eChapters

In provincial areas of non-industrial nations, over 70% of the populace actually relies upon agriculture . Be that as it may, financial emergencies, informal land distribution and Climate change issues have ruined endeavored gains in agricultural efficiency and related rustic improvement results. Innovation pushed advancement has generally driven farming to the brink of another improvement that can influence plant variety and yield, yet additionally climatological and financial results. The idea of economical agriculture has become progressively advocated as examination and cultivating networks accept that efficiency with natural and social outcomes should be sensibly adjusted. Agriculture of created countries has basically benefitted of this idea overall during the 1990 s. To find success around the world, broad exploration should be led not just for enormous scope farmers in created countries yet additionally for limited scope ranches in agricultural countries. Climatic smart agriculture saves biodiversity by taking on sound, multifunctional, and multi-trimming approach through plan arranged spatial preparation and stream among them. Here, a spatiotemporal information driven worldview for detecting observing and coordinating agricultural Climate has been proposed to upgrade the utilization of Climatic smart agriculture framework. As an excursion towards maintainable coordinated cultivating, computerized farming offers different open doors for universal systems administration and computerized resources through information sway. The exploration issues are basically worried in the combination of different information archives with administration information, open access information, institutional information, and space explicit information from end clients. Because of the mix of heterogeneous sensors and advanced stages in agriculture, the information typology is getting more extravagant, and how much information is continually expanding.
Feedstocks assume a significant part in the comprehensive advancement of non-industrial nations that depend vigorously on agriculture. It has been standard that farmers must be actually present on their fields during each

Climate change and resource scarcity require farming systems that anticipate risks and make proactive decisions. This chapter explains how predictive modelling, artificial intelligence and IoT technologies can strengthen climate-smart agriculture. Statistical techniques, Convolutional Neural Networks, Long Short-Term Memory networks and Transformer models are examined for forecasting weather, crop performance and resource requirements. These tools can improve irrigation and fertilizer management, raise productivity and reduce environmental impact. Adoption remains constrained by poor-quality data, infrastructure deficiencies, cost, limited interpretability and unequal access to digital technology. Farmer-centred design, federated learning, gradual deployment and scalable systems are recommended for developing fair, practical and climate-resilient agricultural solutions.

Introduction

A complete neurological examination is warranted whenever an equine patient displays signs consistent with neurological disorder or to establish that the horse is neurologically normal (e.g., pre-purchase examination). Neurological examination of equine patient is challenging due to their large size, which prevents veterinarian from using many of the tests used for small animals. The key to a successful neurologic examination is to do the examination in a consistent and organized fashion. It is important to develop a routine, then do it the same way each time. This approach ensures that some parts of the examination are not forgotten and it also increases the consistency of the exam as you examine more number of equine patients. A standardized examination form is a valuable aid in this and can be concise. The goals of the neurologic examination are to determine whether disease of the nervous system exists, to localize the lesion to a particular area of the nervous system, and to describe and record the responses as a baseline for future evaluations. In this paper, a complete neurological examination is described with special emphasis on field conditions.

Opportunities

India is home to largest milch animal population, totalling around 100 million cattle and buffalo (out of total population of 300 million) spread over 6.5 million census villages. India is the largest milk producer with CAGR of 4.5%, likely to go up to 5%, which is likely to create around 20 million jobs every year. The most significant factor is that dairy industry is likely to exceed IT industry in employment generation. In year 2018, India produced 146 million tonnes of milk contributing 26% of the agriculture GDP.

But the foundation of dairy industry is economically vulnerable as per animal productivity and fertility are very low leading to meagre profits and frequent demands to enhance procurement price. National schemes like Operation Flood, National Dairy Plan-I and National Dairy Plan II, largely focussed on milk collection, processing, distribution and organising farmers but the animal component has been neglected. Enhancing per animal productivity by empowering farmers to scientific management and breeding to achieve genetic improvement needs urgent attention. Although numerous technologies to improve health and genetics are available but the single most important gap is failure to deliver these to farmers. Unlike western world, India has not been able to develop modern veterinary service delivery models. In order to cater to breeding and health services of cattle and buffalo (300 million in numbers) and also pet animals in rural areas and small towns, huge manpower and infrastructure will be required.

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.

Clouds, those ever-present features of our sky, are more than just aesthetically pleasing; they are fundamental components of earth‘s weather and climatesystems. This chapter, „cloud formation and classification,“ will embark on a journey to understand the fascinating processes that lead to their creation and the systematic ways in which they are categorized.
We will begin by exploring the essential ingredients for cloud formation: the presence of water vapor in the atmosphere, the availability of condensation or ice nuclei, and the crucial mechanism of air cooling. We will delve into how rising air parcels cool adiabatically, reaching saturation and allowing water vapor to condense or deposit into the myriad forms we recognize as clouds. Following the understanding of their genesis, we will navigate the internationally recognized system of cloud classification. This system, primarily based on a cloud’s altitude, appearance, and the processes that formed it, provides a common language for meteorologists and observers worldwide. We will learn to identify the main cloud types – high, mid-level, low, and vertically developing – and their associated characteristics, from the wispy cirrus to the towering cumulonimbus.
Furthermore, we will touch upon the significance of different cloud types in indicating current weather conditions and forecasting future weather. The appearance and evolution of clouds can offer valuable clues about atmospheric stability, moisture content, and the likelihood of precipitation. By the end of this chapter, you will gain a comprehensive understanding of how clouds form the diverse array of cloud types that grace our skies, and their importance in the broader context of atmospheric science. Let’s unlock the secrets held within these visible masses of water and ice suspended in our atmosphere.

Sending a message from one place to another place, which are physically separated from each other, is called Communication. The Communication Engineering or Communication Technology deals with the design and development of engineering systems to transmit information from its source, and at the same time, to receive the same information at a remote destination with reliability, accuracy and quality.

It is emphasizing the systematic approach required for the successful planning and funding of projects:
Components of Project Formulation
1. Needs Analysis: Identifying and defining the needs that the project intends to address, typically involving stakeholder consultation and market analysis. This process involves systematically gathering and analyzing data about a community, a group, or a market to identify both the current conditions and the gaps between these conditions and desired outcomes. Effective needs assessment aids in designing projects that are relevant, targeted, and likely to produce significant impact. Here’s a more detailed exploration of how needs assessment is integral to project formulation:
Key Steps in Conducting a Needs Assessment
• Stakeholder Identification: Early in the project formulation, identify all relevant stakeholders, including direct beneficiaries, community leaders, funders, and other affected parties. Understanding who will be impacted by the project helps in targeting the needs assessment process more effectively.
• Data Collection: Utilize various methods such as surveys, interviews, focus groups, and field observations to gather comprehensive data about the current state and deficiencies experienced by stakeholders. This might involve looking at socioeconomic data, environmental conditions, or specific community challenges.
• Gap Analysis: Analyze the data to identify gaps between the current state and the desired outcomes. This analysis helps pinpoint the specific areas where interventions are needed.formulation: 

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

Introduction

Agriculture has played an important role in the global economy in recent years. The further growth of the population is leading to a gradual decrease in the area of the agricultural land and further growing stresses on the farm system. Agricultural food production methods are rising in demand for successful and healthy. Innovative sensoring and motive technologies and advanced information and communications systems and Artificial Intelligence must supplement conventional agricultural management methods to speed up farm productivity growth more accurately, thereby promoting the production of high-quality and high-performance agriculture. Computer vision monitoring systems have become valuable tools in farm operations over the past decades, and their use has increased significantly.

What is Computer Vision?

Computer vision can be characterized as a field of AI for extracting information from digital images. The type of data obtained from an image may be different from recognition, navigational space measurements or applications of increased reality.

The applications of computer vision can also be described. Computer vision builds algorithms that recognize and use the quality of pictures for other purposes.

Large Language Models have fundamentally changed natural language processing and expanded the role of artificial intelligence across software engineering, healthcare, education, business and scientific research. This concluding chapter synthesizes the major technological, operational and societal themes addressed throughout the book and develops a roadmap for next-generation AI models. It considers the continuing evolution of model architectures, training methodologies, multimodal capabilities, real-time inference and specialized applications. At the same time, future AI development must address privacy, fairness, transparency, accountability, security, computational cost and environmental sustainability. The chapter emphasizes that LLM development cannot be viewed solely as a race toward larger models; future systems must also become more efficient, adaptable, interpretable and responsible. Emerging directions include self-improving systems, real-time reasoning, multimodal intelligence, domain-specific models and increasingly autonomous AI agents. Lifecycle governance, continuous evaluation and human oversight will remain essential as models become more deeply integrated into society. The roadmap therefore calls for collaboration among researchers, developers, industry, policymakers and users to create AI systems that combine technological capability with safety, sustainability, human values and social responsibility.

India is primarily an agriculture based economy, which implies, agriculture dominates the nation’s economic landscape. In the course of socioeconomic growth, agriculture is crucial. Around 58 per cent of the population in India relies only on agriculture for their livelihood. India is enriched with various agricultural and horticultural crops with diversified agro-climatic conditions and different soil types. India is one of the leading producing countries of cereals, pulses, oilseeds, fruits, vegetables etc. India is also have a prominent scope in allied sectors of agriculture includes livestock, poultry, forestry, sericulture etc. Among such crops, Moringa is the selected crop for this research study. Moringa (Moringa oleifera), which is popularly known as ‘Drumstick’ belongs to the family Moringaceae. Moringa is basically a native crop of India. This crop has its own therapeutic properties with greater nutritional content. Especially in South Indian cuisine, moringa pod is a highly regarded vegetable because of its distinctive flavour and fragrance.

Product Development and Marketing

Several efforts are being made on the research front for development of novel products from fish which have enhanced shelf life, retain the freshness and nutrients and are safe to consume. During the course of product development, which are in many stages, studies are conducted prior to commercialisation to ensure the acceptance of a product.

In a business, understanding consumer behaviour plays an important role in success. To be in a business for a long time, it is essential to know what the consumer prefers and why. Consumers make the buying decisions based on a number of factors. The purpose behind studying the buying behaviour and consumer preference is to produce and market products which may better meet the needs of a consumer. The emerging fastfood culture among the young and affordable has brought focus on processed food and its demand in the domestic food market in India. Domestically, spending on food and food products constitutes the largest portion of the Indian consumer’s spending – more than a 31% share of wallet. Evaluation of consumer preferences before introducing a new product will help the marketer to refine the product for better reach.

Conjoint Analysis

Conjoint analysis is a popular technique used in marketing research to study the features a product that should possess to have a wide consumer reach. Conjoint analysis was initially conceptualised by Luce and Tukey (1964) and further developed by Green and Rao (1971) for marketing research. It employs a decompositional method to estimate the structure of consumer preferences and consumer utility values of different attributes of a product or service. It is a decompositional method that disaggregates the structure of consumer preferences into utility values. The relative importance of a product can also be estimated using this method.

The important convincing appealing and easily understood method of presenting the statistical data is the use of diagrams and graphs. Diagram makes the comparison of the data but graphs makes the relationship among the data.

Objective: Aggregated figures for merchandise export in India for eight years are as follows

Frequency Distribution is the arrangement of various values of a variable in order of magnitude either individually or in groups alongwith their corresponding frequencies or class frequencies. Frequency distribution is necessary for data condensation and revealing pattern within the data. Frequency distribution can be (i) Ungrouped Frequency Distribution (ii) Grouped frequency distribution.

In case ofungrouped frequency distribution, the individual values ofthe variable are arranged in ascending order alongwith their frequencies. Frequency is the number of times a particular observation is repeated.

In case of grouped frequency distribution, the values of variable are grouped into classes alongwith their corresponding class frequencies. Class frequency of a class is the number of observations lying within the upper class limit (maximum value) and lower class limit (minimum value) of the class.

Let us first illustrate the preparation of ungrouped frequency distribution. Consider 50 petridishes each containing 6 no.s of seeds for germination. Let the variable x_i denote the no. of seeds germinated in a petridish.

Frequency Distribution: A tabular presentation of the data in which the fre- quencies of values of a variable are given along with them is called a frequen- cy distribution. Two types of frequency distribution are available

1. Discrete Frequency Distribution or Ungrouped Frequency Distribution
2. Continuous Frequency Distribution or Grouped Frequency Distribution

Objective (a): Prepare a discrete frequency distribution from the following sentence in English: “Today there is hardly a phase of Endeavour which does not find statistical devices at least occasionally useful.”

Theory: This problem relates to discrete frequency distribution i. e. a frequency distribution which is formed by distinct values of a discrete or continuous variable.

Pie charts are useful to compare different parts of a whole amount. They are often used to present financial information. E.g. A farmer’s expenditure on farm can be shown to be the sum of its parts including different expense cate- gories such as cultivation, irrigation, sowing, harvesting, and general running costs (i.e. rent, electricity, fuel etc).

A pie chart is a circular chart in which the circle is divided into sectors. Each sector visually represents an item in a data set to match the amount of the item as a percentage or fraction of the total data set.

Components of biodiversity are all the various forms of life on Earth including ecosystems, animals, plants, fungi, microorganisms, and genetic diversity. Ecosystems, species and genetic resources should be used for the benefit of humans, but in a way that does not lead to the decline of biodiversity. Substantial investments are required to conserve biodiversity, but it will bring significant environmental, economic and social benefits in return. To address these challenges various initiatives were taken in the past such as Ramsar Convention on Wetlands in 1971, Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) in 1973, 1979 Bonn Convention on Migratory Species (CMS) in 1979.

Abstract

Intellectual property right (IPR) is the most serious, new branch of law and a legal right conferred by federal government in accordance with International conventions and treaties. IPR protects some of the finer manifestations of human achievement. IPR safeguards the application of ideas and information that are of commercial value. The exploitable ideas become more sophisticated and hope for a successful economic future depends increasingly upon the superior corpus of new knowledge and fashionable conceits.  Copyright law is one of the effective tools of IPR. Copyright law provides an incentive to creative activity of author and permits the society to benefit from those inventions. However, the technological advancement has made violation of copyright very easy and regulation very difficult. This paper aims at familiarizing  copyright laws and the powers enshrined in copyright laws.