eChapters

Effective crop monitoring is essential for maintaining plant health, optimizing farm operations, and improving agricultural productivity. This chapter explores the application of artificial intelligence for real-time crop monitoring and farm management. AI systems integrate information from sensors, satellites, drones, mobile devices, and other sources to assess crop conditions and identify emerging problems. Machine learning and deep learning techniques can analyse crop images, environmental parameters, growth patterns, and field conditions to provide actionable insights. The chapter examines real-time monitoring, image analysis, crop health assessment, anomaly detection, and decision-support applications. It also highlights the role of IoT and multimodal agricultural data in developing intelligent and responsive farming systems.

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.

Early and accurate detection of plant diseases is essential for protecting crop productivity and reducing agricultural losses. This chapter explores artificial intelligence-based plant disease detection using image processing and deep learning. It introduces major plant diseases and the fundamentals of image processing before examining deep learning approaches for identifying disease symptoms from plant images. AI models can automate disease recognition and support timely interventions in smart agricultural systems. The chapter presents a proposed methodology and discusses results and analysis, practical applications and limitations. It also considers challenges associated with data quality, environmental conditions and model performance, followed by future directions for developing more accurate, scalable and intelligent plant disease diagnostic systems

Plant phenotyping is essential for understanding plant growth, development, morphology, physiology and responses to environmental conditions. This chapter examines how artificial intelligence is transforming conventional phenotyping through automation, high-throughput data collection and advanced analysis. It covers image-based, UAV-based and sensor-based phenotyping, together with genotype-to-phenotype modelling and machine learning and deep learning methods. Imaging systems, spectral sensing, drones, sensors and robotic platforms provide diverse sources of plant information for AI analysis. Applications include trait measurement, disease and stress detection, crop performance prediction and resource management. The chapter also discusses limitations, emerging technologies, digital twins, IoT, cloud computing and future opportunities for scalable and sustainable phenotyping.

Soil health and nutrient availability are fundamental determinants of plant growth, crop productivity and sustainable agricultural production. This chapter examines how artificial intelligence can support soil health monitoring and nutrient management. It introduces soil health concepts and nutrient dynamics before exploring AI technologies capable of processing soil, environmental and crop-related datasets. AI-based approaches can assist in identifying soil conditions, assessing nutrient requirements and developing site-specific nutrient management strategies. Integration with precision agriculture technologies can improve fertilizer-use efficiency and reduce unnecessary inputs. The chapter also considers practical case studies, challenges and future opportunities. By combining AI with agricultural data, soil monitoring can become more timely, precise and useful for informed crop management.

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

ABSTRACT

This paper presents an outline on the patenting system prevailing in India. 

INTRODUCTION

Ideas and knowledge are increasingly important parts of trade. Most of the value of new medicines and other high technology products lies in the amount of invention, innovation, research, design and testing involved. Hence, protection of such values is paramount. This paper details how these values are protected in India.

Analytical testing provides objective evidence of the composition, safety, physical structure, and sensory quality of ice cream. This chapter introduces the major analytical approaches used in industrial ice cream manufacturing, including chemical, physical, microbiological, and sensory evaluation. Important parameters such as fat, milk solids-not-fat, protein, sugars, ash, acidity, pH, overrun, meltdown, fat destabilization, texture, and product weight are discussed. The chapter explains how different tests support routine quality control, product development, troubleshooting, research, regulatory compliance, and shelf-life studies. It emphasizes that no single analytical parameter can define overall quality. Effective product evaluation requires integrated interpretation of chemical, physical, sensory, microbiological, and storage data.

Setting

The basic difference which dovetails most of the animal breeding experiments is the unbalancedness of the data collated with respect to factorial classes thereby making non-orthogonal nature of the effects involved ubiquitous. Henderson, C.R. (1953), through a series of seminal works dealt with these types of data and came out with three distinct methods to estimate the variance components emanating out of such non-orthogonal animal experiment data. Before discussing those methods, let us delve a bit on the challenges in estimating the variance components in animal breeding data. There are two major reasons, first, several methods of estimation are available (most of which are reduced to the analysis of variance method for balanced data), but not one of them has yet been clearly established as superior to the others. Second, all the methods involve relatively cumbersome algebra; discussion of unbalanced data can therefore easily deteriorate into a welter of symbols, a situation which functions as the anti-appetizer to applied researchers.

A safe starting point towards understanding the nuances of dealing with unbalanced data could be the paper by Henderson (1953) which is quite seminal in nature. The methods described have been often referred to as the gold standard in the area of estimating variance components from unbalanced data for many years to come. Of the three, method 1 is simply an analogue of the analysis of variance method used with balanced data and method 2 is used to correct a deficiency of method 1 that arises out of mixed models and the last one is quite different and involves the method of fitting constants so often used in fixed effects models. Let us see these methods in a bit more detail in the ensuing passages.

Analysis of Covariance (ANCOVA) combines features of analysis of variance and regression analysis to account for the influence of a concomitant or covariate variable. In forestry and horticultural experiments, initial measurements or other associated characteristics may influence the response variable and contribute to experimental variation. This chapter explains how treatment effects can be evaluated after adjusting for the linear relationship with a covariate. It presents the ANCOVA framework, calculation of sums of squares, regression coefficients, adjusted error and treatment variation, F-tests, and adjusted treatment means. Examples involving seedling growth and physiological responses demonstrate how covariance adjustment can provide more precise comparisons among treatments.

Analysis of Variance (ANOVA) is an important statistical technique for separating variation attributable to different sources in an experiment. The chapter explains how total variation can be partitioned into controlled and uncontrolled components and how experimental error is used to assess treatment differences. The F-test provides the basis for testing the homogeneity of several treatment means simultaneously. The chapter discusses the assumptions underlying ANOVA, including independence, normality, additivity, and homogeneity of variance. It also introduces fixed- and random-effects models and explains their relevance to experimental analysis. Forestry and agricultural examples demonstrate the application of ANOVA for interpreting treatment and experimental effects.

Throughout human history, people across the globe have sought to understand and predict the ever-changing skies. From the scorching deserts of North Africa to the lush rainforests of South America, and from the snow-capped Himalayas to the vast Australian outback, ancient cultures developed unique methods to forecast the weather. These traditional knowledge systems, often passed downthrough oral traditions and  storytelling, reflected the specific environmental contexts, mythological beliefs, and spiritual practices of each community. In ancient China, royal astronomers meticulously recorded weather patterns and planetary movements to guide agricultural planning and imperial decisionmakingIn Greece, philosophers like Aristotle and Theophrastus wrote extensively on weather signs and forecasting methods. In Africa, tribal communities observed animal migrations and plant behaviors to anticipate seasonal changes. Native American tribes used lunar cycles and star constellations to predict weather patterns, while ancient Indians studied the movements of planets and nakshatras (lunar mansions) to forecast monsoon rains.

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

Veterinary oncology is an upcoming field in recent years due to the integration of the importance of spontaneous canine cancers in understanding and developing novel strategies for human cancer management. We are stepping towards a very exciting era in veterinary oncology, where comparative oncology has played a significant role for many decades in understanding the molecular basis of cancer in companion animals. The comparative animal models have generated remarkable interest in both academia and industry. Studies have demonstrated the utility of comparative oncology in understanding drug resistance and tumor biology, contributing to the translational research[1]. This has yielded breakthrough discoveries, from advanced diagnostics to novel therapies, providing opportunities to improve the longevity and quality of life of the pets. These inputs have led to advancements such as the first conditionally approved chemotherapeutic agent for lymphoma, novel BRAF testing for canine urothelial carcinoma, tyrosine kinase inhibitors for mast cell tumors, updates in stereotactic/FLASH radiation, and novel immunotherapeutics. As the understanding of cancer biology has advanced, the field of veterinary oncology has grown significantly, paralleling developments in human oncology. Veterinary oncologists treat a variety of cancers in domestic animals, particularly dogs and cats, but also in exotic animals, horses, and even zoo animals. While the types of cancers in animals share similarities with human cancers, treatment strategies can differ significantly due to species-specific differences in anatomy, physiology, and response to therapies [2, 3].

AMR is undoubtedly the most serious challenge ever faced by the world health community because of its multifaceted complex nature and its overwhelming impact over the human and veterinary communities. Livestock sector is no exception to have the serious and devastating impact from the emergence and spread of AMR. Because of a steady shift towards intensive farming and increased stocking density there has been increase occurrence of infectious diseases. Growing world population, rapid urbanization, and deforestation, shrinkage of grazing land all aided to this process. This leads to increased usage of antimicrobials in food animals – cause of concern for the scientific community as AMU is known the single most important driver of AMR.

Asiatic Elephant (Elephas maximus) is the national heritage animal of India. This particular species now exists in 13 Asian countries including India. As per 2017 census, India is home to an estimated figure of 29,964 elephants that is >60% wild population of the globe. Once distributed across the country, now they are squeezed to 14 states grouped in four regions ie., southern (14,612), north-eastern (10,139), east-central (3,128) and northern regions (2,085). There are 2675 captive elephants across different states under both private and government custody. This largest terrestrial mammal is protected under Seclude I species of Indian Wild Life Protection Act, 1972.

Both central and state Government are implementing short- and long-term programmes for health and well-being of captive as well as free-range elephants. The Central Government has established an ‘Elephant Cell’ at the Wildlife Institute of India (WII), Dehradun to provide technical inputs to the ministry for strengthening the efforts towards conservation of elephants. Add-on, the ‘Project Elephant’ was launched in 1991-92 in the Ministry of Environment, Forests and Climate Change to restore the elephant habitats and reduce their sufferings. Elephant habitats are notified as Elephant Reserves that are basic management units for elephant conservation and managements. A total of 33 Elephant Reserves has been notified in the country. A book titled, ‘Caring for elephants: Managing health and welfare in captivity’ is edited for field officers, veterinary professionals and elephant handlers.

Need of Analytics in Fisheries Management

New technologies are introduced for industrial vessels every day: Fuel and gear sensors, cameras, electronic catch reports etc. There is a global push to track small-scale fishing vessel. In the coming years, fisheries monitoring centers will have to adjust from tracking a few hundred vessels to tens of thousands of vessels, meaning huge quantity of data. To make sense of all this information, it must be properly analysed. The future of fisheries depends on big-data analytics

This may help in many ways. Fishing trips could be tailored to meet quotas in the shortest time reducing fuel consumption and crew costs. Real-time catch data could help administrations close a fishing zone once quota is reached. Fisheries administrations can monitor catch efforts over an entire region and decide which zones to close, improving management of stocks and fishing licenses. Fighting the challenge of Illegal and Unreported fishing contributing to over-exploitation of fish stocks. Use of disruptive technologies in fisheries and aquaculture like block chains, sensors and automatic identification systems (AIS) has the potential to change the processes profitability and sustainability of the sector

Globally used popular Data Science Software

• MS-EXCEL

• SPSS (IBM)

• STATA • MINITAB

• SAS

• R

• PYTHON

Abstract

The paper discusses patentability aspect of subject matter. Patentable subject matter must satisfy three basic criteria i.e. novelty, inventiveness and industrial applicability, though few subject matter satisfying all the three basic criteria stated above may still be not patentable in specific jurisdiction based on certain circumstances or as stated in the patent act of the specified jurisdiction. Therefore grant of a patent is subject to certain limitations mentioned in the section 3 and 4 of the Indian Patent Act for Indian jurisdiction. The aim of this paper is to study such limitations in detail.

This chapter integrates case studies, scientific examples, and policy perspectives to illustrate how AI and ML can transform agricultural weather intelligence. The discussion spans from advanced predictive models in developed nations to emerging applications in technology-limited regions, while also addressing the urgent need for resilient crisis management systems in the face of natural and human-caused conditions. Advanced scientific techniques can not only optimize food production and resource management but also play a critical role in recovery efforts during times of crisis. Agriculture is intrinsically linked to the natural world—a domain defined by fluctuating weather patterns, seasonal shifts, and unpredictable environmental factors. Today, Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing how we analyze and predict weather patterns, and their applications in agriculture are far-reaching (Fig. 5.1). From forecasting crop yields and fishery conditions to managing plantations and monitoring livestock health, these technologies are creating a paradigm shift in agricultural weather intelligence. Scientific organizations worldwide are leveraging AI and ML to refine predictive models, enable multi-variate analysis, and provide advanced warning systems for extreme events such as droughts, tsunamis, earthquakes, and even crises caused by human-made conflicts. Emerging nations with rapidly growing technological capabilities stand to benefit immensely from these innovations, tailoring solutions to local challenges while contributing to global food security and crisis resilience.