, this does not represent the true value of digestibility of a feed or nutrient because during the period of recording some part of digested material is returned to the digestive tract after absorption in the body and excreted in the faeces. In addition to this some secretions of the digestive system and debris of epithelial cells are also eliminated in the faeces. These errors are quite large and highly variable for the nitrogenous constituents and minerals in the feeds. Due to these factors the determined digestibility coefficient is known as apparent digestibility coefficient. For the determination of digestion coefficient, only daily intake of feed or nutrients and faecal void values are recorded.

The availability of nitrogenous nutrients and minerals is more accurately determined by their retention (balance) in the body. Therefore, for the determination of the retention of nitrogen and minerals,metabolism trials are conducted. The routes of elimination of these constituents from the body are faeces, urine, expired air, skin secretions and milk (in case of lactating mammalian species). In a conventional balance study the loss of nutrient (s) through skin, being quantitatively little, is ignored. However, in case of some very specific study, collection of skin secretions and materials like shed hair and scurf are collected. For the collection of skin secretion (sweat) a special absorbant suit is used. In all routine balance studies, on male animals quantity of intake and void in faeces and urine are only taken into account.

Nutritional significance of tannins in feeds and fodders

Tannins are widely distributed in many types of browse plants, tree leaves, forages and feed ingredients. The structure of tannins are not completely well defined but they are polymers of phenolic and aliphatic hydroxy compounds. Although tannins and related polyphenols are considered as protein binders and, protease and cellulolytic inhibitors, they can also exert beneficial effects on rumen fermentation. There are two forms of tannins viz., hydrolysable and condensed. Hydrolysable tannins are considered to have less deleterious effects on protein digestion since these types of tannins might get hydrolysed under acidic gastric condition and release bound proteins. Attempts have been made to utilize this to advantage in protection of proteins from rumen degradation.The condensed tannins are resistant to hydrolytic degradation. The reduction in digestibility due to condensed tannins is not clear whether it is due to specific interference with cellulases or precipitation of proteins.

Certain feed ingredients have been incorporated as a source of tannins, in the diets of ruminants to benefit ruminal fermentation and performance. Inclusion of Bengal gram husk as a source of tannins in the diet of sheep prevented potentially digestible carbohydrate in the rumen, but digested post ruminally (Sreerangaraju et al., 2000).

Infra red (IR) spectroscopy is an analytical technique that serves both qualitativeandquantitativedetectionoforganicconstituents.NearInfraRed spectroscopy (NIRS) is actually a regression technique. Its predictions are based on correlations between spectral information (light absorption) and reference data fed to the spectroscopy. The principle of a NIRS machine is that a feedstuff is illuminated with light of a specific and known frequency (or wavelength) in the near infrared region. The chemical bonds in organic molecules absorb or emit infrared light when their vibrational state changes. In the near IR part of the spectrum, large changes in vibrational state are observed (overtones). In the near IR, ‘overtones’ are observed (larger than expected energy transitions that occur when molecules get ‘over excited’) resulting in broad, less well defined peaks.The characteristics of these peaks make quantification difficult as the areas of the peaks are difficult to determine and,typically,indirect (statistical) technique have to be applied to obtain usable data.Water and the C-H bonds absorb strongly in the near IR region,as do the N-H and O-H bonds,but most other have only a weak peak or none at all. The absorption of light by the feedstuff is then measured as thedifferencebetweentheamountoflightemittedbytheNIRSmachineand the amount of light reflected by the sample. Modern equipment typically uses monochromators, specially constructed mirrors that reflect light in a wavelength-dependent manner, thus allowing exposure of the sample to a single wavelength at a time. This allows samples to be scanned over the entire near IR region (they are also referred to as scanning instruments). However, as the monochromator relies on mechanical positioning of the mirror for its wavelength accuracy, the spectra generated are less reproducible from machine to machine.

Samples those are relatively transparent, such as most gases and liquids, can be assayed easily from the light transmitted (transmittance). For opaque solid samples, however, many problems arise. IR light can only travel through a few microns of opaque material. A few microns, however, can be very difficult to work with, especially when the non-homogeneous nature of most agricultural products is considered. Thus, the major challenge in applying IR spectroscopy to solid samples is sample presentation. For this technique, the sample is put into a cup and illuminated with IR light. This light can have three different fates; it can be reflected off the surface of the sample, it can enter the sample where it may be absorbed if it encounters a bond that matches the light’s frequency or, if it does not encounter such a bond, it can bounce back out of the sample. The light bounced back (or reflected) from the sample is captured and its intensity measured.

The potential of atomic absorption spectrometry/spectrophotometry/ spectroscopy for determination of metallic elements was first realized by Alan Walsh during 1950. The standard equipment termed as atomic absorption spectrometer/ spectrophotometer (AAS) has now gained a reputation in the field of analytical chemistry for determination of very small concentrations of metallic elements and metalloids. As little as 0.01 ppm of many elements can be determined through this tool. Recently, the Zeeman background correction technique has further enhanced the quality of the results obtained from the flame-less graphite furnace atomizer. Combining the very latest in AA technology with the most powerful computing and software available many manufacturers have developed several designs/models of AAS based on flame or flame-less technique.

Cotton (Gossypium) seed meal (CSM), the by-product of cotton seed oil industry, is widely used as an animal feed. However, its utilization as an animal feed is limited due to the presence of gossypol, a polyphenolie binaphthyl aldyde, which is toxic to non-ruminants such as poultry and swine and young ruminants such as calves and lambs. The gossypol (free gossypol, FG) present primarily in the pigment glands of cotton seed, is mostly converted to bound gossypol due to condensation of aldehyde group with free amino groups of proteins to form a schiff base. Formation of schiff base though results in the detoxification of gossypol to some extent, it also lowers the nutritive value by reducing the availability of lysine, the limiting amino acid of cotton seed. Thus, bound gossypol, consequently total gossypol (TG) (free plus bound) content is an important factor of cotton seed meal.

In spite of its high protein (CP, 34-36%) content, castor bean meal as such is not used as livestock feed due to the presence of toxic factors viz., ricin, ricinine and allergen. Of the three, ricin is the most detrimental to animals. In order to detoxify the castor cake, a number of physical and chemical methods are employed. The efficacy of the treatments is assessed, based on the qualitative and quantitative changes in ricin content. Of all the detoxification methods, autoclaving (15 p.s.i., 60 min) and lime (4% w/w) treatments completely destroy the toxin. (Anandan et. al., 2005).

Estimation of Ricin

The raw and detoxified samples can be analysed for the toxin, ricin as per the method of Kabat et al. (1947) with modifications as suggested by Waller and Negi (1958).

Extraction of Ricin

The solvent extracted castor cake sample of 500 g is extracted with 2.5 l of water acidified with HCl to a pH of 3.8 by shaking the contents in a conical flask for 6 h. The contents are allowed to settle and are then filtered trough Whatman filter paper No.1 . The residue is treated with 1.5 l of distilled water, shaken for 3 h and filtered through the same paper. A second treatment with water is given to the residue and filtered again through the same filterpaper.

This filtrate contains all the ricin and portions of ricinine that are soluble in cold, dilute HCI. It is evaporated to a small volume by vacuum distillation below 40 °C. The filtrate is saturated with sodium chloride and centrifuged at 4000 rpm for 20 min. to separate the precipitate containing only ricin.

Neem seed cake (NSC) despite of being rich in protein is not used as animal feed due to the presence of large number of toxic/bitter compounds in it. Studies have shown that NSC can be safely included the ration of animals after water washing / alkali/ urea treatment. These treatment methods appear to remove / reduce or largely inactivate the toxic principles present in NSC. In order to assess the quality/safety of the treated product, quantification of the residual toxic compounds is essential. One such compound is azadirachtin. It has been reported to be biologically most active.

One of the best techniques for the estimation of azadirachtin is by using high performance liquid chromatography (HPLC).

Various products of karanj (Pongamia glabra) are utilized for industrial, health and animal agricultural application in the Indian subcontinent. Despite a rich source of protein (CP 28 —34 %), karanj (honge) cake is found to be slightly bitter in taste and toxic owing to the presence of furanoflavonoid (Karanjin), restricting its safe inclusion in the livestock diets. Though a non polar solvent, soxhlet extraction of karanj seed cake with petroleum ether/ acetone drastically reduces karanjin content. The residual cake left after solvent extraction is proved to be safe as animal feed supplement.

Estimation of karanjin

The samples can be analysed for the toxin, karanjin as per the method of Prabhu et al. (2002).

Extraction of Karanjin from Raw and Processed Cake

Exactly 25 gm of ground and thoroughly mixed sample is weighed and transferred into the thimble and extracted for 12h using 200ml of freshly distilled methanol as solvent. Methanol extract is then cooled and filtered into preweighted round bottom flask using Whatman No.1 filter paper.The excess methanol is distilled off under vacuum. The flask containing the extract is weighed again to obtain the weight of extract by subtracting the empty flask’s weight. Sufficient amount of moisture free extract is transferred into vials for further analysis.

Chromatography is one of the most effective technique for accurate, fast and convenient separation and identification of the components of a mixture. The method was developed by Russian botanist Mikhail Tswett (1906), described by Karl Runge (1855). Martin and Synage (1944) received Noble Prize for developing methodology of partition Chromatography.

Partition chromatography

Partition chromatogaphy is a system comprising of two physically distinguished components - a mobile component or phase and a stationary component or phase. A mixture of substances which are to be separated is placed on the system and separation takes place on the basis of difference in distribution of molecular species in two phases. Relative movement is a result of driving force and retarding effects.

Stationary phase is the sorbent, which may be liquid or paper or gel. If it is liquid then it is held stationary by adsorbing on a solid support or matrix. The mobile phase may be solvent or developer or a gas. Since the two phases are immiscible, the partitioning is defined as the distribution of solute in two immisicible phases which are in contact with one another. When a column is filled with the sorbent and solvent the two phases form a large number of theoretical plates. Each plate contains both the phases. Solutes in solution (mixture of substances to be separated) is layered on the top of the column and when it enters to the column it distributes in the two phases. The ratio of the concentration of a solute in two phases is called its partition coefficient. The resolution of solutes improves as the length of stationary phase increases.

Nutrition plays a pivotal role for the animal research, be it health or production. Research in animal nutrition employs many basic techniques and chromatography is one of them. Chromatographic processes can be defined as separation techniques involving mass transfer between stationary and mobile phases. Various types of chromatography have one application or the other in analysis of food/feed components (Day and Williamson, 2001), quantitation of nutrients as well as the characterization of the effect of nutrients in biological samples like serum/plasma or tissues (Merken and Beecher, 2000).

A. Principle

Basically all chromatographic systems consist of the stationary phase that may be solid,gel,liquid or a solid/liquid mixture that is immobilized and the mobile phase, which may be gas or liquid and it flows over or through the stationary phase. This partition is achieved in one of the following ways:

Adsorption chromatography : It is one of the oldest types of chromatography. It utilizes a mobile liquid or a gaseous phase that is adsorbed onto the surface of a stationary solid phase. The equilibrium between the mobile and the stationary phase accounts the separation of different solutes e.g. adsorption chromatography, hydrophobic interaction chromatography.

Partition chromatography : It is based on a thin film formed on the surface of a solid support by a liquid stationary phase. Solutes equilibrate between the mobile and the stationary liquid (e.g. partition chromatography, reverse phase chromatography, gas-liquid chromatography, etc.).

Principle

Polyethylene glycol (PEG) is a high molecular weight compound which is inert to rumen fermentation. The concentration in the rumen liquor declines with the passage of digesta from rumen to lower G.I. tract and is proportional to time. Protein free filtrate of rumen liquor gives turbidity in the presence of trichloro acetic acid and barium chloride which is a measure of PEG concentration at different post feeding hours.

Principle

Nucleic acids leaving the rumen are mainly of microbial origin which are extensively digested in the small intestine and the purines liberated are absorbed. Only a small amount of absorbed purines are utilized by the host animal, the rest are metabolised forming hypoxanthine, xanthine, uric acid and allantoin. These metabolites are excreted mainly through urine. Therefore, with the understanding of how urinary excretion of purine derivatives (PD) respond to purine absorption i.e. the response curve of PD excretion to purine input into intestines, the microbial purine outflow from the rumen and the microbial N supply to the ruminant animal can be estimated by measuring the purine derivatives excretion through urine.

Introduction

In ruminants,nutritional studies are very complicated unlike non ruminants where it is quite simple. In non-ruminants, analysis of feed taken and faeces voided by the animal gives overall picture of digestion and nutrient availability to the animal. In ruminants, since it has a complex microbial digestive system (rumen) within the animal digestive system, the analysis of the feed taken and faeces voided will not express the whole processes in the GI tract of the animal. To have complete picture, it is necessary to study at the site of microbial digestion (rumen). For such study, samples from the rumen have to be drawn and analyzed for metabolities, microbial enzymes etc.

6.1 Kinetics of Rumen Digestion

The rate and extent of digestion of dry matter or organic matter in the rumen is regarded as the important parameter to predict voluntary feed intake (Khazaal et al., 1993; Blummel,1994). Voluntary feed intake is responsible for more than 50 per cent of the variations in animal response to the diet (Van Soest, 1994) and with low quality unsupplemented roughages, this can be as much as 93% (Blumme1,1994). Therefore, any parameter of feed evaluation that would indicate the voluntary feed intake would be a useful parameter to judge the quality of feedstuff. The rate of digestion of any feed component can be measured by determining extent of digestion at intervals of time. Therefore, the techniques for determining extent of digestion, such as the rumen in vitro techniques (Tilley and Terry, 1963; Goering and Van Soest, 1970) or the in situ Dacron bag technique have been adopted to derive rate of degradation of dry matter of any particular component of the feedstuff. However, unlike in determining the extent of digestion, wherein the incubation is turned off at one particular time (normally 48 hours), measurement of rate of digestion, requires termination of incubation at several points between the start of incubation and until at least 90% of the potentially digestible dry matter is digested (72 or 96 hours). This generates a large number of samples for analysis.

In gas production technique, since gas produced from digestion of substrate is accumulated in the incubations, measurement of gas at different times of incubation would reflect the extent of digestion at different times of incubation. Therefore, unlike with other in vitro / in situ techniques wherein, digestion is determined from the gravimetric disappearance of feed at different times of incubation, gas production technique offers the possibility of using cumulative gas production at different times of incubation to measure the rate of digestion. Thus, a single incubation of feedstuffs is adequate to measure the rate of gas production.

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.

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.

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.

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,

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.

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.

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

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.

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

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 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 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.

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.

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:

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.

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.”

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

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.

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.

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).

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.

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.

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.

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).

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.

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.

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

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.

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.

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.

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.

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

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%).