eChapters

Introduction

Importance of basic statistics in everyday life cannot be stressed enough. Inclusion of basic statistics in high school curriculum is uncommon but proves highly beneficial to supplement certain careers in future. This field may be the most widely used of all and most people don’t even know they are using it. Many of today’s brightest business minds use this study to create charts and graph that allow them to stress a certain aspect of the business they are dealing with. Market research agencies, News rooms, marketing divisions of large corporations, NGOs, accounting firms etc. are full of people who specialize in basic statistics.

Statistics is concerned with the describing, interpreting and analyzing data. It is, therefore, an essential element in any improvement process. Statistics is often categorized into descriptive and inferential statistics. It uses analytical methods which provide the math to model and predict variation. It uses graphical methods to help making numbers visible for communication purposes.

Descriptive Statistics

• Descriptive statistics summarizes or describes characteristics of a data set.

• They help to make sense of large numbers of individual responses, to communicate the essence of those responses to others

• Helps exploring and making conclusions about the data in order to make rational decisions.

• Includes calculating things such as the average of the data, its spread and the shape it produces.

• Are displayed as tables, charts, percentages, frequency, distributions and reported as measures of central tendency

• Determine if the sample is normally distributed (bell curve) as most statistical tests require the sample to have normal distribution

• Determine if the sample can be compared to the larger population

Difference between Descriptive and Inferential Statistics

Nowadays, statistics is playing a major role in the field of research; that helps in the collection, analysis and presentation of data in a measurable form. It is quite hard to identify, whether the research relies on descriptive statistics or inferential statistics, as people usually, lacks knowledge about these two branches of statistics. As the name suggests, descriptive statistics is one which describes the population. On the other end, Inferential statistics is used to make the generalisation about the population based on the samples.

Abstract

Conversion of an already arid land into a desert through indiscriminate human actions magnified by droughts. Such actions include overgrazing, repeated burning, intensive farming, and stripping of vegetation for firewood. One quarter of the earth’s surface is threatened by desertification - an area of over 3.6 billion hectares. Since 1990, Six million hectares of productive land are lost every year due to land degradation. Desertification threatens the liveihoods of more than one billion people and has already made over 135 million people homeless. Desertification does not occur in linear, easily mappable patterns. Deserts advance erratically, forming patches on their borders. Areas far from natural deserts can degrade quickly to barren soil, rock, or sand through poor land management.

Concept and Definition

Desertification is a major problem in the drylands of India, affecting the way of life for its inhabitants. The problem is more severe in the arid lands in the northwestern part of the country. There have been a few numbers of definitions of the word “Desertification”. However for simplicity of its origin and meaning we would refer to two of the frequently used ones; (1) proposed in 1991 by UNEP: “Land degradation in arid, semi-arid and dry sub-humid areas resulting mainly from adverse human impact and (2) prepared for the Earth Summit at Rio, held in June 1992, for inclusion in Agenda 21, as “Land degradation in arid, semi-arid and dry sub-humid areas resulting from climatic variations and human activities. (Anonymous 1992).

Comparing two of these initial definitions, it would show that first one indicated desertification as a state of natural resources like soil and vegetation, mainly resulting from human activity while the second one included climate as a major cause. However, it reduced the emphasis of human activities as being the primary responsible factor and tried to link the possible impact of climate change on desertification. But the simplicity of the 1992 definition of desertification helped researchers to bring in sharp focus on the special environmental and socio-economic problems of the drylands.

So over the years, the key words that explains the term “desertification” has been changing or are being amended for specific purposes. This would at least mean that the problem of desertification is still debated world-wide, and its impact is not limited to some nations/regions as was presumed and discussed in first of UNCCD platforms. Therefore, as a precaution and combating this process, few catchy words like non-sustainable management, productivity, over-exploitation of natural resources, dryland livelihood, droughts are found to surround the word desertification. Earlier, some, people equated desertification with advancing boundaries of the existing desert and in India, examples of expansion / contraction of geographical boundary of Thar desert was a public debate among various researchers and planners. In the same time, some of the definitions tried to broaden its geographical scope so that greater populations suffering from adverse effects of land degradation would benefit out of various remedial measures.

4.1 Introduction

The objective of this chapter is to design a mobile app for agricultural field automation with Blynk app. Mobile app is designed to control the water management in field as per requirement. The complete system comprises of two sections sensor node and mobile app. Sensor node comprises of Node MCU, Power supply, LCD, Relay board, soil sensor and Ultrasonic sensor. The system is designed to establish control and communication with specific agricultural field to take sensory data from soil and ultrasonic level sensor and control the PUMP IN motor and PUMP OUT motor with the help of mobile app.Fig.4.1 shows a block diagram for a system to control specific agricultural field with mobile app and IoT modem.

5.1 Introduction

The chapter describes the development of local web server on the laptop, to control the actuator from the server. Web server is used as remote control for the system. The system is designed to control water pump with respect to the soil sensor values. It comprises of NodeMCU, soil sensor, LCD, two relay boards for pump in and out the water from field.

The Central Nervous System not only consists of the Brain but also involves the Spinal cord.

The spinal cord is responsible for conveying information from the Brain to the rest of the body via the peripheral nervous system. The spinal cord can be divided into 4 functional segments.

1. C1-C5 (Upper Cervical Segments)

2. C6-T2 (Brachial Plexus)

3. T3-L3 (Thoraco - Lumbar)

4. L4-S3 (Lumbo-Sacral Plexus )

It is important to note that the motor and sensory function of the pelvic limbs are not only controlled by the Lumbo-Sacral plexus but also involve the Thoraco Lumbar segments (T3-L3) of the spinal cord. It is important to understand not only the neuroanatomy, but also the functional anatomy before approaching a neurological patient.

Diagrams and graphs are the effective and attractive ways of presenting data. Widely used diagrams for data presentation are pi chart and bar diagrams. Bar diagrams are also of various types; (i) Simple bar diagram (ii) Component or sub-divided bar diagram (iii) Multiple bar diagram (iv) Percentage bar diagram. The widely used graphical presentation are histograms, frequency polygon and frequency curve.

There are numerous libraries to create charts and graphs in R programming language.

Pie-chart- A pie-chart is a representation of values of variable as sectors of a circle with different colours/shadings. The colours/shadings used for different sectors are indexed at the side of the graph. pie() function is used to create pie chrt in R.

Let us create an exel file names as ‘exp_sports’ which contains the expenditure made (in Rs.) under various sports in a sports club. The name of the sports are cricket, hockey, tennis and football. The names of sports are placed under the column head ‘sports’. The corresponding expenditure (in Rs.) are 7200, 3600, 1800, 1800 and they are placed under the column head ‘expenditure’.

The basic syntax for creating a pie-chart using the R is.

8.1 Introduction to Dictionary in Python

A dictionary is a collection which is unordered changeable and index. In python dictionaries are written within curly brackets, and they have keys and values. Means the dictionary contains two things first is the key and the second is the value.

8.1.1 Create and Print a Dictionary

Example

dict1={‘brand’:‘Suzuki’,‘model’:‘dzire’,‘year’:2020}

print(dict1)

Accessing items: you can access the items of a dictionary by referring to its key name, inside square brackets.

Example

Accessing items: there is also a method called get() for the same task.

dict1={‘brand’:‘Suzuki’,‘model’:‘dzire’,‘year’:2020}

print(dict1)

y=dict1.get(?model”)

print(y)

Digital dairy farming integrates IoT, artificial intelligence, machine learning, big-data analytics, cloud computing, sensors and automation to improve herd management. This chapter explains how wearable devices and monitoring systems support early disease detection, welfare assessment, milk-yield analysis and precision feeding. Automated milking, feeding and other operations reduce manual labour, improve consistency and lower operating costs. The architecture of smart dairy systems—including sensors, wireless communication and decision algorithms—is examined alongside environmental and economic benefits. Barriers include high initial investment, limited digital skills, poor rural connectivity and cybersecurity risks. Future developments may include AI-enabled autonomous dairy farms and interconnected, sustainable agricultural ecosystems.

Introduction 
Digital governance in India has made remarkable strides over the past two decades, transitioning from isolated digitisation projects to an integrated, citizen-centric governance ecosystem. Enabled by policy reforms, technological innovation, and infrastructural development, digital governance has become a cornerstone of the country’s public service delivery, transparency, and accountability. However, the ambitious expansion of digital governance has also encountered systemic and structural challenges that continue to hinder its full realisation. These challenges are not merely technological but are deeply embedded in socio-political, administrative, and economic contexts that define the Indian public administration landscape. While the progress has been notable, the sustainability and inclusiveness of digital governance remain contingent on the country’s ability to address these entrenched constraints. Despite substantial advances, several persistent challenges threaten the transformative potential of digital governance in India. Chief among them is the digital divide across rural-urban, gender, economic, and social lines, which inhibits equitable access to digital services. According to the National Family Health Survey (NFHS-5, 2019–21), internet usage in rural areas remains alarmingly low, with only 33% of women and 57% of men reporting regular access. Infrastructural constraints, human resource limitations, weak system interoperability, cyber-security threats, and concerns about data protection and digital rights compound these disparities. The post-2015 period has witnessed transformative initiatives under the Digital India Programme, including the proliferation of platforms such as Aadhaar, DigiLocker, Unified Payments Interface (UPI), and MyGov, among others (MeitY, 2022). While these developments have significantly enhanced service accessibility and administrative efficiency, they have simultaneously revealed critical gaps in digital infrastructure, policy coherence, data governance, and institutional capacities. For instance, the rural-urban digital divide, disparities

Introduction 
India’s digital governance journey represents a transformative reimagining of public administration in the 21st century. The integration of Information and Communication Technologies (ICTs) into governance structures has enabled a paradigmatic shift from hierarchical, rigid, and often exclusionary bureaucratic systems to more responsive, transparent, participatory, and citizen-centric governance mechanisms (Bhatnagar, 2004; Madon, 2009; Singh, 2013; Rao et al., 2020). This shift aligns with global trends in public administration, where digitalization is seen as a means to enhance state capacity, citizen engagement, and administrative efficiency. India’s progress in this domain is evidenced by its improved performance in international indices. For instance, the United Nations E-Government Development Index (EGDI) 2022 places India 105th out of 193 countries, with notable improvements in the Online Services Index (OSI) and Human Capital Index (HCI), making it one of South Asia’s top digital governance performers despite a recalibrated drop in global ranking (UN DESA, 2022). In the Network Readiness Index (NRI) 2023, India stood at 65th among 134 countries, reflecting advancements in digital infrastructure, skills, and public digital platforms like Aadhaar and UPI (Portulans Institute, 2023). However, challenges persist, as highlighted by the ND-GAIN Index, which underscores India’s vulnerability due to digital inequality and climate-sensitive infrastructure (Notre Dame Global Adaptation Initiative, 2023). These performance indicators reflect a dual narrative: India is at the forefront of digital public innovation, yet its trajectory remains marred by barriers to inclusivity, last-mile service delivery, and institutional adaptability. The conceptual framework of digital governance in India encompasses key principles and mechanisms that collectively aim to bridge these gaps and foster a digitally empowered society and knowledge economy. Digital governance extends beyond mere e-Governance or ICT deployment; it refers to the strategic, integrated use of digital tools to transform governance models, improve service delivery, enhance transparency, and facilitate citizen

Introduction 
India’s digital transformation over the past decade has emerged as a defining feature of its governance and developmental strategy, propelled by the ambitious Digital India initiative launched in 2015 by the Ministry of Electronics and Information Technology (MeitY). With a vision to transform India into a digitally empowered society and knowledge economy, the initiative seeks to harness the power of information and communication technologies (ICT) to make governance inclusive, efficient, transparent, and responsive. Digital India marks a paradigm shift in how public administration operates, embedding technology at the core of policy delivery, citizen engagement, and institutional coordination across sectors and tiers of government. Creating a robust and scalable digital infrastructure is at the heart of this transformation. Major projects like BharatNet aim to connect 2.5 lakh Gram Panchayats via high-speed optical fiber, of which over 1.5 lakh were connected by 2020. Simultaneously, establishing more than 3.7 lakh Common Service Centres (CSCs) has extended digital service delivery to rural and remote areas. These CSCs function as inclusive access points, offering health, education, agriculture, banking, and public utilities services in a culturally and linguistically localized format, catalyzing rural digital entrepreneurship and last-mile inclusion. India’s approach to digital governance extends beyond infrastructure to encompass citizen-centric digital services and platform-based public service delivery models. Platforms such as UMANG, eHospital, BHIMUPI, DigiLocker, eCourts, Tele-Law, and eWay Bills have simplified citizen interaction with the state, enhancing the ease of living and improving administrative efficiency. The Government eMarketplace (GeM) has transformed public procurement by making it more transparent, efficient, and accessible for small businesses and start-ups. The Jan Dhan–Aadhaar–Mobile (JAM) trinity has revolutionized welfare delivery by facilitating Direct Benefit Transfers (DBT) into verified bank accounts, eliminating intermediaries, and saving over ?1.7 lakh crore by 2020.

Introduction 
Digital infrastructure has emerged as the foundational pillar for transforming governance systems in the 21st century, particularly in developing countries like India that aspire to harness digital technologies for inclusive growth and efficient public service delivery. Defined broadly, digital infrastructure encompasses the integrated ecosystem of hardware, software, connectivity networks, data centers, cloud platforms, and institutional frameworks that enable the generation, storage, transmission, and utilization of digital information (Verdecchia, Lago, & de Vries, 2022; Clark, Marin, Ardic Alper, & Galicia Rabadan, 2025). In the context of governance, such infrastructure provides the critical backbone for digital public goods, service delivery platforms, and real-time decision-making systems. India’s strategic thrust towards digitalization since the early 2000s, and particularly after the launch of the Digital India programme in 2015, reflects an ambitious attempt to leverage digital infrastructure not merely as a technological upgrade but as an instrument of governance reform, economic empowerment, and social equity (Ministry of Information & Broadcasting, n.d.-a). Digital infrastructure forms the bedrock of contemporary digital governance, unlocking scalable, secure, and inclusive public service delivery in India. It comprises high-speed broadband networks, State Data Centres, identity platforms, cloud systems, and institutional linkages essential for capturing, processing, storing, and transmitting data across government and citizen-facing services (Aapti Institute, 2024). Since the launch of the National e-Governance Plan (NeGP) in 2006, India’s efforts have focused on strategic infrastructure investments like State Wide Area Networks (SWANs), State Data Centres (SDCs), and Common Services Centres (CSCs) to decentralize digital access across states and districts (Ministry of Information & Broadcasting, n.d.-a). Building on the foundation laid by the National e-Governance Plan, the Digital India Mission—initiated in December 2015—elevated infrastructure development to a national policy priority by integrating platforms such as

Introduction

Globally, the SOC pool of 1,550 Pg (1 Pg = 1015 g) to 1-m depth is about three times that of biotic and twice that of atmospheric pools (Post et al. 1982, Watson et al. 2000). In arid and semi-arid ecosystems, SOC contents show high degree of spatial variability due to patchiness of vegetation (Wiesmeier et al. 2009). The arid ecosystem might play a key role in mitigation of climate change effects through sequestering carbon in soils (United Nations 2011). Therefore, a suitable land use system having high C sequestration potential for arid ecosystem needs to be identified and promoted. However, the prevailing conditions of arid ecosystems normally does not support dense vegetation. Therefore, low carbon stock is expected in both above-ground vegetation and below-ground soil. Fruit trees are an integral part of arid farming system and played a significant role to improve the nutritional security and employment opportunity of farmers but also have potential role to increase carbon storage both in biomass and soil. 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. Therefore, reliable estimates of SOC pools are essential to establish the soil carbon sequestration programmes at different landscape scales.

Introduction

A complex spatial soil properties pattern is directly controlled by soil internal factors and anthropogenic impacts.Hartemink et al. (2001) elaborate a strong decline in mineralogy, morphology and genesis of soil research than strong increase in pedometrics applications during 1967 to 2001. Hartemink and McBratney (2008) identified this change as “soil science renaissance”. Basically, the soil science deeply rooted in agriculture, geology, and chemistry but the paradigm has been shifted from classification and inventorization to quantification of soil patterns in spatial as well as temporal patterns and their impact on the ecosystem health and hydrological cycle. The environmental keen approach explains that soil is in the center of the ecosystem and highly interacting with the biotic and abiotic factors of the ecosystem and a complex pattern and process has been co-evolved with the time.

Introduction

In the last decade, digitalization has transformed every aspect of life and society, changing almost every area of our day-to day lives. Not just that; the rate of change that is happening is astonishing and only going to accelerate further. From the way, we work and communicate to how we organize our lives, digital tools and technologies are everywhere including the veterinary sector. Veterinarians are recognizing the need to depart “the analog island” and increasingly willing to embrace digital solutions. Undoubtedly, digitalization is crucial when it comes to boosting efficiency and quality in veterinary medicine and delivery of animal healthcare to masses across various geographies and resource poor setups. Uniquely these digital tools helps vets also to focus on the needs of patients and animal owners.

Digitalization is transforming ice cream operations by connecting procurement, production, quality, cold chain, sales, distribution, finance, and management through integrated information systems. This chapter examines the transition from traditional manual operations to ERP-, AI-, IoT-, and data-driven management. It explains the costs of limited digitalization and the department-wise benefits of integrated systems, including better traceability, demand forecasting, inventory management, energy monitoring, cold-chain control, predictive maintenance, and profitability analysis. The chapter also explores artificial intelligence, business intelligence, IoT monitoring, and evidence from industry research. It emphasizes that technology alone is insufficient: standardized processes, accurate master data, user training, governance, and management discipline are essential for achieving measurable digital transformation.

Introduction

In agriculture, plant diseases have always been a major problem, as they cause crop quality to decrease, resulting in yield. Plant disease has a range of effects from minor symptoms to serious damages in whole crop areas, which lead to high financial costs and significantly impact the agricultural economy, particularly in developing countries dependent on a single crop or on a few crops.

Various approaches have been developed to detect disease in order to avoid significant losses. Causal agents have been precisely defined by methods developed in molecular biology and immunology. However, for many farmers these techniques are inaccessible and require detailed knowledge of the field or substantial time and energy to be used. According to the United Nations Food and Agriculture Organization, most farms in the world are small and family-owned in developed countries. Such families provide food for a significant proportion of the population of the planet. However, hunger and food insecurity are not rare and there is restricted access to markets and services. For the above reasons, a great deal of work has been undertaken in order to establish methods that are sufficiently reliable and available to most farmers. Where Plant disease is an essential factor contributing to a major decline in plant production quality and quantity. Plant disease identification and classification are a significant role in improving plant production and economic development.

The main purpose of this lecture note is to provide basic information on patent drafting skills and train applicants for drafting a patent in clear and concise manner.

We will see the components of Complete Specification through the following Case studey (1) of a solar water heater. The figures are provided below for reference and better understanding.

Aerial photography is objective of getting panoramic view of earth surface to analyze the features of earth and their interrelation at particular time period which may be changed in further events. In this concerned, the observation may play a vital role in achieving the goals of photography. Undoubtedly it is widely used in social function and so on, but still awaited for scientific or research use. Drone can measure the geometry of ground surface at lower altitude with more accuracy in the form of visual feature in natural colour. flight height of drone mainly limits the large area coverage due to lower flying and drone can work perfectly at low altitude planning with good quality feature/photography.

Abstract

Drug design is a complex computational process that involves studying the structural and physicochemical properties of biological moieties in order to identify new chemical or biochemical substances that can be used as drugs with specific pharmacological properties. The drug discovery pipeline is a crucial aspect of drug design that requires a detailed understanding of the various phases involved. In this chapter, we discuss the fundamental principles and phases of drug design. Additionally, we delve into the concept of docking, a critical step in the drug discovery process that involves predicting the orientation and affinity of a ligand binding to a protein target. The use of computational tools and techniques in drug design has revolutionized the field and is essential for the development of safe and effective drugs.

Early Warning Systems (EWS) have evolved from simple, rudimentary alert mechanisms into complex, multi-hazard, data-driven networks that play a critical role in disaster risk management and climate adaptation. In today’s rapidly changing climate, timely and accurate information is essential not only for saving lives but also for protecting livelihoods, reducing economic losses, and enhancing community resilience. The application of EWS varies significantly by region, geography, type of disaster, and across diverse sectors such as agriculture, aquaculture, horticulture, animal husbandry, fisheries, and allied sciences. Global initiatives - led by organizations such as the United Nations - aim to ensure that every person, particularly the most vulnerable, is equipped with lifesaving early warnings by 2027 (Fig.11.1). Complementary partnerships, such as that between the World Meteorological Organization (WMO) and the Food and Agriculture Organization (FAO), further strengthen these systems by integrating cutting edge forecasting with actionable advisories for critical sectors. A particularly groundbreaking effort is the United Nations’ Early Warnings for All (EW4ALL) initiative, launched in 2022 by UN Secretary General António Guterres, which seeks to provide comprehensive early warning coverage for hazardous weather, water, and climate events to every person on Earth. Underpinning these global efforts is Weather Intelligence - a transformative approach that harnesses advanced data analytics, real time monitoring, and machine learning techniques to refine forecasts and deliver precise, context specific alerts. By integrating diverse data streams and tailoring

Earth’s atmosphere is a dynamic and complex system that plays a crucial role in supporting life as we know it. This chapter explores into the fascinating world of this gaseous envelope that surrounds our planet, exploring its composition, structure, and the processes that drive both our daily weather and long-term climate. It includes:
• Composition and Structure: It will begin by examining the unique blend of gases that make up the atmosphere, including nitrogen, oxygen, argon, and trace amounts of others. It will also be explored how the atmosphere is structured into distinct layers (troposphere, stratosphere, mesosphere, thermosphere, and exosphere), each with its own characteristics and role.
• Weather: Weather refers to the short-term conditions of the atmosphere at a specific time and place. We’ll discuss the key elements of weather, such as temperature, pressure, humidity, wind, and precipitation, and how they interact to create the ever-changing weather patterns we experience.
• Climate: In contrast to weather, climate describes the long-term average of atmospheric conditions in a particular region. We’ll investigate the factors that influence climate, including latitude, altitude, proximity to oceans, and atmospheric circulation patterns. We’ll also explore the different climate zones found across the globe.
• Interconnectedness: A key focus of this chapter understands how weather and climate are interconnected. Daily weather patterns are influenced by the same fundamental atmospheric processes that shape long-term climate. For example, the way the sun’s energy interacts with the atmosphere drives both daily temperature variations and global climate patterns.

Technology Scouting and Innovations in Technology Incubation
Technology scouting and innovations in technology incubation have a symbiotic relationship, fuelling each other for the benefit of budding startups and the broader technology ecosystem. Let’s dive deeper into their individual roles and how they interact:
Technology Scouting
Technology Scouting is the process of discovering, analyzing and evaluating new or existing technologies that will help them with their innovation process. In simple terms, it is a process for companies to find the necessary technology outside the company.
Hunting for promising technologies: Scouts actively search for emerging technologies, patents, research advancements, and market trends across various sectors.
Evaluating potential and fit: They assess the technical feasibility, market potential, and compatibility with the needs of incubators and their supported startups.
Connecting innovators and ventures: Scouts bridge the gap between technology creators (researchers, inventors, startups) and potential users (incubators, established companies).
Fuelling innovation pipelines: Technology scouting helps incubators access a wider range of technologies and ideas, enriching their portfolio and diversifying the innovations supported.

Intelligent healthcare is advancing through the integration of multimodal data, trustworthy AI, federated learning, digital twins, IoT devices and real-time decision-support systems. This chapter reviews the evolution of healthcare-data analysis and examines emerging methods for intelligent diagnosis and predictive care. It discusses explainability, privacy-preserving analytics, interoperability, data governance and ethical frameworks necessary for responsible implementation. Wearable technologies and connected sensors support continuous monitoring, while human–AI collaboration improves clinical decision-making. Case studies illustrate practical healthcare applications and implementation strategies. The chapter also considers global-health requirements, particularly in developing countries, and identifies future opportunities for scalable, equitable, secure and clinically effective intelligent healthcare systems.

This chapter analytically represents the results of the empirical research study with the appropriate discussion on the basis of analysis.

6.1. Results of data collected from moringa growers of Tamil Nadu

6.1.1. Visualisation of descriptive statistics

This Table-6.1 presents 18 independent variables (x1-x18 ) and 5 dependent variables (y1-y5 ) in terms of their descriptive statistics viz. Minimum, Maximum, Range, Mean, Standard Deviation (S.D.), Variance and Co-efficient of Variation (C.V.).

of moringa growers, the maximum years of experience is 44 years and the minimum years of experience is 3 years. The Range of this variable x1 was found to be 41. The mean value of experience was found to be 16.033 years, with the Standard Deviation of 8.324 and Variance of 69.289 for the total distribution taken for the study.

The rapid growth of Large Language Models has produced remarkable advances in artificial intelligence but has also increased computational requirements, energy consumption and environmental impact. This chapter examines the relationship between large-scale AI development, energy efficiency and sustainable computing. Modern AI models may contain billions or even trillions of parameters and require extensive datasets, powerful accelerators and energy-intensive data-centre infrastructure for training and inference. The chapter investigates the environmental consequences associated with model development, including energy consumption, carbon emissions, cooling requirements and resource utilization. It examines model-level optimization techniques such as pruning, quantization and knowledge distillation as methods for reducing computational demands. System-level approaches, including specialized hardware accelerators, energy-efficient data centres, improved cooling systems, renewable energy and intelligent workload scheduling, are also considered. The distinction between energy efficiency and broader sustainability is emphasized, with attention to metrics such as energy per inference, total energy consumption, carbon emissions and hardware lifecycle impacts. The chapter further highlights the importance of transparent sustainability reporting, green computing and governance policies. Sustainable AI development requires balancing model performance and innovation with responsible resource use and long-term environmental objectives

The integration of Large Language Models into software development has created powerful capabilities for automated code generation, debugging, testing and application development. However, AI-generated code can introduce security vulnerabilities when models produce incorrect, unsafe or poorly validated programming solutions. This chapter examines the need for security-aware frameworks that combine LLM-based code generation with verification, rule-based evaluation and risk management. It considers the limitations of conventional rule-based systems, including rule explosion, inflexibility, maintenance overhead and limited personalization, and explores how AI-driven approaches can improve adaptability while maintaining safeguards. Particular attention is given to secure integration in sensitive domains such as healthcare, where generated code and automated analytical systems may influence clinical information and decision-making. The chapter considers data-driven evaluation, machine learning, genomic information and risk detection as components of secure AI systems. It emphasizes the importance of validating generated code rather than treating model output as automatically reliable. Secure deployment requires testing, human oversight, access controls, verification mechanisms and continuous monitoring. The chapter therefore presents safe LLM integration as a multidisciplinary challenge combining AI capability, software engineering, cybersecurity and responsible governance.

AI-enabled livestock technologies can improve productivity, health monitoring, animal welfare and environmental performance, but they also raise significant ethical and governance concerns. This chapter examines data ownership, privacy, algorithmic bias, transparency, accountability, cybersecurity, socioeconomic inequality and animal well-being. Precision livestock systems continuously monitor animals through sensors, computer vision, machine learning and IoT networks, enabling preventive decisions and automated interventions. However, excessive dependence on algorithms may reduce human oversight and treat animals merely as data-producing units. The chapter advocates an interdisciplinary framework for responsible AI that aligns innovation with animal-welfare standards, farmer interests, social values, equitable access, regulatory requirements and long-term environmental sustainability.

Agriculture has progressed from labour-intensive traditional practices through mechanization and the Green Revolution to precision agriculture and Agriculture 5.0. This chapter traces that historical development and identifies limitations associated with conventional farming, including inefficient resource use, environmental degradation and weak adaptability. It explains how GPS-guided machinery, sensors and precision technologies prepared the foundation for intelligent systems. Artificial neural networks, deep learning and fuzzy logic now process data from IoT devices, UAVs and satellites to support automated irrigation, crop monitoring and soil analysis. The chapter emphasizes that integrating human knowledge with computational power is increasingly essential for global food security, sustainability and climate resilience.

Modern farm planning involves nonlinear, discontinuous and multi-objective problems that conventional optimization methods may not solve effectively. This chapter examines Genetic Algorithms, Particle Swarm Optimization and Differential Evolution for improving agricultural resource allocation. These nature-inspired techniques can simultaneously address crop yield, water use, fertilizer application, environmental effects and financial viability. Their integration with deep-learning forecasts and crop-simulation systems enables more realistic optimization. Multi-objective evolutionary algorithms identify Pareto-optimal alternatives, allowing decision-makers to balance productivity and ecological sustainability. The chapter also emphasizes explainable artificial intelligence, economical implementation and farmer usability as necessary conditions for transferring evolutionary optimization methods from experimental research to practical farm management.

Advanced artificial-intelligence models can achieve high predictive performance, but their black-box nature limits clinical trust and accountability. This chapter examines interpretability and explainability as essential requirements for trustworthy healthcare analytics. It reviews intrinsically interpretable models, post-hoc explanation methods, example-based approaches and network-based explanations. A framework combining data preparation, model selection, ensemble learning, calibration, uncertainty estimation and explanation validation is presented. An explainable sepsis-risk prediction case study demonstrates logistic modelling, ensemble methods, Bayesian uncertainty, SHAP attribution and network analysis. The chapter also addresses causal reasoning, adversarial robustness, temporal and multimodal data, human-centred explainability, ethical responsibilities and regulatory requirements for dependable clinical AI.

Factorial experiments provide an efficient method for studying two or more factors simultaneously at different levels. This chapter introduces the concept and notation of factorial arrangements and explains treatment combinations in experiments such as 2 × 2 and higher-order factorials. It distinguishes among simple effects, main effects, and interaction effects, emphasizing the importance of interactions in interpreting biological responses. Factorial experiments allow researchers to examine the effects of individual factors as well as their combined influence. The chapter also explains that factorial treatments may be arranged using different basic experimental designs, such as Completely Randomized, Randomized Block, or Latin Square designs, according to experimental objectives and conditions.

Abstract

FASTA, which stands for “Fast All”, is a sequence alignment tool developed by William R. Pearson and David J. Lipman in 1985. It is commonly used to identify similarities between DNA and protein sequences. FASTA employs a “hashing” strategy to locate matches for short stretches of identical residues with a length of k, referred to as k-tuples or k-tups. This method enables FASTA to quickly search large databases for sequence similarities, making it a popular tool for identifying homologous sequences, predicting gene functions, and analyzing evolutionary relationships. This chapter elaborates some basics of FASTA.

Healthcare organizations require collaborative learning methods that protect sensitive patient information. This chapter introduces federated learning as a framework for training models across decentralized institutions without transferring raw clinical data. Horizontal, vertical and hybrid federated architectures are examined alongside communication protocols, threat models and security requirements. Privacy-enhancing methods—including differential privacy, secure aggregation, cryptographic techniques and trusted execution environments—are discussed. Applications in medical imaging, electronic health-record analysis, wearable devices and remote patient monitoring demonstrate the framework’s practical value. The chapter also evaluates accuracy, privacy, communication cost, scalability and robustness while addressing data heterogeneity, fairness, explainability, legal compliance and ethical challenges in collaborative healthcare analytics.

Finger millet (Ragi/ Mandua) is the principal small millet grown in South Asia. It is believed to be native of India (De condole, 1886) but according to Vavilov (1926) it originated in Abyssinia.

12.1 Meaning and Definition

A flow chart is a graphical or symbolic representation of a process. Each step in the process is represented by a different symbol and contains a short description of the process step. The flow chart symbols are linked together with arrows showing the process flow direction. This diagrammatic representation can give a step-by-step solution to a given problem. Flowcharts are used in analyzing, designing, documenting or managing a process or program in various fields.

Introduction
 
Arid western part of Rajasthan which contains the Indian segment of the Thar Desert, accounts for ~62% of the country’s hot arid area and is a region of low rainfall. Under such physico-climatic situation, fluvial processes function much less frequently and more episodically than the aeolian processes, but their impacts on landforms and society are large, as noticed on several occasions in arid regions. There is a distinct east-west gradient of rainfall and temperature in the region. Along the eastern margin bordering Aravallies, the mean annual rainfall is 500 mm while in the westernmost part at Jaisalmer, it is less than 150 mm. The rainfall is largely monsoon driven which brings 80 - 90 per cent of total rainfall received during the year. There is large inter-annual rainfall variability of rainfall which leads to climatic extremes of drought and floods of different magnitudes. For example on an average, the Thar Desert in India experiences flood-causing rainfall in 2-3 years in a decade, but a maximum of 5 flood causing rainfall years in a decade was recorded during the 1950s, 1970s and the 1990s (Rao et al. 2006).

Cowpea is a quick growing leguminous forage crop. It is usually grown mixed with cereal fodders and grasses to improve the nutritive value of the herbage. It contains 20 – 24 % crude protein, 43 - 49 % neutral detergent fibre, 34 – 37 % acid detergent fibre, 23– 25 % cellulose and 5 – 6 % hemicelluloses on dry matter basis. The digestibility of cowpea fodder is above 70%. Cowpea can be grown under partial shaded conditions. It is an excellent cover crop, which suppresses weeds and enriches the soil. Cowpea requires warm climate with good atmospheric humidity. It can be grown in kharif as well as in zaid season.

Forages: The term forages denote plants that are used for feeding the animals that are cultivated in the farm, or it may be a wild used for stock feeding grown for the vegetative purpose. The vegetative phase is encouraged and the reproductive phase is suppressed.

Introduction

As India is the leading global economy with livestock contributing 4.66% in total GVA to Indian Economy (PIB, 2024) and ranks 1st in Milk Production, 2nd in Egg production, 5th in meat production (BAHS, 2024). Being the largest exporter of milk and meat in the world , this has led to the intensification of animal production due to the increasing demand of major livestock products (MLPs), which has led to an increasing overall use of antimicrobials. Antimicrobials are invariably used to preserve animal and public health, but also as growth promoters at a sub-therapeutic level. The overuse and misuse of antibiotics can contribute to antimicrobial resistance. The growing resistance to antimicrobial drugs could reverse the therapeutic benefits; animals’ resistance to antimicrobial drugs, reduces productivity and leads to economic losses. Unwanted antimicrobials residues may be present in products of animal origins, in animal waste contaminating soil and water and the environment. Seventy-five to ninety percent of antimicrobials used in livestock are excreted, mostly unmetabolized. AMR poses a serious threat to the safety and quality of animal origin food, food security and livelihoods. Unhealthy and unproductive animals are no longer able to generate food products of acceptable safety and quality for human consumption and can no longer contribute to income generation. Insufficient standards for residues in animal food products, reduces the livestock sectors potential of access to trade and increases public health risks (https://www.fao.org/antimicrobial-resistance/key-sectors/animal-production/ en/).

Foundation architectures provide the technological basis for modern artificial intelligence systems and enable models to generalize across diverse tasks and modalities. This chapter examines the evolution of neural architectures that ultimately led to the dominance of the Transformer in contemporary language and multimodal AI. It begins with early neural networks, convolutional neural networks and recurrent neural networks before introducing attention mechanisms and the Transformer architecture. Particular attention is given to the major components of Transformers, including self-attention, multi-head attention, positional encoding and feedforward layers. Encoder, decoder and encoder–decoder configurations are examined through influential models such as BERT and GPT. The chapter further considers Vision Transformers and CLIP, demonstrating the extension of Transformer principles beyond natural language processing. At the same time, limitations such as computational complexity, memory requirements and long-context processing are discussed. Alternative approaches, including state-space models such as Mamba, efficient attention mechanisms, hierarchical architectures and Mixture-of-Experts models, are also explored as promising directions for scalable, efficient and adaptive AI.

Labelling is a critical component of food regulation, consumer communication, traceability, and brand credibility. This chapter examines the regulatory requirements governing ice cream and frozen dessert labelling in India, with emphasis on FSSAI standards and declarations. It explains the distinction between product standards and label declarations and introduces systems for managing recipe masters, nutrition information, label copy, artwork, and packaging approvals. Mandatory declarations, ingredient information, allergen declarations, nutritional information, claims, and common labelling errors are considered. The chapter also identifies departmental responsibilities and best practices for label compliance. Proper regulatory control ensures accurate consumer information, reduces legal risk, and supports consistent packaging and commercial operations.

4.1 Introduction to Function in Python

Python Functions is a block of related statements designed to perform a computational, logical, or evaluative task. The idea is to put some commonly or repeatedly done tasks together and make a function so that instead of writing the same code again and again for different inputs, we can do the function calls to reuse code contained in it over and over again. Functions can be both built-in or user-defined.

Function means sub-program

1. Function definition

2. Function call

Example:

def message: #function definition

print(?hello python”)

message() #function call

Artificial intelligence and machine learning provide the technological foundation for intelligent agricultural applications. This chapter introduces supervised, unsupervised and reinforcement learning, model-development pipelines, feature engineering and evaluation metrics. It explains how algorithms can be selected and implemented for agricultural datasets that may be noisy, incomplete, variable or weakly labelled. Advanced topics include deep-learning architectures, transfer learning and model optimization. Practical considerations involving preprocessing, interpretability and deployment constraints are also discussed. Agricultural case studies illustrate AI applications in crop-yield prediction, plant-disease detection and environmental modelling. The chapter provides the technical foundation needed to design, evaluate and responsibly implement AI-driven agricultural solutions.

Artificial intelligence offers practical solutions to agricultural problems associated with population growth, food demand, climate change and limited natural resources. This chapter introduces machine learning, deep learning, computer vision and natural language processing, together with enabling technologies such as IoT, remote sensing and cloud computing. It explains their applications in crop monitoring, disease recognition, yield forecasting, irrigation, soil assessment and livestock management. AI can improve decision-making, resource efficiency, automation and precision farming while reducing reliance on manual labour. The chapter also examines adoption barriers, including high costs, limited digital literacy, data-security concerns and weak rural infrastructure, and considers future developments in autonomous and climate-resilient farming.

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.

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.