eChapters

Artificial intelligence can detect subtle changes in animal health before they become apparent through conventional observation. This chapter examines machine learning, deep learning and predictive analytics for livestock disease prediction and diagnosis. Models use physiological signals, diagnostic images, behaviour patterns, environmental conditions and historical records to recognize abnormalities and estimate disease risk. Wearable sensors and IoT devices enable continuous monitoring and early-warning systems, supporting preventive veterinary care and outbreak management. AI-based detection can reduce mortality, treatment costs and production losses while improving decision-making. The chapter also evaluates challenges involving inconsistent data, model interpretability, rural infrastructure, technical accessibility and the practical implementation of diagnostic systems.

Animal behaviour provides valuable information about health, welfare, reproduction, environmental adaptation and social interaction. Traditional observation is labour-intensive, time-consuming and vulnerable to observer bias. This chapter explains how machine learning automates behavioural analysis using video, sensor and bioacoustic data. It covers preprocessing, feature extraction, classification, clustering and recurrent neural networks for temporal analysis. Computer-vision techniques—including object detection, tracking and pose estimation—enable continuous and non-invasive monitoring. Case studies demonstrate applications in livestock management, wildlife conservation and laboratory research. The chapter also examines data labelling, model generalization, ethics, explainable AI, real-time processing and the integration of multiple behavioural data sources

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.

Robotics is transforming livestock production by automating repetitive, labour-intensive and precision-dependent operations. This chapter examines automated milking, robotic feeding, manure removal, health monitoring, drones and autonomous herd-management systems across dairy, poultry, swine and sheep production. Integration with AI, IoT and sensors creates connected environments capable of continuous monitoring and data-driven action. Benefits include higher productivity, lower labour requirements, timely animal care and more efficient resource use. Challenges include substantial investment, technical complexity, maintenance and farmer acceptance. Emerging developments such as autonomous farms, swarm robotics, solar-powered equipment and climate-smart robotic systems may further strengthen animal welfare, environmental sustainability and global food security.

14.1 Introduction
As we reach the culmination of our exploration into meteorological data science, from foundational principles to specialized applications and emerging trends, it is imperative to pause and reflect on the broader context of this rapidly evolving field. The chapter, “Challenges, Ethics, and the Future of Meteorological Data Science,” shifts our focus from the “how” to the “why” and “what next,” addressing the significant hurdles, crucial ethical considerations, and transformative opportunities that lie ahead. The journey through meteorological data science has revealed a landscape of immense progress: unprecedented data volumes, sophisticated modeling capabilities, and increasingly accurate predictions. However, this progress is not without its complexities. We grapple with the inherent chaotic nature of
the atmosphere, the persistent uncertainties in our models, and the sheer computational demands of simulating Earth’s intricate systems. Beyond these scientific and technical challenges, the increasing societal reliance on meteorological data raises profound ethical questions concerning data privacy, equitable access to information, the responsible use of AI, and the potential for bias in algorithms. This chapter will delve into these multifaceted aspects. We will first examine the enduring scientific and technical challenges that continue to push the boundaries of research, from improving sub-seasonal to seasonal predictability to seamlessly integrating diverse data sources. Following this, we will confront the critical ethical considerations that arise when dealing with vast amounts of environmental data and its direct impact on human lives and livelihoods. This includes discussions on data ownership, algorithmic transparency, and the potential for exacerbating existing societal inequalities if not handled with care. Finally, we will cast our gaze forward, envisioning the future of meteorological data science. This forward-looking perspective will explore the potential for truly integrated Earth system models, the transformative impact of artificial intelligence, and the growing imperative for interdisciplinary collaboration to address the grand challenges of climate change and environmental sustainability. Ultimately, this chapter serves as a critical reflection, reminding us that while the technological advancements in meteorological data science are aweinspiring, their true value lies in their responsible and ethical application for the benefit of all. It is a call to continuous innovation, thoughtful leadership, and a commitment to leveraging the power of data for a more resilient and informed future.

The increasing deployment of Large Language Models across education, healthcare, finance, employment and public services has made fairness and ethical responsibility central concerns in AI development. LLMs learn from enormous datasets that may contain historical inequalities, stereotypes, cultural assumptions and other forms of social bias. This chapter examines the origins, manifestations and consequences of bias in LLMs and considers approaches for promoting fair and responsible deployment. It discusses data bias arising from the composition and curation of training datasets, algorithmic bias introduced through model architectures and learning processes, and bias associated with human-in-the-loop systems. Gender, racial and cultural biases are considered because they can influence generated outputs and potentially reinforce existing inequalities. The chapter also examines fairness evaluation, mitigation strategies, transparency, accountability and human oversight. Ethical concerns extend beyond bias to hallucination, misinformation, privacy and inappropriate automation. Effective governance requires continuous monitoring rather than one-time assessment because model behaviour may change across applications and populations. The chapter therefore emphasizes responsible AI principles that place fairness, transparency, accountability, safety and human interests at the centre of LLM development and deployment.

15.1 Data Visualization

Data visualization is the graphical representation of information and data. By using different visual elements like charts, graphs and maps. Data visualization tool provides easy way to see the understand the data and related trend/pattern.

1. There are multiple tools available for data visualization using which we can perform data visualization.

2. In python we can use a library called “matplot” for data visualization.

3. Python based plotting library “matplotlib” enables us to produce 2D as well as 3D graphics i.e. charts/graphs.

4. Pyplot interface of matplotlib is used for representing 2D graphs.

Introduction 
The evolution of public administration in the digital age has been anything but linear. As governments worldwide grapple with waves of technological advancement, the boundaries between reform phases such as e-government, e-governance, digital government, and digital governance often blur. Transformations occur unevenly across sectors and regions, with older paradigms coexisting with or even outlasting newer innovations. As Dunleavy, Margetts, Bastow, and Tinkler (2006, p. 216) perceptively observe: “Defining periods in the evolution of any complex system, such as public management systems in advanced industrial countries, is tricky. New developments accumulate while older trends are still playing out and flourishing. Relatively established ideas move from leading-edge countries or sectors to implementation in previous laggard areas, even as the same ideas are being repudiated or reversed in the erstwhile pioneering locations. Moreover, a confusing welter of changes goes on simultaneously, amongst which it is difficult to distinguish ephemeral and hyped-up innovations from those that are fundamental and longerlasting”. This observation aptly captures the administrative reforms’ non-linear and layered character in the digital era. The coexistence of multiple reform paradigms ranging from early e-government initiatives focused on service delivery, to broader e-governance aimed at leveraging technologies for participatory governance, and finally to the strategic integration seen in digital governance illustrates how public sector innovation is cumulative and contested. Digital governance, in particular, cannot replace previous phases. Instead, it serves as their expansion and convergence, wherein technology, data, and citizen-centricity are embedded in policymaking, service design, and institutional accountability. To fully understand the rise of digital governance, examining the broader historical trajectory of administrative reforms is important. Since the 1960s and 1970s, such reforms have consistently remained a focal point for both

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

I. ROLE OF INTELLECTUAL PROPERTY

The international community has set itself the target of reducing the proportion of people below poverty line to half by 2015, along with associated specific targets for improving health and education and environmental sustainability. The Millennium Development Goals, adopted at various international fora, recognize the crucial importance of reducing poverty and hunger, improving health and education, and ensuring environmental sustainability. It is important to know whether and how intellectual property rights (IPRs) could play a role in helping the world meet these targets — in particular by reducing poverty, helping to combat diseases, enhancing access to education and contributing to sustainable development, protecting our cultural heritage, safeguarding the food security for the country. It is also necessary to deliberate whether and how they create obstacles to meeting those targets and, if so, how those obstacles can be removed.

There are strong views from both sides on the protection and enforcement of IPRs though there is no data to check these views objectively. Some argue strongly that IPRs are necessary to stimulate economic growth, which in turn, helps in poverty reduction. By stimulating invention and new technologies, IPRs help to increase agricultural or industrial production, promote domestic and foreign investment, facilitate technology transfer and improve the availability of medicines necessary to combat diseases. They are also of the view that there is no reason why a system that has worked for developed countries would not work for developing countries. Others argue equally vehemently the opposite. IPRs do little to stimulate invention in developing countries, because the necessary human and technical capacity may be absent there. In assessing these opposing arguments, it is important to remember the technological disparity between developed and developing countries as a group. What role the intellectual property can play to plug this gap is a serious subject for every developing nation? For this, it is also pertinent to know about the IPRs, the challenges they pose to the legal system in general and to the Indian legal system in particular, and their basic rationale to take a considered approach in their implementation.

I. INTRODUCTION

Procedural remedies for the infringement of one’s right are not enough by themselves unless they result in some reliefs of substantive nature whereby it can reasonably be said that these reliefs, temporary or permanent, as the case may be, are enough in the circumstance to make good or, say, compensate the genuine loss of the aggrieved party to an infringement suit. The substantive remedies available to a successful plaintiff at any stage in any suit for patent infringement could, however, be derived from the express as well as by necessary implication of the provisions of the Patents Act, 1970 dealing with the “Reliefs in suit for infringement”. Its Sec. 108 declares in terms quoted below:

Big Data refers to extremely large, very fast, highly diverse, and complex data that cannot be managed with conventional data management systems, tools or processes. In the perfect world, Big Data may include all kind of data, which can deliver the right information, to right person, in the right quantity, at the right time, to help in making the right decisions.

Agricultural drones have emerged as important tools for precision farming, crop surveillance, and field-level data acquisition. This chapter introduces drones as aerial intelligence platforms capable of collecting high-resolution spatial and spectral information about crops, soil, and environmental conditions. It discusses drone architecture, sensing technologies, remote sensing, crop monitoring workflows, and crop health assessment. Artificial intelligence and machine learning enable efficient analysis of aerial imagery for disease detection, irrigation management, precision spraying, crop stress assessment, and yield prediction. The chapter also examines autonomous drones, drone swarms, IoT integration, edge computing, and digital twins. Operational limitations, regulatory considerations, data management, and sustainability implications are also addressed.

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.

Remote sensing and Geographic Information Systems provide spatial information for monitoring crops, land, soil and water resources. This chapter introduces their fundamental principles and explains developments involving satellites, UAVs, hyperspectral sensors, thermal imaging and cloud-based geospatial platforms. Analytical techniques include vegetation indices, time-series analysis, spatial modelling and artificial intelligence. Applications cover crop-health monitoring, yield forecasting, irrigation planning, drought assessment, pest and disease identification and soil-resource management. Integration with IoT, automation and other digital technologies improves the timeliness and resolution of agricultural information. These tools support evidence-based decisions responding to food-security, climate-change and natural-resource sustainability challenges.

Reliable artificial intelligence depends on accurate, timely and multidimensional livestock data. This chapter examines the transition from manual record-keeping to sensor-based systems that capture physiological, behavioural, production and environmental information. Data sources include wearable devices, IoT monitoring networks, computer-vision systems and environmental sensors. Their applications include monitoring health, welfare, movement, feeding and productivity. The chapter also considers the integration of heterogeneous datasets and persistent challenges involving missing values, noise, interoperability, scalability and system reliability. By connecting data generation with intelligent analysis, it emphasizes that robust acquisition protocols and dependable infrastructure are essential for developing effective, evidence-based and responsive livestock-management systems.

Cloud computing and Edge AI provide complementary infrastructure for intelligent livestock management. Cloud platforms offer scalable storage, centralized analytics, model training and integration of data from sensors, cameras, automated feeders and farm-information systems. Edge AI processes information close to the farm, reducing latency, bandwidth consumption and response time. This chapter explains their architectures, workflows and applications in dairy, poultry, swine and small-ruminant production. Uses include disease detection, estrus prediction, behavioural monitoring, feeding and environmental control. Hybrid cloud-edge systems balance centralized intelligence with local autonomy. Challenges involving connectivity, interoperability, cybersecurity, cost, model reliability and ethical animal surveillance are also critically examined.

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.

Computer vision provides an important foundation for automated plant analysis, monitoring and precision agriculture. This chapter explores computer vision approaches for plant phenotyping, disease detection, growth monitoring and yield prediction. Traditional image-processing methods are considered alongside machine learning and modern deep learning techniques, including convolutional neural networks and vision transformers. Image segmentation, object detection and classification are used to extract meaningful information from plant images and video data. The chapter also examines multispectral and hyperspectral imaging, IoT integration and real-time monitoring. Environmental variability, dataset quality, computational complexity and model generalization are discussed, together with transfer learning, data augmentation, edge computing and future directions

Precision agriculture uses spatially and temporally variable information to improve crop management, resource efficiency and agricultural productivity. This chapter examines the integration of artificial intelligence throughout the precision agriculture workflow. It introduces variable-rate technologies, georeferenced data collection and decision-support systems, followed by machine learning, deep learning, computer vision, reinforcement learning, fuzzy logic and expert systems. Applications include crop yield prediction, plant disease monitoring, soil property estimation, irrigation planning and autonomous agricultural operations. Remote sensing, satellite imagery, IoT sensor networks and edge computing are considered for real-time decision-making. The chapter also discusses agricultural robots and autonomous machinery, practical field applications, data limitations, hardware costs, connectivity and model interpretability.