eChapters

Introduction

Climate change, a global phenomenon of present time, has emerged as the greatest challenge of the world in the twenty-first century. Rising earth’s surface temperature is one of the most threatening factors coming into view. The substantial changes in the earth’s climate have an impact on ecosystems, economics and societies in a variety of ways (Sharma et al., 2024). It is one of the important environmental, agricultural, human and animal health issue. Climate change is an association of multidimensional effects involving physical characteristics, causes and consequences (Visschers, 2018). The climate change has resulted in to increasing global average temperature of air and ocean, melting of snow, rising of ocean level, change in the rainfall pattern and extreme whether events such as drought, flood, heat waves, storms etc.

12.1 Introduction
Having explored the dynamic realm of Numerical Weather Prediction, which focuses on forecasting the atmosphere’s state over days to weeks, we now shift our focus to a broader, longer-term perspective: Climate Data and Analysis. While weather describes the atmospheric conditions at a specific place and time, climate refers to the long-term average pattern of weather in a region, typically spanning decades or longer. Understanding and analyzing climate data is paramount for comprehending our planet’s past climate, identifying ongoing changes, and projecting future scenarios in a warming world. The study of climate is fundamentally data-driven. Unlike the instantaneous snapshots used in weather forecasting, climate analysis requires vast historical records, often extending back centuries or even millennia, to discern natural variability from human-induced changes. This data comes from a diverse array of sources, including instrumental records (thermometers, rain gauges, barometers), satellite observations, and proxy records (tree rings, ice cores, sediment layers) that provide indirect evidence of past climate conditions. The sheer volume and heterogeneous nature of this data present unique challenges and opportunities for analysis.
This chapter will delve into the essential aspects of climate data and the methodologies used to analyze it. We will begin by exploring the various types of climate data available, understanding their strengths, limitations, and how they are collected and curated. Crucially, we will examine how this data is used to characterize climate variability – the natural fluctuations within the climate system – and to detect climate trends, particularly those associated with global warming. We will introduce key climate indices that summarize large-scale atmospheric and oceanic patterns, providing simplified metrics for
understanding complex interactions.

Climate normals for crops and livestock refer to the long-term average weather conditions, such as temperature, precipitation, and humidity that are suitable for specific crops and livestock in a particular region. These normals help farmers and agricultural planners understand the typical climate conditions that support optimal growth, productivity, and health of crops and livestock. By knowing the climate normals, farmers can make informed decisions about:
• Crop selection and breeding
• Planting and harvesting schedules
• Livestock management and breeding
• Irrigation and water management
• Pest and disease management
15.0. Crop Weather Calendars
A Crop Weather Calendar (CWC) is a valuable agrometeorological tool used to align the crop growth stages with prevailing weather conditions to optimize agricultural practices. It helps farmers, extension workers, and planners make informed decisions by integrating weather information with the crop‘s physiological needs (Fig. 15.1).

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: 

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.

I. INTRODUCTION

The Websites are soaked in information, much of it with varying degrees of copyright protection. In fact, the reality is that almost everything on the Net is protected by copyright law. E-business Websites are a composition of materials, often consisting of words, graphics, audio, and video that are expressed to the consumer as information content. The owners and Website developers carefully select the content to sell the company’s product or service. The subject matter expressed in the site is an electronic publication of this content. Since, designing, producing, and maintaining a sophisticated Website is very expensive, protecting content ownership is extremely important. Electronic commerce will continue to be highly competitive. As Websites become more and more interactive with consumers, their creation, design, and maintenance place enormous demands on innovative marketing techniques that should be legally protected.

Never before has it been so easy to violate a copyright owner’s exclusive right to copy the material. Everyone with a computer and an Internet connection creates his own Web pages and thus become a publisher. Hence the rules that once applied to only a few companies bind million of people now. Imagine having paid a Website developer hundreds of thousands of rupees to launch a killer application and acquiring millions of Rupees from a venture capitalist firm, only to discover that another e—business is using your online material. Or consider a scenario where your Website is linked to an e-business that displayed copyrighted material without the owner’s consent. Could your company be liable under a theory of copyright infringement? This paper highlights the scenario when contents of your Website are exploited by others without your permission or knowledge. The discussion is centered on copyright issues involved in the practices of linking, inlining and framing technologies which are normally being used on the Internet.

Cotton is often called as “white gold”. True to its name cotton supports the largest agro based industries in India and cotton industry ranks first in the agro based industries and engages 4 to 5 million people in cotton growing. The industrial sector employs 1.0 million people. At present there are 3660 ginning and 1570 textile mills in India. India earns foreign exchange to a tune of Rs. 2500 million through exports of cotton textiles and products. Cotton is cultivated for ‘lint’ which is used for preparing threads, yarns and medical cotton mixed with synthetic fibre and for various other purposes.

This chapter provides an in-depth exploration of advanced techniques and conceptual frameworks—here termed “Weather Intelligence”—and their potential applications in precision agriculture. Although the term is not yet standardized, it serves as an exploratory framework for integrating advanced forecasting, diverse datasets, and data analytics to capture both natural and anthropogenic influences on weather patterns. The discussion spans crop and livestock production as well as allied sectors such as fisheries, aquaculture, plantations, horticulture, and specialized areas like flower farming. By integrating real-time data from sensors, remote sensing platforms, and historical records, these exploratory systems aim to empower farmers and food producers to optimize irrigation, planting, harvesting, and overall resource management. Agriculture has always depended on weather, yet the rapid pace of climate change and the need for sustainable practices have driven a technological revolution in farm management. In today’s digital age, advanced forecasting techniques and data analytics are converging into a new conceptual framework we call “Weather Intelligence.” Although this term is exploratory and not yet widely adopted, it encapsulates the vision of integrating high-resolution satellite imagery, IoT devices, and machine learning algorithms to forecast local microclimatic conditions with unprecedented accuracy. These forecasts enable proactive adjustments to critical operations—such as irrigation scheduling, planting, and harvesting, thereby mitigating weather-induced risks and enhancing productivity. Moreover, it is increasingly evident that weather patterns are not only shaped by natural phenomena but also by human-induced risks such as wars, conflicts, and environmental abuse. Factors like landfills, water and sea pollution, toxic dumping, and unsustainable industrial practices are altering microclimates around the world. This complex interplay of natural and anthropogenic forces calls for the incorporation of a broader array of variables and larger datasets into forecasting systems. In this context, embracing Weather Intelligence as a dedicated field of study and application in agricultural and allied sciences becomes not only desirable but essential. This chapter aims to detail the evolution and current state of these integrated approaches, discuss their potential applications across various agricultural sectors, and outline strategies for precision decision making. While much of the content is exploratory in nature, it provides a conceptual framework for future research

Abstract

This paper portray the generation of spectral signature library for semi arid area crops using hyperspectral data and the scope of the application of the generated digital library for the classification of the satellite imagery of similar regions.

Hypersprctral data have narrow bands which help to provide additional information related to observed features. In case of this agriculture study crop related information are gathered using hyperspectral sensors The variation in the observations are due to physic-chemical characteristics such as biochemical composition, physical structure, water content and plant ecophysical status. Using the hand held spectroradiometer the reflectance is captured for the bajra, moong, moth crops from the Rajasthan, India’s semi arid region. The observations have been taken for different dates considering the phenology of the crops. Hyperion data from EO-1 satellite have been used to implement the classification operation based on the spectral signature library of bajra. It is observed that the image with the crops can be classified according to the phenology difference.

ABSTRACT

The concept and nature of intellectual property have undergone a paradigm shift during the last few decades. The importance of effective IPR governance has attracted the attention of its stakeholders. Hence, this paper focuses on the role of IPR governance and management practices for improving the quality of relationships among stakeholders besides documenting current and future trends of IPR governance and management.

INTELLECTUAL PROPERTY RIGHTS – AN INTRODUCTION

Intellectual property rights refer to the privileges granted by the government to any individual or organization for creative prowess. In recent years, such rights have gained immense importance. The laws in existence for protection and enforcement of these rights are meant to foster innovation and for regulating the use of inventions.

Introduction

Resources are used unscientifically commonly. For example applying feed somewhere in the pond results in loss, but by using technology, we can know when to apply where to apply, and how to apply which leads to efficient management of resources. Monitoring should be continuous which is not possible and very difficult for humans, but with technology, it could be monitored and the status could be known even from home which is communicated through mobile, which is sent by sensors installed. The data are also easily maintained. When technology is used, resources are efficiently utilized, since everything has a demand and that can be satisfied. So resources can easily be maintained which reduces wastage. Field management is a very skilled activity and it needs years of experience and onsite exposure which needs skilled labour, in such cases technologies aids with better methodologies. When the resources are applied at the right time, at the right amount, the growth becomes normal which increases the produce quality. After a product is produced, it has to be sent to a series of channels such as transport, market etc. and a technology should backup and monitor everything. The produce after being produced should be sold and many apps can be downloaded to improve the marketing. The whole world is changing within a period of time. So one should stay updated to impress the livelihood and the wide knowledge on technology helps to lead a better life with easy management practices. Thus data driven technology interventions are needed for efficient management of resources, monitoring of things anytime & anywhere, reduction of wastage of resources, reduction of dependence on trained and skilled manpower, enhanced product quality, better integration, better marketability, to stay updated with latest knowledge and access to other knowledge source for better upgrades.

Cytauxzoonosis is an emerging tick-borne disease of domestic cats and wild felids caused by the protozoa of the genus Cytauxzoon. The common clinical findings in affected cats are similar to other hemoparasitic infections namely anorexia, depression, anemia, vomiting, icterus, and high fever. The first reporting of feline cytauxzoonosis with four fatal cases was done by Wagner in Missouri, USA in the year 1976.

Bobcats (Lynx rufus) are recognized as the natural wild reservoir of Cytauxzoon felis with prevalence reaching up to 100% in some enzootic areas (Zieman et al., 2017). This infection is highly fatal in domestic cats with 97% mortality without treatment (Meinkoth and Kocan, 2005). However, subclinical infection in some cats has also been evidenced i.e., they were infected with C? felis but did not display the acute clinical signs of the disease before diagnosis (Brown et al., 2010 and Rizzi et al., 2015).

Several other wild felids apart from bobcats and domestic cats have been found to be affected by fatal or nonfatal Cytauxzoon sp. infection. These include lions, jaguar, cougars, pumas, ocelots, Iberian lynx, meerkats, European wild cats and Asiatic wild cat, panthers, and tigers (Jakob and Wesemeier, 1996; Rotstein et al., 1999; Yabsley et al., 2006; Andre et al., 2009; Filoni et al., 2012 and Furtado et al., 2017).

Dashboards play a crucial role in the fields of Machine Learning and Artificial Intelligence by providing a visual and interactive interface through which complex data, model outputs, and performance metrics can be easily understood and monitored. In data-driven environments, where vast amounts of information are processed continuously, dashboards serve as an essential bridge between technical systems and human decision-making. They transform raw data and analytical results into meaningful visual representations, enabling users to interpret insights quickly and effectively. In the context of Machine Learning and Artificial Intelligence, dashboards are used at multiple stages of the workflow. During data exploration and preprocessing, dashboards help visualize trends, patterns, and anomalies in datasets, allowing data scientists to make informed decisions about feature selection and data cleaning. Once models are developed, dashboards

Agriculture has long been at the mercy of the weather, and in today’s datadriven world, harnessing meteorological information is more critical than ever. Weather intelligence—the process of collecting, analyzing, and disseminating weather data—supports decision-making in diverse fields such as aviation, transportation, emergency management, and especially agriculture (Fig 3.1). This chapter explores how advanced data acquisition methods and robust database management techniques underpin agricultural weather intelligence. It covers the variety of data sources available, details strategies for integrating and ensuring the quality of vast datasets, and examines the forecasting techniques that transform raw data into actionable insights for precision forecasting and decision making in food production. Fig. 3.1. Weather Data Acquisition System In this chapter, we not only discuss the technological means by which weather data is collected—from satellites, ground sensors, and IoT networks—but also dive into the structure of weather databases. These repositories house surface weather observations, radar and satellite imagery, upper air measurements, and outputs from weather models. Further, we outline the different forecast types and forecasting methodologies that are essential to interpreting this data, ranging from nowcasting for immediate weather conditions to seasonal forecasting that supports long-term planning. Finally, the role of big data analytics in supporting precision forecasting and optimizing decision-making in agricultural production is examined, highlighting the integration of advanced techniques like numerical

Some basic and most common analysis tools used in research are summary statistics. These can be analyzed in Ms-Excel through the following ways.

8.1 Summary Statistics

The following data are the yield (q/ha) of 60 varieties of paddy maintained in a research station for breeding trails:

53 ,74, 89, 65, 88, 65, 45, 70, 25, 80, 75, 85, 47, 63, 78, 44, 42, 51, 59, 86, 76, 48, 30, 38, 67, 78, 74, 68, 58, 59, 36, 76, 89, 48, 67, 69, 35, 46, 89, 75, 79, 56, 42, 41, 60, 65, 68, 69, 61, 63, 95, 94, 85, 83, 76, 71, 68, 62, 64, 62.

Calculate the summary statistics for the study of the data.

Solution: The summary statistics of the data using MS – excel can be obtained / analyzed through following steps:

Step 1: Open Excel sheet and feed the data as given in Fig. 8.1.

Step 2: Go to data menu and select Data Analysis. Sometimes this menu is not available in the data, it can be added through add in → check in, analysis tool pack → go. It will appear as the way showing in Fig. 8.1.

Introduction

The term analytics and data analytics are used inter-changeably. When referring to analytics, it means “analysis of data” or “what can be learnt from an analysis of data”. In an industry like fishing industry, the researchers have been slow to adopt the tools used to portray accurate representations of data. For example, a dashboard with historical data from the past ten or fifteen years displayed on a digital map can enable the researchers to decide the places where fishing can be good. This map can help in locating the points fished during specific seasons. This will enable the researchers to filter the data on various parameters like area, year, season etc. or on any other specific parameter which can give valuable information. Similarly, this type of dashboard can affect the decision making process while fishing, like one can find the monetary value associated with each trip during a specific season or compare the values associated with two different trips to gauge the cost-benefits.

Data Analytics helps in taking most informed decisions in the fishing industry if one of the goals is to increase income while ensuring sustainability. It helps in deciding whether to reduce overhead costs or to increase expenditure to avoid overhead costs in future.

Fisheries sector undergoes continuous changes with introduction of newer technologies evolved through research and development institutions. Statistics per se deals with generation of data, data management, data analysis and information generation from data. There are varied disciplines of fisheries viz., Aquaculture, Fisheries Resource Management, Fish Genetics, Fish Biotechnology, Aquatic Health, Nutrition, Environment, Fish Physiology, and Post-harvest Technology, wherein research on practical aspects is undertaken which affect production and sustainability.

Data cleaning is one of the most important parts of machine learning. It plays an important role in building a machine learning model. Data quality is a significant aspect to train the ML model. Inaccurate data can have an impact on results. Data quality problems can occur anywhere in information systems. A technique that helps to convert improper data into meaningful data. Machine Learning is datadriven. With the data cleaning techniques, your Machine Learning model will perform better. So, it is important to process data before use. Without quality data, it is unwise to expect a correct output. Data cleaning refers to identifying and correcting errors in the dataset that may negatively impact a predictive model. It is used to refer to all kinds of tasks and activities to detect and repair errors in the data. These problems can be solved by using various data cleaning techniques. The process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. When combining multiple data

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.

Now we have to learn how to enter numbers and text, how to edit any erroneous entry and then how to do simple calculations. Open empty excel spread sheet by clicking on the start menu in the window tool bars, click on Programs and then click on the Excel menu item.

6.1 Entering Numbers

Numeric entries are constant, can be integers, decimal fractions, integer fractions and scientific nations which can be entered by typing the number in the active cell [cell which can be made active by clicking the mouse on the cell.

6.1 Introduction
Our journey through remote sensing has, thus far, focused on the remarkable capabilities of various platforms and sensors to observe Earth’s atmosphere and surface. From the localized, high-resolution insights provided by groundbased instruments like Wind Profilers and SODARs, to the expansive, global coverage offered by an array of satellite-borne imagers, sounders, radars, and lidars, we’ve seen how a continuous stream of invaluable meteorological data is acquired.
However, the mere act of collecting this data, no matter how sophisticated the technology or comprehensive the observations, represents only the initial phase of its lifecycle. The true power and utility of remote sensing data are unlocked only when it can be efficiently organized, securely preserved, and readily available to the diverse community of meteorologists, climate scientists, researchers, and operational forecasters who rely on it daily. This necessitates a profound understanding of the principles and practices governing data formats, data storage, and data access.
The sheer volume, velocity, and variety of remote sensing data pose significant challenges. Petabytes of information are generated annually, requiring robust infrastructure and standardized methodologies to ensure its integrity, interoperability, and long-term usability. Without effective strategies for managing this deluge of information, the potential of remote sensing to enhance weather prediction, advance climate science, and inform critical decision-making would remain largely untapped. 

Abstract

Droughts are normal recurring climatic phenomena that vary in space, time, and intensity. The spatial and temporal variability and multiple impacts of droughts provide challenges for mapping and monitoring on regional scales. Large historical datasets are required in drought studies to understand the complex inter-relationship between the climatic and meteorological data. Extraction of valuable information from such large data archives demands an automated and efficient way. Data mining is answer to above problem as it has the potential to search for hidden pattern and identify the relationship between the datasets. For the current research, rainfall data from 1951-2006 was used to compute Standardized Precipitation Index (SPI) and NOAA-AVHRR NDVI images for the period 1981-2006 was used to calculate Vegetation Condition Index (VCI). SPI and VCI were used as input parameters in the Apriori Algorithm for generating the association rules. Interesting rules were developed identifying the relationship between user-specified target episodes and meteorological events. Therefore, spatial and temporal pattern of drought was achieved with 88% confidence.

Livestock farms generate large quantities of physiological, environmental, behavioural and production data through sensors, IoT devices and management records. Raw information must be processed before it can support dependable analysis. This chapter explains data cleaning, missing-value treatment, noise reduction, anomaly detection, integration, normalization, transformation and dimensionality reduction. It also discusses feature extraction, categorical encoding, variable transformation and temporal-feature analysis. Machine learning, AutoML, Python, R and other analytical platforms are introduced as tools for preparing livestock datasets. Applications include disease prediction, yield improvement and movement monitoring. The chapter concludes by examining scalability, data quality, privacy and ethical challenges in intelligent livestock analytics.

Healthcare artificial intelligence depends on the quality, fairness and reliability of the data used for model development. This chapter examines the characteristics of healthcare data and explains how incomplete, inconsistent or unrepresentative datasets can produce inaccurate and discriminatory clinical outcomes. It identifies sampling, demographic, measurement, historical, institutional and annotation biases and discusses strategies for mitigating them at the data, algorithm and evaluation stages. Model robustness across different populations and clinical environments is also considered. Particular attention is given to explainable AI, human oversight, continuous auditing and interdisciplinary collaboration as essential components of trustworthy, equitable and clinically dependable healthcare systems.

Introduction

The first step of geostatistical analysis in R is the creation of dataset which is acceptable in R environment by using the data collected from field survey or laboratory experiments. Different type of data is collected during experiments e.g. sand content, silt content, organic carbon content, geographical location of data points, soil type, land use type etc. Here, we will discuss how these data are converted into a acceptable dataset format for R environment and how we manage these data. In R this task is performed in two steps. First, a data structure is created to hold the data and second is the entering or exporting the data into data structure.

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.

Abstract

Biological databases that are composed of a large collection of digital nucleic acid sequences, protein sequences, or other polymer sequences stored on a computer, which is made available to the research community worldwide using web servers. The vast amount of information generated in molecular biology is analyzed, curated, tabulated, and arranged in databases. There are different types of biological databases, based on their contents. Besides, the database management system, a program to manage the databases, can be of two types: distributed DBMS and relational DBMS. This chapter briefly narrates the importance and purpose of biological databases.

The content presented in this chapter is exploratory and reflects emerging trends in integrating weather intelligence with decision support systems. The term “weather intelligence” is introduced as a framework, with ongoing research expected to further refine and expand upon these ideas as technologies evolve. In an era where climate variability and extreme weather events increasingly threaten global food systems, the integration of advanced decision support systems (DSS) with what we are now calling weather intelligence has emerged as a transformative paradigm. This chapter explores how food production— encompassing agriculture, fisheries, and animal husbandry—can harness integrated weather intelligence to inform real-time crisis management, strategic planning, and risk mitigation. The discussion is exploratory in many respects, as the concept of weather intelligence represents an innovative framework that challenges traditional forecasting methods and decision support tools (Fig. 9.1). Drawing on multidisciplinary research and practical case studies, we examine the building blocks of decision support systems tailored to food production. We then discuss the integration of weather intelligence into crisis response and strategic planning, highlighting both established risk mitigation approaches and emerging tools designed to anticipate and respond to extreme weather events. By contextualizing these developments within the broader scope of sustainable food production, this chapter offers an in-depth analysis and a forward-looking vision for decision support and response management in a rapidly changing climate.

Deep learning enables automated analysis of agricultural images for crop classification, disease detection, weed identification, yield prediction and soil monitoring. This chapter examines Convolutional Neural Networks, Recurrent Neural Networks and Vision Transformers applied to imagery collected by satellites, drones and ground-based sensors. These models automatically identify patterns and features that conventional methods may overlook. Integration with precision agriculture can improve decision-making, increase crop productivity and reduce resource waste. Challenges include shortages of annotated datasets, limited interpretability and high computational requirements. Transfer learning, data augmentation, edge computing and real-time processing are discussed as approaches for enabling practical deployment in remote and resource-constrained agricultural environments.

Traditional livestock diagnosis is often reactive and may identify disease only after clinical symptoms become visible. This chapter introduces deep-learning methods for automated health monitoring, early disease detection and predictive analytics. Models analyse images, videos, body temperature, heart rate, activity patterns and historical health information. Convolutional neural networks extract visual features and classify diseases, while LSTM networks identify patterns in time-dependent physiological and behavioural data. Multimodal fusion improves the reliability of predictions by integrating different data sources. IoT-connected systems can generate real-time warnings and support timely intervention. The chapter compares deep learning with conventional approaches and evaluates accuracy, recall, false-negative rates and practical deployment.

Deep learning emerged as a serious contender in the field from a 10-year explosive computational development. Deep learning is therefore a special form of machine learning, the algorithms of which are based on the structure and function of the human brain.

Machine learning VS Deep learning

Deep learning is arguably the most efficient form of machine learning. This is so important because they know the best way to solve the problem and how to fix everything. The following is a description of deep learning and machine Learning:

Ice cream defects can result from formulation imbalance, poor ingredient quality, processing deviations, inadequate freezing or hardening, packaging problems, storage conditions, and cold-chain failures. This chapter provides a systematic framework for understanding, classifying, diagnosing, and correcting defects affecting flavour, body, texture, melting, colour, appearance, packaging, and storage stability. It emphasizes that a visible defect in the finished product may originate much earlier in the production or distribution chain. Examples include iciness caused by slow freezing or temperature abuse, weak body associated with formulation or overrun, and flavour problems originating from raw materials or microbial activity. Defect analysis therefore becomes an important tool for troubleshooting, quality improvement, training, and preventive action.

Abstract

Population of Rajasthan as per Census 2011 is 68,621,012 persons which is much higher than census 2001 that records 56,507,188. It includes 12 districts of Rajasthan. Western Rajasthan covers about 61% area of the total geographical area and 39.51% population of the total population of the Rajasthan. The growth rate of population is much higher than that of the Rajasthan State. In the census 1981 Western Rajasthan had registered a phenomenal increase of 35.48 per cent over 1971 population. Regional pattern of growth rate in western Rajasthan shows that extreme hot arid region district (Barmer) is on top having 32.55 per cent growth rate whereas Indira Gandhi Canal Region (Ganganagar) district has least population growth of 10.06 per cent only. In 1981, the average urban growth rate had been 54.52 per cent. In 2001, the rural density of population was 83 persons per sq. km.

The branch of statistics which describes the data is called descriptive statistics. After collection, the data are presented in form of tables, diagrams or graphs. This facilitates the further analysis of data. The most basic form of data analysis is descriptive analysis of data collected and presented for the study. The descriptive statistics summarises the data and helps to find the representative value of the entire data set and the variation of the other values in the data set around the representative value. The descriptive statistics includes the measures of central tendency or averages, measures of dispersion or scatteredness, measures of skewness and the measures of kurtosis. Before going to discuss the R codes for various measures of descriptive analysis, we would first discuss in brief about these measure. This will help in better understanding of the R codes.

Measures of central tendency or averages: They are the representative values of the data series. They give an idea about the concentration of the values in the central part of the distribution of the data. There are five types of averages. They are 1. Arithmetic Mean (A.M.) 2. Geometric mean (G.M.) 3. Harmonic Mean (H.M.) 4. Median 5. Mode.