eChapters

The Arid Legume includes clusterbean (Cyamopsis tetragonoloba (L.) Taub.); moth bean (Vigna aconotifolia (Jacq) Marechal; cowpea (Vigna unguiculata (L.) Walp) and horse gram (Macrotylom uniflorum (L.) Verdc). In India, these are grown extensively as rainfed crops in Rajasthan, Orissa, Maharashtra, Karnataka, Gujarat, Haryana, M.P. and to a limited extent in other states covering a total area of 51-80 lakh hectares (Table1). Most of the production comes from harsh environments where both biotic and abiotic constraints prevail limiting the production.

Arid Legumes as a group of pulses are extremely important in the common vegetarian diet of the people in the area of its cultivation. They also offer a new dimension as they have become foreign exchange earners through export opportunities. For example, clusterbean (guar) contains 30-35 per cent guar gum and India is a world leader in the export of this commodity and earns more than Rs.1100 crores annually (Mathur and Henry, 2009). Similarly, moth bean is gaining great industrial importance because of Bikaneri Bhujia industry which is heavily based on this crop (Singh and Henry, 1985). Cowpea is very important in Dahi bada industry and making curry preparation while horsegram is a minor pulse and is utilized by the poorest of the poor in its growing areas as well used for kidney stone treatment (Henry et.al., 2006; Vishwanatha et. al., 2006). Clusterbean and moth bean together account for 3.5 million hectare areas in the great Indian Thar Desert and play a very important role in the economy of the desert farmers. Similarly, cowpea and horse gram are grown mostly in marginal and sub-marginal lands under rainfed situations with no inputs applied by the farmers. These two pulses are grown in various areas in the country and easily adapted to various conditions.

In the world of academic publishing, Article Processing Charges (APCs) have emerged as a significant barrier, particularly for researchers from countries with limited resources like India. These charges, often levied by open-access journals to cover the costs of peer review, editing, and dissemination, have created a financial divide that impedes equitable access to scholarly communication. This chapter delves into the multifaceted challenges posed by APCs and outlines potential solutions to address the inequities they perpetuate (Fig. 6.1).
6.1. The Burden of Article Processing Charges
The financial burden of APCs is disproportionately felt by researchers in developing nations. Many prestigious open-access journals charge APCs ranging from $1,000 to $5,000 or more per article. For researchers in high-income countries, these charges might be manageable, supported by robust institutional funding or grants. However, for their counterparts in countries like India, where research funding is limited and often tied to modest institutional budgets, such costs are prohibitive. The disparity in income and currency exchange rates further amplifies this challenge. A charge of $2,000, for instance, could equate to several months’ salary for a researcher in India, compared to a fraction of that amount for a researcher in the United States or Europe.

9.1. Introduction

The human population of the world will reach nearly 10 billion by 2050, boosting agricultural order-in a situation of humble financial development (FAO, 2017). At present, approximately 37.7% of total land surface is being utilized for crop production. From employment generation to contribution to National Income, agriculture sector is contributing a significant role in economic prosperity of the developed nations and is playing an active part in the economy of the developing countries as well. The growth of agriculture has resulted in a significant increase in the per-capita income of the rural community. Thus, a greater emphasis on agricultural sector is the need of the hour. For countries, like India, the agricultural sector accounts for 18% of GDP and provides employment to 50% of the country’s workforce. Development in the agricultural sector will enrich the rural development which will lead toward rural transformation and ultimately the desired structural transformation (Mogili and Deepak, 2018; Shah et al., 2019). The role of Artificial Intelligence (AI) has been obvious in the agricultural sector recently. The sector needs the impetus from AI based technology to maximize its yield including improper soil treatment, disease and pest infestation, big data requirements, low output, and knowledge gap between farmers and technology.

This chapter integrates case studies, scientific examples, and policy perspectives to illustrate how AI and ML can transform agricultural weather intelligence. The discussion spans from advanced predictive models in developed nations to emerging applications in technology-limited regions, while also addressing the urgent need for resilient crisis management systems in the face of natural and human-caused conditions. Advanced scientific techniques can not only optimize food production and resource management but also play a critical role in recovery efforts during times of crisis. Agriculture is intrinsically linked to the natural world—a domain defined by fluctuating weather patterns, seasonal shifts, and unpredictable environmental factors. Today, Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing how we analyze and predict weather patterns, and their applications in agriculture are far-reaching (Fig. 5.1). From forecasting crop yields and fishery conditions to managing plantations and monitoring livestock health, these technologies are creating a paradigm shift in agricultural weather intelligence. Scientific organizations worldwide are leveraging AI and ML to refine predictive models, enable multi-variate analysis, and provide advanced warning systems for extreme events such as droughts, tsunamis, earthquakes, and even crises caused by human-made conflicts. Emerging nations with rapidly growing technological capabilities stand to benefit immensely from these innovations, tailoring solutions to local challenges while contributing to global food security and crisis resilience.

1. Introduction
Studies on soil health and agricultural growth are increasingly using artificial intelligence (AI) and machine learning (ML) techniques. With the help of these technologies, it is possible to analyse vast amounts of data and gain insightful knowledge that will help to boost crop yields and optimise agricultural practises (Neethirajan, 2020). Applications of AI and ML to research crop development and soil health have enormous potential to revolutionise agriculture. In order to increase soil health and crop yield, farmers and researchers can use these technologies to analyse massive datasets, generate precise forecasts, and optimise farming practises. Overall, artificial intelligence (AI) and machine learning (ML) provide useful tools that can support efficient and sustainable agriculture, solving global concerns in food availability and the sustainability of the environment. Unquestionably, technical developments have significantly increased the productivity and efficiency of many industries, including agriculture. The advent of words like “big data,” “data analytics,” “artificial intelligence,” “Internet of Things,” “erosion modelling,” “smart farming,” and “machine learning” are just a few examples of this technological revolution (Almoussawi et al., 2022). Digital soil mapping (DSM) uses computational models to infer regional and temporal shifts of soil types and properties based on soil observations, prior knowledge, and relevant environmental variables in order to create and populate spatial soil information systems. The supply chain for agricultural production is extremely intricate (Lamichhane et al., 2019). The way our food is produced, distributed, and consumed is changing as a result of AI. When planning crop rotations, planting times, water and nutrient management, pest and disease control, optimal harvesting, food marketing, product distribution, food safety, and other agriculture-related tasks throughout every step of the food supply chain, researchers use powered by AI tools to supply advice and expertise. In “Harnessing AI to transform agriculture and inform agricultural research,” Peters et al. (2020) presented a summary of the most recent developments, difficulties, and prospects for AI technology in agriculture. They use the four main elements of the food system—production, distribution, consumption, and uncertainty—to show the possibilities of AI. They come to the conclusion that agricultural businesses are excellent candidates for using AI and other technologies. In “AI down on the farm,” Sudduth et al. (2020), examined a number of case studies in which machine learning (ML) has been used to model various aspects of agricultural production systems and give data that can be utilised to make management decisions at the farm level. These research efforts involve providing data, essential for creating precise and effective irrigation systems and improving tools for suggesting the best nitrogen fertilisation rates for maize. Traditional crop health monitoring techniques need a lot of work and time. Using AI to monitor and detect potential crop problems or nutrient deficits in the soil is an effective method. Applications to analyse plant health trends in agriculture are being created with the aid of deep learning. These AI-powered tools are essential for improving our understanding of soil quality, crop pests, and disease in plants (Virnodkar et al., 2020).

Millets have potential as both food and fodders crops. Foods made from millet are abundant in micronutrients and necessary amino acids for regulatory functions. Since, millets were traditionally grown under adverse conditions, a great amount of polymorphism was preserved, making millet crops naturally resistant to abiotic and biotic stresses. Using data-driven technology to increase genetic gain in breeding operations can improve traditional breeding. Artificial intelligence can be used to create varieties that are adapted to certain soil and climatic conditions. In order to achieve breeding goals, machine learning (ML) is crucial in data mining and analysis, providing information for decision-making. We have been able to generate information on interesting genes using data mining and allele identification methods. For the exploitation of the natural variation and the identification of target genes, advances in high-throughput sequencing for the detection of genetic variations as well as the role of phenomics in genomics-assisted breeding can be employed. QTLs and marker-trait correlations can be identified via genome-wide association analyses. Multiple loci and genes that control agronomic traits have can be studied by comparative and functional genomics. Model selection is crucial for ML algorithms in smart breeding, which depends on knowledge in genotype, phenotype, and G×E interaction to be handled through computer science.

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.

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.

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 (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

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).

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.

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.

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:

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.

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

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.