eChapters

Introduction

Geostatistics is a branch of statistics that deals with the analysis and modelling of geo-referenced data. Its main aims are to quantify spatial variability and to create maps from point observations. Of course there is much more to geostatistics than just that and indeed some of the other uses of geostatistics are addressed in this chapter too, but the emphasis is on the assessment of spatial variability and geostatistical interpolation.

The first step of geostatistical interpolation is to quantify the spatial correlation structure of the variable of interest. This can be done by examining the observations and how these vary in space. Next spatial interpolation makes use of the quantified spatial correlation to derive optimal predictions at unobserved locations and create a map. The interpolation error is quantified as well, which helps to design optimal spatial sampling schemes that balance data collection costs and map accuracy. All this will be explained in this chapter, but in order to do so we first need to explain the statistical theory that underlies geostatistical interpolation followed by representation of the spatial correlation structure, the basics of geostatistical interpolation (‘kriging’), kriging extensions and spatial stochastic simulation.

Intelligent irrigation requires systems capable of responding dynamically to crop, soil and environmental conditions. This chapter explores fusion-driven reinforcement learning for precision irrigation, with particular attention to rice alternate wetting and drying and drip farming systems. It introduces the overall framework and methodology used to combine information from different sources for irrigation decision-making. Kalman filter fusion is considered for integrating relevant data, while reinforcement learning provides an adaptive environment in which irrigation strategies can be optimized. The chapter presents results and discussions associated with the proposed approach and considers its potential contribution to efficient water management. Future research directions focus on improving intelligent irrigation systems and their agricultural applicability.

Computational intelligence is central to the development of proactive, personalized and connected healthcare systems. This chapter examines artificial intelligence, machine learning, expert systems, medical-image analysis, IoT, big-data analytics, cloud computing and blockchain as key enabling technologies. It presents intelligent architectures for diagnosis, monitoring, treatment support and secure health-data management. Privacy, cybersecurity, explainability, bias, fairness, scalability and deployment challenges are discussed in relation to practical healthcare implementation. The chapter proposes a research roadmap involving trustworthy AI, advanced privacy-preserving mechanisms, edge-cloud intelligence, digital twins, multi-omics integration and personalized health models. It also emphasizes accessible computational-intelligence solutions for rural and resource-constrained healthcare environments

The rapid pace of technological innovation, coupled with mounting environmental challenges and shifting societal expectations, is fundamentally reshaping the landscape of sustainable food production. In this chapter, we explore the future outlook of this dynamic field by examining emerging trends in weather intelligence, the transformative potential of innovations in artificial intelligence (AI), the Internet of Things (IoT), and data integration, as well as the evolving policy and ethical considerations that underpin sustainable agriculture. The integration of advanced technologies with traditional agronomic practices has led to the development of data-driven systems that not only forecast weather with greater accuracy but also optimize crop management, resource utilization, and risk mitigation. As we look toward the future, the convergence of these disciplines promises to usher in a new era of resilient, efficient, and environmentally conscious food production systems. We begin by outlining the current landscape and identifying key trends that are driving change. The discussion then transitions into an exploration of cuttingedge innovations in AI, IoT, and data integration that are revolutionizing weather intelligence. Subsequent sections address the policy frameworks and ethical dilemmas accompanying rapid technological adoption in agriculture, before concluding with a vision for future research directions and strategic planning. Finally, the chapter suggests additional topics—such as blockchain for supply chain transparency and urban vertical farming—to ensure a holistic view of sustainable food production in a rapidly changing world.
12.1 Emerging Trends in Weather Intelligence and Food Production
Over the past decade, advancements in sensor technologies, communication networks, and computational algorithms have revolutionized weather forecasting and climate monitoring. These breakthroughs are not only critical for predicting weather events but also serve as the foundation for proactive decision-making in agricultural management. High-resolution satellite imagery and remote sensing techniques now provide granular data about atmospheric conditions, soil moisture levels, and crop health (Fig.12.1).

AI-driven animal husbandry is advancing through machine learning, computer vision, IoT, big-data analytics, wearable devices and automated equipment. This chapter considers future applications for monitoring animal health, behaviour, nutrition, reproduction and environmental conditions. Predictive systems can support early disease detection, precision feeding, improved breeding and reduced production costs. However, high implementation costs, data privacy, inadequate infrastructure and limited digital knowledge remain significant obstacles, particularly in developing regions. Future research should prioritize affordable, user-friendly and explainable technologies suited to smallholders. Ethical AI, climate adaptation, multimodal data integration, omics analysis and interdisciplinary collaboration among animal scientists, engineers, policymakers and farmers are identified as essential research directions.

Agricultural decisions are frequently made under uncertainty caused by variable weather, soil conditions, crop responses and market behaviour. Conventional binary logic may not adequately represent such imprecision. This chapter explains how fuzzy sets, membership functions, linguistic variables and rule-based reasoning model conditions such as soil moisture, crop health and yield potential. Applications include irrigation scheduling, fertilizer management, disease diagnosis and greenhouse climate control. Fuzzy logic can connect deterministic mathematical models with real-world variability and complement simulations of crop growth, pests and water dynamics. Its integration with artificial intelligence, machine learning and IoT systems can support adaptive, resource-efficient and climate-resilient agricultural decision-making.

Modern plant genomics generates highly complex and high-dimensional datasets that require advanced computational approaches for effective analysis. This chapter explores the application of artificial intelligence to plant genomic data analysis. Machine learning and deep learning techniques are discussed for identifying complex patterns, classifying genomic information, predicting phenotypes and supporting genomic selection. Convolutional neural networks, long short-term memory networks and transformer models are considered for analyzing genomic sequences and relationships. The chapter presents an AI-based framework integrating data processing, model development and prediction while highlighting the challenges associated with genomic data heterogeneity and dimensionality. It emphasizes the potential of AI for improving genomic research and plant improvement strategies

In modem days, many informations receive through electronic media with instant connecting by satellite based information on social, economical, environmental etc. the informations are coded with feasible electronic encrypt and delivered to people. Similarly remote sensing, GIS (Geographical Information System) and GPS (Global Positioning System) also serve world for better understanding the ground reality along with reducing cost of operation, transforming data through electronic webs easily. In earlier phase, ground related information was not easily obtained because all kind of work had been done manually using analog maps of earth; even map draw was done by drafting from totposheet or hand maps, no interface of analog and digital world.

Introduction

Geomorphology is the study of earth’s landscapes over time and space, and involves an analysis of both its surface features as well as the processes responsible. Historically, the subject evolved through monumental studies on supposed evolution of the landscape, based on intuitive knowledge of the researchers on landscape response to endogenic and exogenic forces, as well as visions of possible changes over a time scale of centuries to millennia or more. Too much emphasis on the study of possible landscape evolution in an era of little technological back-up facilities meant that the discourses were largely philosophical in nature, with fewer measurable parameters to substantiate the concepts or the reasoning, although reflecting deep understanding of the researchers involved. As older concepts began to be confronted with newer ones, and applied aspects started receiving greater attention, a need was felt for quantification of the features and processes. This was more so for an understanding of the fluvial landscape, although aeolian and other process-based studies also gradually incorporated quantitative analysis as a means of understanding the landscape. Despite the awareness, lack of adequate equipment hindered the progress of quantification in the pre-World War II period.

The term ‘geo’ is a Greek word, it means the earth, and the term ‘spatial’ means parameter/feature measured, mapped or modeled with reference to distance or space. The geospatial technology refers to the technology that measure, model, analyze, map, or predict the features or phenomena that occur on earth and its surface.

Introduction

High degree of spatial variability in soil properties is observed at different scales. This variability is generally originated from the combined effect of physical, chemical or biological processes operating with different intensities at different scales (Goovaerts 1998). Knowledge of this spatial variationof soil properties is important in several land management applications e.g. variable rate application of nutrient and water in agricultural production system, land suitability classifications, modeling water balance components at landscape scales, precision farming etc. The concept of ‘management zone’ was also evolved in response to this large variability with the main purpose in efficient utilization of agricultural inputs with respect to spatial variation of soils and its properties (Franzluebbers et al. 1996, Atherton et al. 1999, Malhi et al. 2001). Therefore, an appropriate understanding of spatial variation of soil properties is essential. The most important way to gather knowledge in this aspect is to prepare soil maps through spatial interpolation of point-based measurements of soil properties. Here, a case study is discussed where soil water retention at two critical levels e.g. field capacity (FC) and permanent wilting point (PWP) was spatially characterized through geostatistical approaches. Because these two soil moisture contents guide farmers to apply right amount of irrigation water at right time. The study was carried out at the experimental farm of ICAR-Indian Agricultural Research Institute, New Delhi, India (28°37′-28°39′ N, 77°8′ 30″-77°10′30″ E, 217-241 m above mean sea level). A detailed discussion of the study is available in Santra et al. (2008)

Introduction

Soil organic carbon (SOC) content plays a key role in maintaining soil fertility in agricultural farm. At present, there has been great interest to reduce the atmospheric CO₂ level through a chain of increasing vegetation carbon pools first and then to store them as soil carbon pool. To quickly assess the soil carbon restoration programmes, it is important to know how much soil carbon is stored in an agricultural farm through adoption of different land management practices. Average SOC content is considered in most soil carbon pool calculations in spite of its large spatial variation in landscape, and thus leads to inaccurate estimate of SOC stock for an area. Therefore, reliable estimates of SOC pools and their spatial variability are essential to establish the soil carbon sequestration programmes at different landscape scales. Here, a case example of assessing the spatial variation at a typical agricultural farm is discussed. The experimental field was located at Badoda village of Jaisalmer district, Rajasthan covering an area of about 76.12 ha. The farm lies between 265^o2′30″-26°53′30″ N and 71°12′15″-71°13′15″ E (Fig. 24.1) and 20 km away from Jaisalmer city in eastward direction. Elevation of the farm ranged from 232 m to 248 m above mean sea level with an overall slope from south to north. Among total area of the farm, 85.38% area was under cultivation in four main blocks: block A, B, C, and D whereas rest area was under grassland.

Agriculture faces increasing pressure from population growth, climate change, resource scarcity, land degradation, water shortages, and growing food demand. This chapter examines the major global challenges affecting agricultural productivity and food security. Climate variability, extreme weather events, declining natural resources, and changing pest and disease patterns create substantial risks for farming systems. The chapter discusses sustainable and climate-smart agricultural approaches for improving resilience and resource-use efficiency. It also highlights the contribution of artificial intelligence, precision agriculture, biotechnology, remote sensing, and digital technologies in addressing these challenges. Policy support, research investment, institutional cooperation, and equitable access to technology are emphasized for sustainable agricultural development

GPS stands for Global Positioning System, basically Navstar GPS is a radio based space aviation system developed by United state government which was operated by United State Air Force. It provides geographic location and time related information to GPS receivers under global navigation satellite from anywhere of earth where unobstructed line of sight to 4 or more GPS satellite co-ordinate (Anonymous, 2018). Even GPS not required any user for transmitting data, with working independently on any telephonic or internet receivers; through these technologies we can enhance usefulness of the GPS location based information. The GPS serves as critical positioning capacity to various fields like army operation, civil aviation, and commercial scope to wider scope in around world.

Introduction 
The evolution of governance reforms in India must be situated within the broader global and domestic transitions in public administration. Inheriting its foundational structures from British colonial rule, India retained key institutional frameworks post-Independence in 1947, including the Parliament, Executive, Judiciary, and systems of law and order and revenue administration. Although these institutions provided a degree of continuity and stability, they gradually became mired in procedural rigidity, excessive centralization, and legal formalism. Mathur (2013) notes that the performance of the three core organs of governance, the legislature, executive, and judiciary, has drawn increasing public concern, particularly in their responsiveness to the rising aspirations of Indian citizens. In the early decades post-independence, India adopted a centralized, commandand- control model of economic and administrative governance. State officials wielded considerable discretion over the allocation of licenses, quotas, and permits, while the private sector remained underdeveloped and tightly regulated. This framework, while aligned with the nation-building ethos of the time, ultimately led to inefficiencies, corruption, and citizen disenchantment. The 1990s marked a watershed moment in India’s governance trajectory. With a balance-of-payments crisis and external pressures from international financial institutions such as the IMF and World Bank, India adopted neoliberal economic reforms centered on liberalization, privatization, and globalization (Ramesh Singh, 2013). These reforms fundamentally altered the state’s role from a direct provider of goods and services to a facilitator and regulator. A new governance discourse emerged, emphasizing accountability, transparency, efficiency, and citizen participation. This also marked the diffusion of New Public Management (NPM) ideas, which advocated for performance-based administration, managerial autonomy, and partnership with non-state actors. Simultaneously, the Tenth Five-Year Plan (2002–2007) explicitly identified governance as a key determinant of development outcomes, underscoring that economic growth could not be sustained without reforming the institutions

The rapid adoption of Large Language Models requires organizations to manage not only model performance but also ethical, regulatory, operational and lifecycle risks. This chapter presents an integrated perspective on governance, optimization and lifecycle management of LLMs. It examines governance principles based on fairness, accountability, transparency, human-centered design and responsible AI. Regulatory developments and policy gaps are considered alongside the challenges of auditing opaque models, controlling hallucinations and establishing accountability across organizations and jurisdictions. The chapter also explores optimization methods such as pruning, quantization, model compression, instruction tuning, LoRA and QLoRA to improve efficiency while maintaining useful capabilities. Lifecycle management extends from data collection and model design through training, evaluation, deployment, monitoring, updating and eventual decommissioning. Real-world cases demonstrate the importance of continuous testing, feedback, security controls and governance throughout the model lifecycle. Emerging threats, including prompt injection, data poisoning, misinformation, privacy leakage and malicious use, further emphasize the need for coordinated oversight. The chapter ultimately advocates a lifecycle-based governance framework combining technical optimization, regulatory compliance, ethical safeguards and continuous monitoring

The successful transformation of agriculture through artificial intelligence and digital technologies requires supportive policies, institutions, infrastructure, and farmer-oriented programmes. This chapter examines the role of government policies and institutional support in promoting smart farming and digital agricultural innovation. Effective policies can facilitate investment in research and development, digital infrastructure, farmer training, technology access, rural connectivity, data governance, and sustainable agricultural practices. Institutional mechanisms are particularly important for overcoming barriers faced by smallholder farmers, including high technology costs, limited digital literacy, and inadequate infrastructure. The chapter emphasizes collaboration among governments, research institutions, universities, private enterprises, and farming communities to promote inclusive technology adoption, strengthen resilience, and achieve sustainable food security.

Solution:

Steps used for plotting the bar graph are as:

Step 1: Open Excel sheet and feed the above data as given in figure. 9.1.

Step 2: Select data and go to insert menu and select column chart. (figure 9.2)

Step 3: Select 2-D column, option first from dialog Box (figure 9.3).

Step 4: The desired output will be as shown (figure 9.4).

Remark The data labels, axis name, title of the graph etc. can be inserted through selecting option formatting chart area and add data labels by right clicking on the graph output (figure 9.5).

The growing demand for sustainable materials has encouraged the development of green composites based on bio-based polymers and renewable reinforcements. This chapter explores advanced carbon nanomaterials, including graphene, graphene oxide, carbon nanotubes, carbon nanofibers, and carbon quantum dots, as functional reinforcements for green composites. It discusses their synthesis, functionalization, incorporation into biopolymer and bio-based matrices, and influence on mechanical, electrical, thermal, and barrier properties. Particular attention is given to green synthesis approaches using biomass-derived resources, surface modification, dispersion, environmental considerations, scalability, cost, and life-cycle assessment. The chapter also considers future opportunities for multifunctional and commercially viable sustainable nanocomposites.

Graphene and related carbon nanomaterials have attracted considerable attention as reinforcements for high-performance green composites. This chapter examines graphene, graphene oxide, reduced graphene oxide, carbon nanotubes, and graphene nanoplatelets and their integration with bio-based polymers and natural fibres. It discusses synthesis and fabrication methods, characterization techniques, mechanical properties, electrical and thermal performance, barrier properties, and applications. The chapter also considers dispersion and compatibility challenges associated with incorporating nanomaterials into green matrices. Graphene-based composites are explored for energy storage, environmental treatment, biomedical applications, sensors, packaging, automotive, and aerospace uses. Emerging trends focus on multifunctionality, scalable production, cost reduction, and sustainable processing.