eChapters

Cereal grain contains 60 to 70% of starch and is excellent energy rich foods for humans. In almost every country and region, cereals provide the staple food. In the world as a whole, only 5% of starchy staple food comes from root crops (mainly cassava, potato, and yams, depending on climate), whereas the rest is from cereal. Cereals are an excellent source of fat-soluble vitamin E, which is an essential antioxidant. Whole cereal grains contain 20 to 30% of the daily requirements of the minerals such as selenium, calcium, zinc and copper.

I. INTRODUCTION

Over the last few decades, protection of intellectual property rights (IPRs) has become a global issue. The existing international intellectual property rights regime has been governed by the Trade- Related Aspects of Intellectual Property Rights (TRIPS) Agreement.1 Inspite of maturity of intellectual property laws in post TRIPS era it still desiderate to established coherence with human rights laws particularly right to health and maintain a distance with the socio-economic dimension of intellectual property protection. On the other hand the international human rights instruments from the very beginning appear to be considerable to make certain links with intellectual property rights.2 The issue that is now debated that the adoption of the TRIPS Agreement provisions have serious impact on the realization of certain basic human rights such as the right to health because of non availability and affordability of much needed medicines for the poorer populations of the world particularly in majority of developing countries. Meanwhile TRIPS flexibilities as confirmed by the Doha Declaration on Public Health were adopted in 2001 by the global community to address medicines as public health issue.3 Keeping in view the above developments the problem of right to health and the relationship between medical patents and access to medicine in the case of the HIV/ AIDS and T.B epidemics is complex challenge in the context of the 2030 Agenda for Sustainable Development Goals in 2015 adopted by Member States of the United Nations in Sep, 2015. One of the aims of the agenda is to ensure healthy lives and promote the well-being of people of ages.4 In such a situation it is high time to revisit the existing intellectual property regime in the broader context to protect everyone who is likely to be negatively affected by intellectual property standards imposed by developed nations. Under this backdrop the present paper wishes to examine the various kinds of potential impacts of intellectual property regime on the realization of access to medicine as part of human right to health as universal entitlements amongst the most disadvantaged and marginalized peoples and communities in the broader context of sustainable development goals.

Agricultural robotics addresses labour shortages, production demands, environmental concerns and the need for greater operational precision. This chapter examines unmanned aerial vehicles, autonomous ground machines, robotic manipulators, greenhouse robots and livestock systems. Enabling technologies include multimodal sensors, computer vision, navigation, control systems and artificial intelligence. Applications extend from land preparation, sowing and crop monitoring to weeding, spraying, irrigation, harvesting and post-harvest processing. Emerging approaches such as reinforcement learning, digital twins, swarm coordination and soft robotics are also explored. Technical reliability, high investment, connectivity, labour displacement and data ownership remain important barriers. Future development must prioritize accessible, affordable and farmer-oriented automation platforms.

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.

The term robot is czech word, first time used by play write Karel Capek, in year 1920, applicable to the forced labour or serfs or slaves. The first commercial, digital and programmable robot, named ‘Unimate’ was built by George Devol in 1954.

2.1 Introduction

Automation of agricultural field with IoT and microcontroller is new era of technology. Arduino based solution to agricultural field is open source platform. It can be used for developing data loggers and new sensors environment. The rate of solutions with Arduino and sensors is high as it is open source platform. The use of Arduino in agriculture can accelerate the growth of automation. Arduino can be interfaced with sensors like:

• Temperature
• Humidity
• Soil Moisture
• Water Pressure
• Water Flow and Water Metering
• Rain Gauge

Data science supports precision agriculture by converting large and varied agricultural datasets into actionable knowledge. Information from soil sensors, weather stations, satellites, drones and smart machinery can guide field preparation, sowing, irrigation, fertilization, pest control and harvesting. This chapter examines data collection, storage, analytics, machine learning, predictive modelling and real-time IoT monitoring. It explains how these tools can optimize water, nutrients, energy and other agricultural inputs while improving productivity and sustainability. Implementation challenges include high costs, insufficient technical knowledge, privacy concerns and inadequate digital infrastructure. Emerging cloud, edge, blockchain and artificial-intelligence technologies are discussed as mechanisms for advancing climate-smart and data-driven agricultural management.

Land resources units or major land resources units are the suitable segmentations of land, based on available natural resources, their potential use and management. Irrespective of terms used by different organizations or institutes, these units have spatial dimension and represent a composite unit for interstate, regional, and national level planning. A typical meaning of these land units can be related to terms like land system. Christian and Stewart (1953) of the Commonwealth Scientific and Industrial Research Organization (CSIRO), Australia devised the ‘Land system’ as the composite mapping-unit which has been defined as “an area or group of areas, throughout which there is a recurring pattern of topography, soils and vegetation”. This system is based on the premise that the land system is expressed on aerial photographs by a distinctive pattern and, as such, the land system can be directly mapped from aerial photographs (Christian and Stewart 1953, Mabbutt and McAlpine 1965). It is a fact that during those period, aerial photographs were more frequently used and studies related to land systems used technique of aerial photogrammetry. As per USDA (United States Developmental Agency), the typical segmentations are; (1) Land Resource Region (LRR), which is the highest level in the hierarchy (2) Major Land Resource Area (MLRA), the second highest level and generally representing broad landforms or a geologic region at a small scale and (3) the Land Resource Unit (LRU).

3.1 Introduction

Internet of Things (IoT) allows computational devices and sensory support to connect with each other and access services on the Internet. The IoT idea was introduced to connect devices through the Internet and facilitate access to information for users. The potential application of IoT includes agriculture. The aim of this chapter is to explain the role of NodeMCU in the agriculture field. NodeMCU is IoT module which can be used to present the IoT concept as a basis for monitoring and control. The systems used in farm production processes. NodeMCU play a key role, with a focus on their realization by available microcontroller platforms and appropriate sensors. IoT based system provides opportunity to users to monitor and control the process remotely.

Abstract

This study evaluates the potential of remote sensing techniques for assessing the relationship between land degradation and poverty in Pali development block of Rajasthan. Due to climatic conditions that prevail in this area of the country, less facilities and management practices to conserve land and its fertility becomes an important factor for intensifying poverty. The other countenance of this is that it is poverty which leads to land degradation. This paper assesses the impact of poverty and land degradation on each other. The desertification intensity and BPL density is calculated and relation is seen using Chi square test. The goodness of fit is seen up to 5 per cent level of significance for land degradation and demographic data.  The desertification in agricultural area is also considered. The total degradation in the block calculated is 8.39 per cent. It is found that village with highest BPL density has more degradation intensity particularly in agricultural area, proving the agricultural based economy of the dry lands in Rajasthan. An example study is also done at village level.  Using cadastral map and satellite image classification, fields that require immediate attention have been identified. Demographic and degradation maps are also prepared.

Introduction 
The transformative potential of digital technologies in reshaping governance and development paradigms is increasingly evident, particularly in rural contexts. In India, where nearly 65% of the population resides in rural areas, the imperative to leverage digital tools for inclusive growth has never been more critical. Rural digital governance refers to the strategic use of information and communication technologies (ICTs) to enhance service delivery, improve livelihoods, and foster participatory development in rural areas. It encompasses various applications from digital health, education, and financial inclusion to precision agriculture, market linkages, and entrepreneurial enablement, each designed to address long-standing structural inequities. Historically, rural regions have been perceived as passive recipients of policy support and welfare interventions. However, with the advent of digital technologies, these areas are increasingly positioned to become both demand drivers through rising incomes and service consumption and supply hubs, contributing to national productivity through digitally enabled livelihoods and enterprises. By enhancing access to essential services, improving the efficiency of traditional sectors such as agriculture and handicrafts, and facilitating diversification into non-farm activities like rural tourism and agri-processing, digital governance catalyzes rural transformation. However, the success of rural digital governance is contingent upon adopting a holistic and contextualized approach. One-size-fits-all digital interventions often fail to address the nuanced realities of rural economies. Effective rural technology deployment requires carefully diagnosing local needs, infrastructural capabilities, and livelihood profiles. In this regard, a participatory approach to technology selection and design grounded in the realities of rural life ensures greater relevance, uptake, and impact. Publicprivate partnerships (PPPs) play a pivotal role in scaling such initiatives, with governments providing scale and legitimacy while private actors contribute innovation, technical know-how, and financial investment.

Sales and distribution determine whether a technically excellent ice cream product successfully reaches consumers in the required condition and at the right time. This chapter examines the architecture of ice cream distribution in India, including factory dispatch, CFAs, distributors, retailers, parlours, modern trade, quick commerce, and online channels. It discusses route-to-market design, presales, delivery systems, distribution management, retail execution, outlet classification, freezer deployment, and channel economics. Product flow and information flow are considered together because sales data, stock information, complaints, freezer conditions, and market feedback influence production and planning decisions. The chapter provides practical insights into distribution KPIs, channel management, cold-chain discipline, and strategies for sustainable market growth.

Abstract
 
Mention about the Great Himalayan river of Vedic Period (10000-8000 BC) is found in ancient Indian literature. The river called as ‘Saraswati’ in India and ‘Hakra’ in Pakistan, was flowing with full majesty through Thar Desert around 6000 BC and dried up during 3000 BC due to climatic and tectonic changes in Himalayan region. The relict of this lost river is still found as palaeochannels in this region. Large numbers of archaeological sites of Harappan civilization occur along the dry river bed. 

Abstract

This paper sets out to describe key results and experiences obtained during the assessment of the crop response to water which varies strongly from crop to crop depending on its specific genetic characteristics and adaptation to ecological conditions. The specific crop water information for the different crops is compiled in a crop-based data base. This includes crop water parameters, yield response to water, as well as selection of economy crops. Existing natural water resources can sustain the crop production with high yield as well as economic value with minimal environment impact. Most of the crop varieties get all of its required water from rainfall- water but due to climate change there is always a demand of irrigation water from the ground water resources. Thus, there is a need to evaluate the crop water information in relation to ground water extraction as a source of irrigation.

5.1 Introduction
The Earth’s atmosphere is a vast, dynamic, and interconnected system, constantly in motion and influencing every aspect of our lives. Accurately observing and understanding this complex system is fundamental to predicting weather, monitoring climate change, and responding to environmental challenges. While ground-based platforms provide invaluable high-resolutiondata at specific locations, theitr  spatial coverage is inherently limited. This is where satellite remote sensing emerges as a revolutionary tool in meteorology. By observing the Earth and its atmosphere from space, satellites offer an unparalleled vantage point, providing global and continuous coverage that is impossible to achieve with ground-based instruments alone. They can capture data from remote oceans, deserts, and polar regions, offering a truly holistic view of atmospheric processes.
Satellite remote sensing enables meteorologists to measure a wide array of atmospheric parameters, including temperature and humidity profiles, cloud properties (type, height, movement), wind fields, precipitation rates, and the distribution of atmospheric gases and aerosols. This wealth of data is crucial for initializing and validating numerical weather prediction models, tracking severe storms, monitoring long-term climate trends, and conducting fundamental atmospheric research. This chapter will delve into the fascinating world of satellite remote sensing in meteorology, exploring the principles behind how these orbiting observatories collect data, the various types of meteorological satellites and their specialized instruments, and the diverse applications of the information they provide for understanding and forecasting
our planet’s weather and climate.

10.1 Introduction

The objective of this chapter is to develop a data logger and indicator for fire detection in agriculture field. The data logger is designed with the help of Scilab. The system comprises of Ardiuini Nano, light sensor, MQ6, MQ135, IR sensor, temperature sensor, power supply. The system is designed to take sensory data on Scilab with serial communication. Fig.10.1 shows block diagram for a system. Table.10.1 shows the component list required to develop the system.

The growing integration of Large Language Models into healthcare, education, business and cybersecurity has created new opportunities while introducing significant security and privacy risks. This chapter provides a comprehensive examination of vulnerabilities across the LLM lifecycle, from training and inference to deployment and integration with external applications. It discusses threats including prompt injection, jailbreak attacks, model extraction, membership inference, privacy-attribute inference and adversarial prompting. Deployment environments introduce additional risks involving unauthorized API access, misconfiguration, insecure integrations, logging-related data leakage and supply-chain attacks. The chapter also considers availability and integrity threats, including denial-of-service attacks and attempts to manipulate or remove AI-generated content watermarks. Privacy protection mechanisms such as differential privacy, anonymization, de-identification and encryption are examined as potential safeguards. Legal and regulatory considerations are also addressed, including data protection requirements and organizational governance. Because LLM applications may process sensitive personal and business information, security cannot be treated as an isolated technical feature. The chapter emphasizes continuous monitoring, secure architecture, privacy-preserving techniques, access controls and responsible governance for trustworthy LLM deployment.

Introduction

Spatial variation structure of soil properties is a key input for digital soil mapping and generally determined through geostatistical techniques (Webster and Oliver 2007). In geostatistics, spatial variation is expressed by semivariogram ?(h) , which measures the average dissimilarity between data separated by a vector h. It is generally computed as half of the average squared difference between the components of data pairs:

One of the major goals in agricultural crop management is to achieve maximum yield with minimum investment while maintaining a good quality of the crop produce. Crop infestation due to insects, diseases, and weeds are some of the major factors responsible for loss in crop yield. Further, an efficient Precision Agriculture Crop Management System is one, which is capable of precisely detecting the crop yield indicators (or crop health parameters) at an early stage and can predict the yield at any point of time of the crop cycle.

Sensor is a physical device used to detect manifestations or variations in a process value and collect data in real time assessment mode for measuring and predicting system/device performance and finally analyzing it to attain a specific task or application. Sensor technology is now-a-days used for interdisciplinary applications for data collection, system analysis and monitoring.