eChapters

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.

5.1. INTRODUCTION

Mean and standard deviation summarizes the salient features of a data set. But they do not describe adequately the full characteristics of the data. For an adequate description of the characteristics of a data set, a convenient method is to plot a frequency distribution curve. Such curve is constructed by first drawing a frequency polygon, which may then be smoothened to a frequency curve, indicating the distribution. The frequency curve or distribution of specific biological variables generally has a specific form and plays a very important role in statistical theory and practice. It is customary to describe a curve in terms of its symmetry or assymmetry and its flatness or peakedness.

The following types of distribution are commonly seen in the biological world:

3.1. INTRODUCTION

In this chapter, we will study the measures of central tendency which is a numerical method to explain the data in brief. We can see examples of summarizing a large set of data in day to day life like average marks obtained by students of a class in a test, average rainfall in an area, average production in a factory, average income of persons living in a locality or working in a firm etc.

One of the most important objectives of statistical analysis is to get one single value that describes the characteristic of the entire mass of data. Such a value is called the central value or an ‘average’ or the expected value of the variable. This single value is the point of location around which individual values cluster and, therefore, called the measure of location. Since this single value has a tendency to be somewhere at the center and within the range of all values it is also known as the measure of central tendency. The purpose of computing an average value for a set of observations is to obtain a single value which is representative of all the items.

4.1. INTRODUCTION

Dispersion is the extent to which values in a distribution differ from the average of the distribution. To quantify the extent of the variation, there are certain measures namely:

1. Range
2. Quartile Deviation
3. Mean Deviation
4. Standard Deviation

Range and Quartile deviation measure the dispersion by calculating the spread within which the values lie. Mean Deviation and Standard Deviation calculate the extent to which the values differ from the average.

12.1 Introduction
 
The design of the experiment includes (i) planning of the experiment (ii) obtaining the relevant information from it regarding the statistical hypothesis under study and (iii) making a statistical analysis of the data. Design of experiment may be defined as the logical construction of the experiment in which the degree of the uncertainty with which the inference is drawn may be well defined. It may also be redefined as the choice of treatments, the method of assigning treatments to experimental units and arrangement of experimental units in varies patterns to suit the requirement of particular problems, are combined known as the design of experiment.

1. Introduction

The term "sustainable development" was originally used in a 1953 article by Joseph L. that was published in Land Economics. The notion was employed to depict the degree or pace at which an environmentally friendly resource is employed for a particular objective while guaranteeing its long term availability. Organizational dynamics, social interactions, and cultural norms all change with the times. These changes are making environmental problems worse and bringing up questions about pollution and the requirement for a safe natural environment. They are being caused by changing human behavior and population expansion (Kami?ska, 2021; Gaffar et al., 2021). Organizations have been forced to review their approaches to dealing with these urgent environmental issues as a result of the increased awareness. Academic libraries have been particularly well-positioned to implement eco friendly practices because they are service-oriented establishments to adopt eco-friendly practices in their operations (Pangail, 2017).

ABSTRACT

With the advancement of mobile technology, the usage of mobile application has increased significantly which leads to change the life style of common people. Library is also not an exception in this context. Library and Information Science Professionals have started to incorporate the changes from the beginning of ICT advancement with an idea “library should be within thumb of people”. Mobile application plays vital role to fulfil the objective of library. Most of the library has started or planning to start the service to its user with various services effectively. But, the process to provide library service through mobile application is not satisfactory till date. No too much usage of libraries mobile apps can be seen; even which mobile applications are in use, most of them are almost inactive form.

But the various recent surveys show us the tremendous growth of the usage of mobile application among people. According to Google, (2017) the user survey shows that mobile can fast-track time to purchase by 20% through facilitating efficiencies in decision-making and enhanced team collaboration, particularly with more complex purchases.

Abstract

Training is essential but expensive. Hence, training impact is a crucial measure. National Agricultural Research System (NARS), India employs about 30000 scientists. To effectively manage such huge scientific-capital, Indian Council of Agricultural Research (ICAR) establishedCentres for Advanced Faculty Training (CAFT) and National Academy of Agricultural Research Management (NAARM). CAFT train faculty of State Agricultural Universities (SAUs), while NAARM trains scientists of ICAR institutes and SAUs. Assessing impact of these training programmes becomes essential. Application of knowledge, skills and attitude (KSA) acquired during training back at job environment is training-transfer that addresses learning, behaviour and results levels of training impact. This chapter critically analyses training-transfer of NAARM and CAFT programmes, suggests empirical framework to enhance impact of NAARM and CAFT programmes and thus contributes to literature. Policy strategies suggested in this paper hold wider applicability.

1. Introduction

With the advent of globalization and subsequent economic liberalization, the focus of Indian agriculture is shifting from “subsistence” to “commercial” mode, where farming is viewed as a “business” rather than “life-support” system of poor farmers. Enabling farmers “decision-making” capacity is a primary driver of success in the market-oriented agriculture. This “commercial focus” has brought tremendous changes in the “nature” and “mode” of extension work. On the other hand, extension work organizations in developing countries like India, are being impacted by sharp decline in public sector funding, rapid technological innovations especially in the Information and Communication Technologies (ICTs), and the shift to a knowledge-based workforce. These developments created new opportunities for the extension workers to perform the information dissemination, capacity building and technology application more effectively. 

ABSTRACT

Mandarin Orange (Citrus reticulata Blanco.), known as ‘Suntala’ to the local populace, is a major cash crop and was once a pride of Darjeeling hills. It is grown in approximately 930 ha with an annual production of 148.224 metric tones and annual turnover of Rs. 25 crores approximately. Since early 1970s, orange orchards in the Darjeeling hills started to be unproductive inflicting huge economic loss showing severe citrus decline. Gradually the malady assumed alarming proportions and threatening the cultivation of the crop. The major causes for the bottleneck of the citrus industry in Darjeeling hills  is  the heavy incidence of insect pests, diseases and nutritional deficiencies.

4.1 Introduction

The objective of this chapter is to design a mobile app for agricultural field automation with Blynk app. Mobile app is designed to control the water management in field as per requirement. The complete system comprises of two sections sensor node and mobile app. Sensor node comprises of Node MCU, Power supply, LCD, Relay board, soil sensor and Ultrasonic sensor. The system is designed to establish control and communication with specific agricultural field to take sensory data from soil and ultrasonic level sensor and control the PUMP IN motor and PUMP OUT motor with the help of mobile app.Fig.4.1 shows a block diagram for a system to control specific agricultural field with mobile app and IoT modem.

ABSTRACT

Future is the era of ICT (Information Communication Technology) which is equally applicable in the field of Agriculture. Farmers database will be an important part of ICT which is required for smooth functioning of various agricultural activities like Farmer’s Trainings, Demonstrations, Crop monitoring, Support to farmers, implementation of various government schemes etc.  This software records farmers Name, Phone number, Sex, Category (SC/ST/Gen),  Number of dependents, Annual agricultural income, whether fully or partially dependent, Land holding size,  Major crops, Village, Block, Sub-division, Land situation (Plains/ Low Hills/ Mid Hills/ High Hills), Soil Reaction, Soil type, (Highly Acidic/Moderately /acidic/Neutral/Alkaline/Highly Alkaline), NPK status, Irrigation facility and information regarding Cattle. From the above record various reports can be generated according to the needs of the scientific personnel. For example soil scientist will have record of soil pH & NPK status of different farmers across the district. Scientist can filter the database to get farmers of a particular soil type to conduct training;

Circular economy principles provide a framework for developing materials that minimize resource consumption and waste while maintaining functional performance throughout their life cycle. This chapter examines the development of green nanocomposites through sustainable sourcing, circular material design, appropriate processing technologies, and functional enhancement. It discusses strategies for selecting renewable raw materials, designing composites for circularity, applying nanotechnology to improve performance, and evaluating environmental impacts through life-cycle assessment. Applications of sustainable green nanocomposites and challenges in implementing circular economy principles are also considered. The chapter concludes by highlighting future innovations in synthesis, design, processing, and evaluation needed to expand the contribution of green nanocomposites to global sustainability.

5.1 Introduction

The chapter describes the development of local web server on the laptop, to control the actuator from the server. Web server is used as remote control for the system. The system is designed to control water pump with respect to the soil sensor values. It comprises of NodeMCU, soil sensor, LCD, two relay boards for pump in and out the water from field.

The Central Nervous System not only consists of the Brain but also involves the Spinal cord.

The spinal cord is responsible for conveying information from the Brain to the rest of the body via the peripheral nervous system. The spinal cord can be divided into 4 functional segments.

1. C1-C5 (Upper Cervical Segments)

2. C6-T2 (Brachial Plexus)

3. T3-L3 (Thoraco - Lumbar)

4. L4-S3 (Lumbo-Sacral Plexus )

It is important to note that the motor and sensory function of the pelvic limbs are not only controlled by the Lumbo-Sacral plexus but also involve the Thoraco Lumbar segments (T3-L3) of the spinal cord. It is important to understand not only the neuroanatomy, but also the functional anatomy before approaching a neurological patient.

8.1 Introduction to Dictionary in Python

A dictionary is a collection which is unordered changeable and index. In python dictionaries are written within curly brackets, and they have keys and values. Means the dictionary contains two things first is the key and the second is the value.

8.1.1 Create and Print a Dictionary

Example

dict1={‘brand’:‘Suzuki’,‘model’:‘dzire’,‘year’:2020}

print(dict1)

Accessing items: you can access the items of a dictionary by referring to its key name, inside square brackets.

Example

Accessing items: there is also a method called get() for the same task.

dict1={‘brand’:‘Suzuki’,‘model’:‘dzire’,‘year’:2020}

print(dict1)

y=dict1.get(?model”)

print(y)

Digital dairy farming integrates IoT, artificial intelligence, machine learning, big-data analytics, cloud computing, sensors and automation to improve herd management. This chapter explains how wearable devices and monitoring systems support early disease detection, welfare assessment, milk-yield analysis and precision feeding. Automated milking, feeding and other operations reduce manual labour, improve consistency and lower operating costs. The architecture of smart dairy systems—including sensors, wireless communication and decision algorithms—is examined alongside environmental and economic benefits. Barriers include high initial investment, limited digital skills, poor rural connectivity and cybersecurity risks. Future developments may include AI-enabled autonomous dairy farms and interconnected, sustainable agricultural ecosystems.

ABSTRACT

In the recent years, the notion of ‘digital divide’ has been widely researched and has attracted much debate and speculation for its economic, social and political consequences. Existing studies reveals, the gap that exists between those who have access to ICTs and those who do not create exclusion, endanger social integration and hamper economic growth. The digital divide has many dimensions and can be categorized as global, regional and national. At national level, there is no single divide, but multiple divides: for instance; within countries, between men and women, young and elderly, rich and poor and most importantly rural and urban. The present paper is mainly focused on India and tries to explore the problem of digital divide mainly in rural-urban India. In the context of present paper, digital divide essentially means teledensity, mobile and Internet divide between the rural and urban areas. The paper reveals that obstacles such as illiteracy, lack of skills, infrastructure,  and investment in rural areas must be tackled if India is to diminish the gap of digital divide. At the government front, it should put thrust towards: connectivity provision, content creation, capacity augmentation, core technologies creation and exploitation, cost reduction, competence building, community participation and commitment to the deprived and disadvantaged which would definitely help in bridging digital divide.

1. Introduction

In the era of digital technology, libraries have expanded their role beyond their initial role as storage facilities for physical material. Libraries have become essential in providing access to electronic resources and promoting lifelong learning due to the emergence of e-learning. E-learning refers to the use of digital technology to access learning materials outside of a traditional classroom setting. E-learning is emerging as a powerful influence in higher education, with a presence on nearly every campus and a growing number of college and university courses. The environment is characterized by growth and dynamism, where fluidity and change are the social, institutional, and technological norms (Mishra & Gupta 2019).

Technology has revolutionized education, turning it into e-learning and internet based learning. This evolution provides individuals with the opportunity to have more freedom, convenience, and the ability to work virtually. Libraries must modify their resources and services to meet the emergence of e-learners. Academic libraries have now established connections with web-based services and e-resources, which are moving the profession forward at a rate never before seen. (Ramkumar & Kannan, 2023)