eChapters

Introduction
Loss of arable land due to population expansion, urbanization and climate change have substantially impacted agricultural production, driving the need to exploit advanced techniques for enhancing sustainable crop production (Bren d’Amour et al. 2017). The yield and productivity of plants are influenced by both their genotype and the growing environment, which govern the performance indices or phenotype (Reynolds et al. 2020). Conventional plant breeding plays a crucial role in ensuring food and nutritional security for the ever-growing global population by harnessing genetic diversity and selecting plants with superior agronomic traits (Singh et al. 2019). However, identifying plants with superior agronomic traits and novel genes (quantitative trait loci or QTLs) through conventional breeding is akin to finding a needle in a haystack (Bohra et al. 2022). Furthermore, the conventional breeding approach is time-consuming and often associated with linkage drag due to extensive inter-crossing/hybridization among elite, wild, and common landraces (Beaver and Osorno 2009). Despite these challenges, classical breeding does develop new plant varieties with improved traits that can withstand climate extremes. Nevertheless, the duration of breeding cycles required to successfully complete the breeding program remains a major bottleneck, taking around 7-9 years to develop an improved variety (Pratap et al 2021).
Efforts have been made to revamp conventional breeding techniques by exploiting molecular markers (Ribaut et al. 2010). These markers effectively capture genetic variations across large populations at an early plant developmental stage. Additionally, the use of molecular markers in plant breeding significantly reduces the time required to screen plant genotypes with desired traits. Otherwise, achieving this task would take 2-3 years (Amiteye et al. 2021). Molecular marker-based techniques, such as marker-assisted selection (MAS), markertrait association (MTA), and genome- wide association studies (GWAS), have become routine tools. They help predict and develop genetic models for plants containing desired traits (Bohra et al. 2020). Furthermore, plant breeders have successfully integrated these molecular markers with genomics, phenomics, transcriptomics, proteomics, metabolomics, and epigenomics approaches (Malik

Agriculture is the support for the sustainable health and economic growth of the country. With the frequent changes in climatic conditions and increase of global population, it is important to come up with new varieties and nutrient rich crops. In this context, underutilized and neglected millets, considered as “nutri-cereals” or “smart foods” due to better adaptation in diverse environmental stresses and tolerance to insect pests and diseases, also a staple crop for hunger-stricken areas. Millet production is limited by the lack of intensive farming and deployment of genetic tools for trait and nutrition improvement. The recent advancement in machine learning (ML) and Artificial Intelligence (AI) technologies are key to depict plant response to environmental perturbation in huge data sets. Therefore, precision agriculture or smart farming is necessary to explore the genetic makeup of the neglected millets. AI has a substantial potential to handle numerous challenges in the formation of knowledge-based farming systems. The next agricultural revolution would emphasize more on farm machinery using AI integrated with the Internet of Things (IoT). In Smart agriculture, deep learning facilitates fast and interesting data analysis and provides support to researchers and farmers by the outcomes generated from the analysis. Here, we emphasize on the advancement of AI and its application to monitor the underutilized and neglected millets quality, yield and disease assessment. This chapter summarizes how AI can contribute to the improvement of sustainable productivity and quality of underutilized and neglected millet.

Genetic improvement creates permanent, heritable gains in milk production, growth, fertility, disease resistance and environmental adaptation. This chapter explains how artificial intelligence complements conventional phenotypic and pedigree-based selection by analysing large genomic and performance datasets. Machine-learning models reveal genotype-phenotype relationships, predict breeding values and support earlier, more reliable selection. Applications include genomic selection, precision breeding, reproductive technologies, bioinformatics and digital twins. AI can reduce generation intervals and improve the efficiency of breeding programmes, but its success depends on data quality, representative populations and interpretable models. Ethical concerns, genetic diversity and long-term sustainability must also be considered when designing AI-supported breeding systems.

Genetic improvement creates permanent, heritable gains in milk production, growth, fertility, disease resistance and environmental adaptation. This chapter explains how artificial intelligence complements conventional phenotypic and pedigree-based selection by analysing large genomic and performance datasets. Machine-learning models reveal genotype-phenotype relationships, predict breeding values and support earlier, more reliable selection. Applications include genomic selection, precision breeding, reproductive technologies, bioinformatics and digital twins. AI can reduce generation intervals and improve the efficiency of breeding programmes, but its success depends on data quality, representative populations and interpretable models. Ethical concerns, genetic diversity and long-term sustainability must also be considered when designing AI-supported breeding systems.

Currently, the milk production of country is 221.06 million tonnes (2021 22) and the target is 300 million tonnes by 2023-24. The livestock sector contributed nearly 5.21% of total gross value added (GVA) and 28.36% of the agriculture and allied sectors GVA. Thus, there is a huge gap between demand and supply, therefore with the requirements of better yield from the dairy animals, we need more better and advanced options. Artificial intelligence (AI) is one of such options which can be exploited in dairy industry. Artificial Intelligence can be a game-changer for the dairy farming in India and it can emerge as a tool that empowers farmers in monitoring, forecasting as well as optimizing the farm animal growth. It can predominantly change the scenario of dairy farmers by maintaining the health, physiological and physical conditions of dairy-cows. This knowledge-based technology has a huge potential and could confront the loopholes in dairy farming and thus indirectly can strengthen the dairy industry. In dairy farming AI has multiple applications like monitoring the activities of the dairy-cows, boosting the milk production and farm productivity, detection of mastitis in dairy-cows and developing the smart cow houses powered by image analysis.

AI enables the dairy farmers to determine whether the cow is ill, ready to breed or has become less productive. The AI also sends alerts to the farmer about the change in the cow’s behavior allowing human intervention where needed. Regular 24x7 monitoring of dairy animals is the key for successful dairy farming, however, traditional methods of monitoring relied on visual observation, but it was not very efficient due to the lack of time and human resources. Without AI, it would be almost impossible for the farmer to keep a watchful eye on every cow in the herd. Artificial Intelligence components of the dairy automation system process the collected data to provide insights on the heat stress, change in feeding efficiency and the estrus of the cow. Diseases like sub-clinical mastitis, one of the more common diseases in the dairy industry, cost the Indian dairy industry one billion dollars annually. Eventually, it provides new hope and open prospects for the overall quality and progress in the dairy industry through a profitable business approach in dairy farming

What is Artificial Intelligence

‘Artificial’ is taken to mean a property of a human-made machine. ‘Intelligence’ will be that when something is able to perform a new action or change its state to achieve the optimal response it could perform given external inputs. This term is coined by- John McCarthy (American computer and cognitive scientist) during 1956 So, AI is basically a machine able to mimic human intelligence by having the ability to predict, classify, learn, plan, reason and perceive.

AI is an emerging technology in the field of agriculture. AI-based equipment and machines, has taken today’s agriculture system to a different level. This technology has enhanced crop production and improved real-time monitoring, harvesting, processing and marketing . The latest technologies of automated systems using agricultural robots and drones have made a tremendous contribution in the agro-based sector. Various hi-tech computer-based systems are designed to determine various important parameters like weed detection, yield detection and crop quality and many other techniques.

Introduction 
In the past, literacy in reading, writing and mathematics was considered the basis of establishment in society. With this knowledge, anyone could run all the businesses of his professional life efficiently because no technical skills other than typewriter and general computer knowledge were required. In present set up, this general knowledge is no longer enough to perform any task smoothly today. In this age of science and technology, the old concept of literacy is no longer applicable. Three other types of literacy or skills have now been added to the past concept of literacy: information literacy, technological literacy and human literacy. In this age of technology, people cannot keep themselves functional in this digitized world by practical knowledge of analog equipment alone. In present days, people live and work on the constant flow of big data, connections, and instant information flowing from every click and touch of a device. Technology is not static. Evernew technological advancement brings tides of data before people, therefore, they need data literacy to read, analyze and use them. Technological literacy gives them grounding in coding and engineering principles, so they know how their machines tick. Ultimately, human literacy teaches them the humanities, communication and design, allowing them to work in human environments. Artificial intelligence (AI) is now a fact of everyday life in our modern hightech societies. There are many definitions of AI and each of those definitions has been revised over time. Presently, most definitions state that AI solves complex cognitive problems associated with human intelligence or that AI helps as many people as possible through smartphones or healthcare, or even that AI recognizes problems and creates solutions for the benefit of technology, people, and society. However, the core concept of AI has continuously been to create machines that were capable of thinking like humans. It took about 200,000 years for human intelligence to evolve from natural to artificial and only 10 years to cut ties with the Earth and move to the clouds. During the creation of AI, mankind learned a lot about what it means to be human, how human intelligence is structured, and how humans learn and acquire expertise. With more predictive power, more efficiency and the ability to deliver better results than humans, AI is rapidly moving aside from humans into the world of expertise. However, it is impossible to imagine that someone else will replace Homo sapiens at this moment, but its probability cannot be ruled out in the days to come. The day, when mankind, in the course of time, will immerse in the world of knowledge, robotics becomes an essential part of the human body and existence, the existence of mankind is sure to be threatened. In October 2017, Saudi Arabia granted citizenship to humanoid Sofia. Perhaps this is a new breed of human being brought about by technology in the 21st century. Just as the message of the first driverless car aroused a lot of curiosity among mankind and now that curiosity has disappeared completely, it is likely that the arrival of many Sophia’s will make the environment very normal in the coming days. So here comes AI – Artificial Intelligence, the driving force behind Sophia or Driverless cars. The very simple definition of artificial intelligence is -the intelligence displayed by machines. Today, in this age of science and technology, we can say that anything imaginable by humans can be done with the help of machines. Sundar Pichai, CEO Google and Alphabet in his article “The promise of AI for India and the World” opined that every technology shift is an opportunity to advance scientific discovery, accelerate human progress and improve lives. AI will do this on an unprecedented scale and India is uniquely positioned to play a leading role.

Artificial Intelligence (AI) technology is increasingly used in our everyday lives. It is used in a variety of industries from gaming, journalism/media, to finance, as well as in the research fields from water resources to robotics. In this chapter concept and applications of AI are discussed in brief.

6.1 Historical Background

After World War II, a number of people worked independently on intelligent machines. The English mathematician Alan Turing was the first who explained the concept of intelligent machines through a lecture in 1947. He stated that intelligent machines could be developed by programming computers rather than by building machines. Since late 1950 many researchers are working on intelligent machines, mostly on programming computers

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

Artificial Intelligence (AI) is technology which can store, retrieve, manipulate, manufacture, transmit or receive, analyze any digital information and can apply machine vision for increasing efficiency and workability of the system. The AI permits systematically scope of ‘3S’ i.e. Safety, Security and Surveillance, which is also known as manmade intelligence. In fact, it needs more reliable and accurate data or information for processing and correction for predicting performance of any system and devices.

Artificial intelligence is founded on the idea that human intelligence may be described in such a way that a computer can simply imitate it and carry out tasks ranging from simple to sophisticated. Artificial intelligence is assisting several agricultural sectors in increasing output and efficiency. In every industry, AI technologies are assisting in overcoming traditional hurdles.

AI intervention in agriculture is assisting farmers in increasing their farming efficiency while reducing harmful environmental impacts. The agriculture business has embraced AI with open arms in order to improve overall results. The way food is produced is changing as a result of artificial intelligence. Incorporating AI technology into agriculture aids in the control and management of any unwelcome natural occurrence.

1.1 Introduction

Since the beginning of humankind, we have thrived as a species due to our unique capabilities. These skills have developed and progressed over the centuries to the point that, in recent decades, we have been able to build machines that can imitate our intelligence, as far as we comprehend it. A novel type of world may lie ahead of us.

Artificial intelligence is the future, and the future is here." - Dave Waters

Artificial intelligence (AI) refers to the replication of human intelligence in computers, which are programmed to think and behave in a manner similar to humans. Literacy, logic, problem-solving, perception, and linguistic appreciation are all manifestations of cognitive abilities. Artificial Intelligence refers to the development of computer systems, computer-controlled robots, or software that are designed to exhibit intelligent behavior similar to that of the human mind. The term artificial intelligence is composed of the word "Artificial," which denotes something created by humans rather than existing naturally, and "Intelligence," which refers to the capacity to gain and utilize knowledge and skills.AI is achieved by the analysis of patterns in the human brain and the evaluation of cognitive processes. The culmination of these studies leads to the creation of sophisticated software and systems with advanced intelligence. The term AI was coined by McCarthy et al. 2006 while working on Dartmouth summer research project in which they proposed that machines have the capability to emulate "every facet of learning or any other characteristic of intelligence."

Abstract

Natural sunlight is the cheapest source available, but for horticulture it is not always attainable in sufficient quantities. Therefore, the use of artificial light has become very common in order to increase production and quality. Under artificial lighting technologies, LED lighting is one of the technology that can help in producing crops and flowers in a more effective and sustainable way around the world. The benefits of LEDs are that the spectrum and intensity can be selected and adjusted, the energy efficiency is higher than most conventional light sources and their small size, durability, long lifetime, cool emitting temperature, and the option to select specific wavelengths for a targeted plant response make LEDs more suitable for plant-based uses than many other light sources. These advantages, coupled with new developments in wavelength availability, light output, and energy conversion efficiency, can bring about a revolution in horticultural lighting. Light quality plays a major role in the appearance and productivity of ornamental and food specialty crop species. Far-red light, for example, is important for stimulating flowering of long-day plants (Deitzer et al., 1979; Downs, 1956) as well as for promoting internode elongation (Morgan and Smith, 1979). Blue light is important for phototropism (Blaauw and Blaauw-Jansen, 1970), for stomatal opening (Schwartz and Zeiger, 1984), and for inhibiting seedling growth on emergence of seedlings from a growth medium (Downs, 1956). The interactions are complex and continue to be unraveled at the molecular level (Devlin et al., 2007), but much of our understanding of these responses comes from studies with narrow-waveband lighting sources, in which LEDs provide obvious advantages. Thus, one potential role of LEDs in horticulture could be to enhance desired characteristics for specific crops. The artificial LED lightings increase the possibility of year-round production of ornamental plants, which will subsequently increase the income of growers related to ornamental industry. Light-emitting diodes pave the way for a better understanding of the interaction between light and pests, diseases, natural enemies and plants, as they make possible the use of very narrow wavelengths. LEDs as an option for reducing the use of growth retardants and obtaining better product quality in ornamental pot plants. Production of secondary metabolites increase with higher blue light ratio. Secondary metabolites production are increased and act against Reactive Oxygen Species (ROS) and pathogens, precondition the plant for environmental changes so they can cope with stress more efficiently.

12.1. Introduction

Artificial Neural Networks (ANNs) are computational models inspired by the way biological neural networks in the human brain process information. They consist of interconnected nodes or “neurons” arranged in layers: an input layer, one or more hidden layers, and an output layer. Each neuron in the network processes data and passes the results to the next layer. ANNs are designed to recognize patterns, learn from data, and make predictions or classifications based on that learning.

In an ANN, the connections between neurons are weighted, meaning each connection has a strength or value that determines the influence of one neuron on another. Through a process called training, the network adjusts these weights to minimize errors in its predictions. This is typically done using techniques like backpropagation, where the error is propagated backward through the network to update the weights (Abiodunet al., 2018).

ANNs are highly versatile and have been applied across various domains, including image and speech recognition, natural language processing, and decision-making systems. Their ability to model complex relationships in large datasets makes them a crucial component in the field of machine learning and artificial intelligence (Ahmed et al., 2023).

INTRODUCTION

There is growing concern in both the scientific community and the general public over the possibility of important climatic changes due to increases in relatively active gases in the atmosphere, a phenomenon commonly referred to as “climate change.” In the past century, the activities of a rapidly expanding and industrializing human population have added significant quantities of heat retaining gases to the atmosphere. For example, the Intergovernmental Panel on Climate Change (IPCC) estimates that by 2100 increases in greenhouse gases will likely lead to an increase in temperature of between I0⁰ C and 3.50⁰ C (Houghton et al., 1996) and an increase in sea level of between 13 and 94 cm (Warrick et al., 1996). The IPCC also reports that warmer temperatures will most likely intensify the hydrological cycle, leading to an increase in the precipitation intensity and number of storm events (Houghton et al., 1996). Our results of this study also agree with it.

INTRODUCTION

Artificial Neural Network (ANN) is a branch of the field in computer science known as “Artificial Intelligence (AI)”. The field goes by many names, such as connectionism, parallel distributed processing, neuro-computing, machine learning, and artificial neural networks. A tremendous growth in interest of this computational mechanism has occurred since Rumelhart(1986) rediscovered a mathematically rigorous theoretical framework neural network. Traditional (physically-based) modeling of physical processes tries to explain the underlying processes. On the contrary, the data-driven models are based on a limited knowledge of the modeling process and rely on the data describing input and output characteristics. Data-driven models are based on pure relationships between input and output data and not the physical principle linking input and output. They offer real advantages over conventional methods, including the ability to handle large amounts of dynamic, non-linear or noisy data and such tools can be especially useful when the underlying relationship are not fully understood. Data-driven modeling uses results from such overlapping fields as data mining, artificial neural networks, rule-based type approaches such as expert systems, fuzzy logic concepts, and machine learning systems.

Artificial neural networks are suited to problems where relationships between the variables to be modeled are not well understood. Mathematically, an ANN may be treated as a universal approximator. The ability to learn and generalize ‘Knowledge’ from sufficient data pairs make it possible for ANN to solve large-scale complex problems such as non-linear modeling, pattern recognition, classification, control and others – all of which find application in water resources management today. Since the early nineties, ANNs have been successfully used in water management area such as rainfall-runoff modeling, stream flow prediction, groundwater modeling, water quality, hydrologic time series, and reservoir operation.

The artificial neural network (ANN), a data driven model, is inherently suited to the problems that are mathematically difficult to describe. An ANN is a flexible mathematical structure that is capable of identifying complex nonlinear relationships between input and output data sets, where explicit knowledge of the internal processes is not available. In this chapter general structure of ANN is described and also explained briefly with a case study.

7.1 What is Artificial Neural Network?

An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous system, such as brain process the information. An ANN is an adaptive (Adaptive means that the system parameters are changed during operation i.e. training phase), most often nonlinear system that learns to perform a function (an input/output map) from data. In more practical term, neural networks are non-linear statistical data modeling tools. They can be used to model complex relationships between inputs and outputs or to find out patterns in the data.

Introduction

Global diversity in domestic animals and aquatic organisms is considered to be under threat worldwide. Worldwide 11 % birds, 25 % mammals and 34 % of fish species are threatened (Lovell-Badge, 2001; Vemuganti and Balasubramanian, 2002) and aquatic ecosystem is becoming more vulnerable to disasters due to run off from agriculture and industrial sources, oil spills or sudden environmental changes leading to total elimination of stocks from different ecosystems. The multifarious human activities in open waters have threatened the fish diversity all round the world. Fish worldwide are in crisis. The effective population sizes in fish hatcheries are getting smaller and smaller that causes loss to farmers worldwide. The World Resources Institute has reported that nearly 70 percent of the world’s marine fish stocks are over fished or are being fished at their biological limit. Apart from fish, across all live stock species there is a shrinking pool of genetic diversity. For instance, the effective population size for all dairy cattle breeds is less than 60 animals. To preserve the gene pool of unique populations by protecting them from extinction, loss of genetic variability (inbreeding) and dilution due to inter breeding with unrelated populations (hybridization) gene banks or germplasm repository can play a vital role. Germplasm repositories in fisheries could be used for: 1) restoration of threatened species, 2) brood stock improvement through genetic modification of a targeted population, and 4) information and technology development including gene pool database management.

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.

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%.

Ascorbic acid (vitamin C) is present in almost all fresh fruits and vegetables in varying quantities ranging from 0.02 to 1.0 mg per g fresh weight. It is most abundantly found in bittergourd and berries.

The method for estimation of ascorbic acid is given here below as described by Albrecht (1993). It is an easy and simple method based on titration technique.

Fruit, vegetables and their products are important sources of ascorbic acid. Aonla, guava and citrus fruits are good sources of Vitamin C (Ascorbic acid).Ascorbic acid is sensitive to heat, light and oxygen but is stable in acidic
 media. The methods for estimation of Ascorbic acid are based on the reduction of 2,6 -dichlorophenol - indophenol dye by ascorbic acid and those based on the reaction of dehydroascorbic acid with 2,4- dinitrophenylhydrazine. However,the 2,6 -dichlorophenol - indophenol dye reduction method is most commonly used in the estimation of ascorbic acid in fresh fruit and vegetables and their processed products.
2,6 dichlorophenol - indophenol visual titration method
The dye, which is blue in an alkaline solution and red in an acid solution, isreduced by ascorbic acid to a colorless form. The reduction is quantitative and specific for ascorbic acid in solutions in the pH range of 1-3.5.
Reagents
i. 3% Metaphosphoric acid (HPO3) solution:Dissolve 3g sticks/ pelletsofMetaphosphoric acidin a volumetric flask and make volume to 1oo ml with glassdistilled water.

Explant

The tissue obtained from plant to be cultured is known as Explant. It may include the parts of shoots, leaves, stems, roots, flowers and callus.

Maintenance of Aseptic Environment

All culture vessels, media and instruments used in handling tissues as well as the explants must be sterilized. The importance is to keep the air surface and floor free of dust. All operations are carried out in laminar air-flow, a sterile cabinet.In general, the methods of elimination of these sources of infection can be grouped under different categories of sterilization procedures: 

7.1. Introduction

In the modem lifestyle, people are extremely busy in their work and lack the time to cook their own foods. They mainly depend upon processed and packaged food for their nutritional intake. So it becomes essential that the packaged products should be safe, hygienic and shelf stable. To preserve the food for long duration, thermal processing is most suitable method. Aseptic packaging is a form of thermal food processing. Aseptic packaging can be defined as the filling of sterile containers with a commercially sterile product under sterile conditions and then sealing the containers so that reinfection is prevented; that is, they are hermetically sealed. 

Aseptic packaging is a specialized and advanced method used to extend the shelf life of perishable foods without the need for refrigeration. This technique involves sterilizing both the food product and the packaging material separately and then filling and sealing the product in a sterile environment. Aseptic packaging has transformed the food industry, allowing products such as dairy, juices, soups, and sauces to remain fresh and safe at room temperature for extended periods. The ability to maintain product quality, nutritional value, and safety while reducing reliance on cold storage has made aseptic packaging a preferred choice in both food manufacturing and distribution. The process of aseptic packaging is both complex and precise, as it requires thorough sterilization of every component to ensure food safety. Packaging materials, typically a combination of paper, plastic, and aluminum, are carefully selected to withstand high temperatures while providing an effective barrier against contamination. This chapter explores the principles of aseptic packaging, its significance in food preservation, and the advanced technologies and materials used to achieve and maintain sterility in packaged foods.

Vegetative propagation enables horticulturists to multiply plants while retaining desirable genetic characteristics of selected mother plants. This chapter covers important methods including cuttings, layering, budding, grafting, division, and specialized vegetative structures. The physiological basis of rooting, wound healing, callus formation, compatibility, and environmental requirements is discussed. Grafting and budding are particularly important for combining desirable scion characteristics with suitable rootstock traits such as vigor, disease resistance, stress tolerance, and adaptation. Proper sanitation and selection of healthy propagation material are emphasized to minimize pathogen transmission. Modern techniques such as rooting hormones, mist chambers, protected propagation, and clonal multiplication further improve the efficiency and uniformity of vegetative propagation.

The inorganic residue remaining after the destruction/burning of organic matter in foodstuffs is called ash. Ash contents in food commodities roughly represent the mineral contents, however, some minerals are likely to be lost due to volatilization or some interaction between constituents during ashing. Estimation of ash content is also used as a measure to judge the quality of fruit products. The presence of high ash content or low alkalinity of the suggests the presence of adulterants in the sample. Similarly, the presence of a high amount of acid-insoluble ash indicates the presence of sand and other silicious matter in the food products.
Principle
Organic matter is burnt off at as low a temperature as possible. Heating is done in stages, first to char the product thoroughly and finally to ash it at 550ºC in a muffle furnace. The inorganic material left after burning organic matter is cooled and weighed.
Apparatus
* Silica Crucibles
* Muffle furnace
* Desiccator
* Balance

Botany: The ash gourd(Benincasa hispida) belonging to the family cucurbitaceae is an annual creeping vine that can either climb structures or be allowed to spread out on the ground. This plant features large green leaves and thick stems covered with coarse hairs. The showy, golden yellow blooms appear early in the summer, and female flowers give way to round or spherical fruits. Young ash gourds are covered with a soft down that disappears with maturity. Fully matured gourds have a white, waxy coating covering the surface.

Field preparation:The field selected for seed production must not have been sown with ashguard in the previous season. This is done to avoid volunteer plants that cause admixture.After proper ploughing, at a spacing of 2.5 x 2 m distance take pits having 45 cm length, width and height. Ten days after that, apply 10 kg FYM and urea 30 g. Super phosphate 72 g and potash 19 g per pit. Then mix the above nutrients with soil and fill the pits and level them.

Soil and climate:Soil with neutral pH must be selected. Loam or clay loam soils are best suited. Higher organic matter will lead to production of vigorous seed.Seed is very sensitive to whether. Hence selecting the right season is necessary. Though ashgourd can grow through out the year, seed crop should be grown such that the seed matures in cool dry climate. This will facilitate proper ripening of fruits and reduce the pest and disease infection. Seasons are selected with this idea in mind. In

I. FILL UPS

1. Ashgourd is a _____ crop.
2. Stigma receptivity is for _____.
3. Ashgourd is a _____ pollinated crop.
4. Ashgourd fruit is a _____.
5. The isolation distance for ashgourd certified seed production is_____.

There has been a surge of data in every field of research due to advancements in information technology. Data collection and storage has become much easier and cheaper in recent years. This voluminous data needs to be systematically stored for quick retrieval, process and to find useful patterns. Data mining techniques help in deriving useful patterns from data. These techniques which are also called as machine learning techniques majorly categorized as classification, clustering and association rule mining methods. Classification is a supervised learning method which needs labeled dataset for model building and prediction purposes. Clustering and association rule mining methods do not require labeled dataset and hence they are referred as unsupervised learning methods.

Clustering aims at grouping the objects based on a set of measured variables where similar objects are assigned to one group. For example, let us say, if we have collected information on 50 fish pond parameters (50 variables) and we wish to find the groups in terms of pond management (like well managed to poorly managed), with cluster analysis, we can group the ponds based on pond variables. In the process of clustering, each pond is assigned to one of the groups made. The question is how many natural clusters present in the data as in many cases we would not be getting perfect clusters of disjoint sets. We can achieve better accuracies in cluster formation only by trial and error. Good clustering criteria are to see that there is minimum with-in cluster variations and maximum between cluster variations.

Cluster analysis has several applications in fisheries research. It could be used to group fish stocks from different regions, categorise ponds based on their technical efficiencies, identify the farm clusters with high disease risk, group farmers based on their preferences, agro zoning based on climatic variables and culture practices. Cluster analysis is also applied in fish bioinformatics to group the similar sequences.

Botany

Ashwagandha (Withania somnifera), also known as Indian ginseng belonging to the family Solanaceae, is an important ancient plant, the roots of which have been employed in Indian traditional systems of medicine, Ayurveda and Unani to cure asthma, fever, chest complaints. It is an erect branching undershrub reaching about 1.50 m in height. It grows in dry and sub-tropical regions. Being hardy and drought tolerant species with its enormous biocompounds, its usage is forever regarded and continuous to enjoy the monopoly in many parts of India, particularly in Madhya Pradesh. It grows in dry parts in sub-tropical regions. Rajasthan, Punjab, Haryana, Uttar Pradesh, Gujarat, Maharashtra and Madhya Pradesh are the major Ashwagandha producing states of the country. Roots are fleshy, tapering, whitish brown. Leaves are ovate and flowers are greenish. The mature fruits are orange-red berries.

Varieties:Poshita and Rakshita are high yielding varieties released by CSIRCIMAP, Lucknow. Jawahar 20 is cultivated in Madhya Pradesh. WSR is another variety released by CSIR-Regional Research Laboratory, Jammu. Nagori is a local variety with starchy roots

4.1. Origin and distribution

• It is called as Indian ginseng.

• Native of Africa, India and Mediterranean region.

• Wild in grazing grounds in Mandsaur and forest lands in Bastar district of Madhya Pradesh, all over foothills of Punjab and Himachal Pradesh and Western Uttar Pradesh, in Himalayas.

• Also wild in Mediterranean regions in North Africa.

4.2. Economic importance

Improved Varieties of Asparagus

(1) Perfection

Source : Developed at IARI, New Delhi

Maturity : Early

Spears : Large, green, succulent, heavy cropper and uniform in maturity

Production Technology of Asparagus

Climate

Asparagus is temperate season vegetable and bears excellent crop between a temperature range of 15-24ºC for most of the growing season. The ideal day temperature is 25-30^oC and night temperature 15-20^oC. It can tolerate frost up to some extent but continuous freezing is harmful for its growth. Spear initiation occurs at soil temperature of more than 10ºC. Spear elongation is faster at high air temperature. Root and shoot development is reduced below 13ºC and above 29ºC. Due to warm weather, spear tips open prematurely and quality of spears is reduced. Elevation of more than 1000m is good for high yield.

Asparagus is an herbaceous perennial that is widely grown in the United States, Germany, Spain, and France, with India contributing only a little amount. For many years, asparagus has been grown. This was a favourite crop of the Ancient Greeks and Romans. It belongs to the Liliaceae family and is thought to have originated in the eastern Mediterranean and further east in the Caucasus Mountains. It’s a monocotyledonous perennial that’s farmed for its edible stems (spears).

Introduction

Mushrooms are very nutritious products that can be generated from lignocellulosic waste materials; and are in rich in crude fibrend protein. In fact, mushrooms also contain low fat, low calories and good vitamins. In addition, many mushrooms possess multi-functional medicinal properties. Edible mushrooms once called the “food of the gods” and still treated as a garnish or delicacy can be taken regularly as part of the human diet or be treated as healthy food or as functional food. The extractable products from medicinal mushrooms, designed to supplement the human diet not as regular food, but as the enhancement of health and fitness, can be classified into the category of dietary supplements/mushroom nutriceuticals (Chang and Buswell, 1996).

According to Indian Philosophy (Bhat, 2012) the physical body is called Annamaya Kosha.A body, when well nourished grows well by absorbing the energies from the food consumed. Fasting is another aspect of traditional food system. It is meant to purify the mind and the body.

Introduction

This is a disease mainly of the respiratory system affecting domestic poultry, wild birds and zoo birds. Systemic aspergillosis affecting visceral organs and other tissues of the bird’s body is also reported. Reports are also available on eye and brain infections.

Assam is surrounded by Bhutan and Arunachal Pradesh on the North; Nagaland and Manipur on the East; Meghalaya and Mizoram on the South and Bangladesh, Tripura and West Bengal on the West. The total land area of Assam is 78,523 sq. km and it accounts for 2.4% of total geographical area of the country. Demographically, Assam is the most populous state in the North East India. Economically, out of eight North East states, Assam is the most developed one, but compared to other states like Maharastra, Tamilnadu, Gujrat, it lags far behind in almost all economic spheres.

Antibiotics are produced by some true bacteria, actinomycetes and fungi that destroy or inhibit the growth of other microorganisms. Some antibiotics are synthesized or modified in the laboratory; however, their origins are always in living cells.

9.1 Assay of Antibiotics

Soil serves as the major repository of microorganisms some of which produces antibiotics. Four groups of soil microorganisms viz; Streptomyces, Bacillus, Penicillium and Cephalosporium represent three microbial types, namely, actinomycetes, true bacteria and molds respectively.

Following procedure may be applied to isolate antibiotic-producing microorganisms from soil:

Isolation and Purification of Secondary Metabolites

Isolation

The act of isolating something; setting something apart from others.

Purification

The process of making pure, free from anything that debases, pollutes, or contaminates, the process of removing impurities.

Introduction

The characterization of the potential health impact of engineered materials produced through nanotechnology is an emerging issue of considerable discussion and debate. The current focus on nanotoxicity fails to assess the risk in different local environments and populations. People in developing countries may be more prone to adverse effects of nanoparticles because of underlying health conditions and malnutrition. Moreover, genetic susceptibility to toxic effects varies in diverse ethnic groups and geographical areas. The scientific community needs to identify these information gaps before developing regulations and standard methodologies for nanotoxicity assessment.

Need for systematic mapping and hazard zonation is emphasized in this chapter. Though prevention of natural disaster is not possible, its destruction could be minimized through implementation of effective hazard management system. With this motive, the coastal region between Tangapatnam and Ovari, Tamilnadu State has been classified into various categories of vulnerability based on its response to the 26th December 2004 tsunami. Based on the extent of seawater inundation and coastal geomorphic features, we have assessed the tsunami impact and estimated the behavior of the beaches in case of similar havoc in future. This study had shown that the maximum seawater inundation recorded in the study area is 750m in Colachel and the minimum is 100m in Kadiapatanam, Mandakadu and Vaniakudy areas. Chinnamuttom, Kanyakumari, Manakudy, Pallam and Colachel areas are under high risk.

INTRODUCTION

On 26^th December 2004, tectonic disturbances happened near Sumatra Island with an intensity of 9.3 in the Richter scale wrecked havoc on southwest part of India. In addition to inflicting life and infrastructure loss, the resultant tsunami event had also eroded severely the coastal region and modified topography of beach and shallow coastal regions. It did raise the concern of scientists and emergency planners about the impact of larger earthquake and consequent tsunami should there be any recurrence of such event. Despite becoming aware of tsunami hazard, there has been confusion about areas at risk and areas of safety. This chapter is an attempt to assess the vulnerability of part of Tamilnadu coast based on its geomorphological conditions and the impact of recent tsunami. The hazard maps have been generated and are intended for educational purposes, to improve awareness of tsunami hazards and to encourage responsible emergency planning efforts by illustrating the range of possible tsunami events.

Abstract

Higher temperature and altered rainfall patterns accompanied by extreme weather events are being considered indications of climate change. These factors impact vegetation growth in natural forest, open scrub and in agriculture land. Vegetation index based on satellite data was used to analyse agricultural vulnerability in India to understand impact of rainfall variability. Time-series Normalized Difference Vegetation Index (NDVI) data products obtained from AVHRR (1982 to 2006) and MODIS (2000-2011) were analyzed to assess spatial and temporal variability. Coefficient of variation of maximum NDVI was assessed for the study period and results were corroborated with Standard Precipitation Index (SPI) instead of actual rainfall. Resolution of AVHRR time-series data facilitated study at regional-scale while MODIS datasets with 250m pixel resolution helped in identifying vulnerability at district-level. Study indicated that over 241 Mha areas in the country may not be vulnerable to rainfall variability induced climate change while over 81.3 Mha in arid, semi-arid and dry sub- humid regions may be vulnerable. Study indicated that over 12.1 and 1.81 Mha of Kharif cropland would be mildly and severally vulnerable while 6.86 and 0.5 Mha of Rabi cropland may be adversely affected in a similar manner. Study indicated that length of cropping season was decreasing due to delay in start of season. The paper also presents analysis of variations in SPI and resultant NDVI in typical drought and flood years in the country.

Keywords: MODIS, AVHRR, NDVI, Rainf ed Agricul ture, Vulnerability, Climate Change

Abstract

The paper deals with taxonomy, ethnobotany and threat status of lesser known liana species Hodgsonia heteroclita (Roxb.) Hook.f. & Thomson. The local people use in terms of foods and rites and its role in ecosystem have been reported. Local people used the seeds of liana as food because of high nutritive value. Threat assessment has been following IUCN version 3.1 to evaluate the threat status of the species in Sikkim Himalaya. Out of the five criteria, data on two criteria could be collected and was used to evaluate the threat category of the species. The criteria used for threat assessment were (a) reduction in population and (b) geographical range in the form of either B1 (extent of occurrence) or B2 (area of occupancy) or both. Considering the population status and geographical range of the species, it has been classified as “Least Concern” (A1acd; B2ab(ii,iv)).

Introduction 
SDG 3 emphasizes good health and well-being for people globally. In this chapter, we will focuses on the health and well-being of the Indigenous Khasi Tribe living in Meghalaya, India. The Khasi Tribe comprises 7 sub-tribes, commonly known as ‘Seven Huts’ or ‘HynniewTrep, HyniewSkum’ in the Khasi language. The 7 sub tribes are Khynriam, Pnar, Bhoi, War, Maram, Lyngngam and Dikko. Although the culture, tradition, and beliefs are shared among all subtribes, earning them the common name Khasi Tribe, there are slight differences in terms of topography. Some sub-tribes live in sloping or steep mountainous areas, engaging primarily in forestry and horticulture, while others reside in plain areas, where they cultivate agricultural crops. As an indigenous group, the Khasi community follows certain beliefs and practices related to health and well-being. The Khasi tribe is a matrilineal society where women hold great responsibility at home and in the community. Some researchers have acknowledged that the Khasi tribe is one of the tribes in India with a better status in terms of literacy rates and nutritional status. (D Agrahar- Murugkar, 2004). Approximately 83% of the Khasi tribe are agrarian. The main food items that boost their immune system and make them strong and healthy include rice, dal, potato, tomato, bamboo shoot, pumpkin, green vegetables, citrus fruits, and meat (beef, pork, fish, eggs, and dry fish), which they cook in their own indigenous style. The majority of them are non-vegetarian. A common stimulant is betel nut, which has been used since ancient times as a social mediator to connect with others and form friendships. The Khasi people are hardworking; enjoy games and sports, and traditional music. In terms of social well-being, the Khasi tribe has a strong sense of “we feeling”, supporting one another, following ethical and moral instincts, and respecting one another. They believe in the spirits of Mother Nature (ki blei, ki basa). To maintain strong bonding and enhance Khasi Tribe sentiments, various festivals are celebrated every year, such as Shad Suk Mynsiem, Shad Nongkrem, Seng Kut Snem, Pom Blang, and Beh Diengkhlam. They also pay homage to freedom fighters who have died for the Khasi land, such as Tirot Sing, Kiang Nongbah, and Phan Nonglait. Following the legacy of these freedom fighters, various leaders are heading civil groups that monitor the performance of government administration and other welfare states for common social well-being.

Concept: It’s a natural process that modifies the cytology, physiology, biochemistry, and physical characteristics of seeds. These modifications lower viability and ultimately result in the seed’s death. A decline in the proportion of germination indicates degradation, and weak seedlings are produced by seeds that do germinate. The factors that impact the ability of seed to be stored include the seed’s quality at the time of storage, its pre-storage history (environmental factors during pre- and post-harvest stages), its moisture content or ambient relative humidity, the temperature of the storage environment, the amount of time the seed is stored, and biotic agents. Seed will inevitably get damaged when being stored (Balesevic-Tubic et al., 2005). The quality of the seed at the time of storage, its pre-storage history (environmental factors during pre and post-harvest stages), its moisture content or ambient relative humidity, the temperature of the storage environment, the length of time the seed is stored, and biotic agents are factors that affect the seed’s ability to be stored. Different physical changes can happen to seed during storage because of factors like temperature, moisture content, and length of storage. The viability and vigor of the seeds may be impacted by these modifications.

INTRODUCTION

Heterotrophic organisms, such as zooplankton, use phytoplankton as food, regenerating nutrients through metabolic processes and transferring energy to higher trophic levels. Zooplankton are involved in the conversion of waste matter into animal food that may be consumed, in addition to helping to shift food from the primary to the secondary level. These species act as a bridge in the food chain, carrying energy from the primary producers, planktonic algae, to the larger predatory invertebrates and fish that eat them (Kumari et al., 2008). Macróplankton (200–2000 μm), microplankton (20–200 μm), nanoplankton (2–20 μm), picoplankton (0.2-2 μm), and femtoplankton (0.02 0.2 μm) are the different sizes of plankton (Kumar et al., 2023).

The sensitivity of zooplankton to alterations in aquatic environments is significant. Differences in species composition, abundance, and distribution of body size can be used to identify the consequences of environmental disturbances(Joshi & Joshi, 2011). A number of variables, including temperature, sunlight, ocean currents, and nutrition availability, affect plankton populations. They are vital to ecosystems because they support carbon cycling, global biodiversity, and the general health of the oceans (Cottenie et al., 2001). An evaluation of the pollution state heavily depends on the interaction between phytoplankton diversity and environmental conditions. The diversity of algae found in the Chlorophyceae family is a key sign of the quality of the water. The differences in water quality can be attributed to a combination of natural and human-caused factors. Owing to extensive human activity, anthropogenic inputs from various sources are typically the main variables influencing the water quality of the majority of rivers, lakes, estuaries, and seas, particularly those that are next to densely populated areas(Jeyaraj et al., 2016). Living things, including plants, plankton, animals, and bacteria, are known as bioindicators, and they are used to monitor the state of the environment's natural ecosystem. They are known across the world as aquatic pollution indicator species. Every organic entity in a biological system gives information about the state of its surroundings. For example, plankton responds quickly to changes in the environment and is a valuable biomarker for determining the quality of water as well as a sign of pollution. Plankton is the best indicator of the health of aquatic flora and serves as an early warning system(Wadjikar et al., 2017). An ecosystem that is in balance is one in which beneficial interactions occur between the environment and living organisms. Since water quality is essential to maintaining an environment in balance, it is evident that it plays a crucial role in this relationship(Jeyaraj et al., 2016).

Introduction

Climate change and human activities have emerged as significant factors affecting water resources in diverse regions (Kumar et al. 2022; Haddeland et al. 2014). The escalation in human water consumption for domestic and industrial purposes enhanced by the impacts of climate change has resulted in an overall decline in water availability (Liu et al. 2017; Paul et al. 2021). Rising temperatures and altered precipitation patterns directly influence water availability for ecosystems and agriculture potentially leading to greater evapotranspiration rates, intensified rainfall events, and prolonged droughts (Sharannya et al. 2021; Myers et al. 2023). Furthermore, urbanization, deforestation, and agricultural practices alter landscapes, affecting water resources in these areas.

Water shortages pose threats to ecosystem health, and socio-economic development and disrupt agricultural production (Piao et al. 2010). Thus, ensuring sustainable and efficient water usage necessitates accounting for water usage and quantifying it in a changing environment. To comprehensively assess the significance of water resources in the environment, Falkenmark (1995) introduced the concept of blue water (BW) and green water (GW) water, particularly for managing water resources in water-stressed regions. Green water refers to the portion of precipitation that infiltrates into the unsaturated soil layer and is subsequently utilized by plants through transpiration and soil evaporation. In fact, “blue water” represents surface and groundwater flow in rivers, lakes, and aquifers (Abate et al 2024; Sharannya et al. 2021). While numerous studies have investigated the spatial and temporal variation of BW and GW using the Soil and Water Assessment Tool (SWAT) (Feng et al. 2021; Li et al. 2009; Lyu et al. 2019), fewer publications have focused on assessing these variations and uncertainties at the basin scale under varying climate scenarios (Sharma et al. 2023). Hydrological models such as SWAT, serve as valuable tools for simulating past and future hydrological changes in the watersheds using projected climate data. SWAT specifically can simulate both blue and green flows at the watershed scale.