eChapters

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.