eChapters

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.

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.

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 identification of crop diseases enables timely management interventions and can reduce losses in agricultural production. This chapter investigates the use of artificial intelligence and sensor data for detecting crop diseases at an early stage. It introduces the fundamentals of crop disease detection and reviews AI techniques applicable to agricultural monitoring. Sensor technologies provide continuous information about crop and environmental conditions, while AI models analyze these data to identify disease-related patterns. The chapter presents an AI- and sensor-based framework, including implementation and experimental procedures, followed by performance analysis. Challenges and limitations are examined, together with future research directions aimed at improving accuracy, reliability and real-time disease monitoring.

Plant diseases significantly reduce crop productivity, quality, and economic returns, making early and accurate detection essential for effective crop protection. This chapter examines the application of machine learning and computer vision for identifying diseases at an early stage. It discusses agricultural image acquisition using RGB, hyperspectral, and sensor-based technologies, followed by image preprocessing, segmentation, feature extraction, and classification. Machine learning algorithms such as support vector machines, random forests, and convolutional neural networks are considered for disease recognition. The chapter also explores the integration of AI with IoT and mobile technologies and presents practical applications while addressing challenges involving datasets, environmental variability, model generalization, and computational requirements.

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.