eChapters

Basics of Technology Commercialization
Technology, a ubiquitous force shaping our world, can be understood from various angles. Here’s a breakdown of its definition, functions, and the fascinating process of its advancement.
Definition
At its core, technology is the application of scientific knowledge for practical purposes. It’s not just about gadgets and gizmos; it encompasses a vast array of systems, processes, and tools that we use to transform the world around us and solve problems. This includes everything from the humble wheel to the intricate workings of artificial intelligence.
Functions
Technology serves a multitude of functions, broadly categorized into:
Simplifying tasks: From automated manufacturing to self-driving cars, technology automates and streamlines processes, saving time and effort.
Enhancing communication and connection: From the internet to social media, technology bridges geographical barriers and fosters global interaction.
Expanding our knowledge and understanding: Scientific instruments, telescopes, and advanced data analysis tools enable us to explore the universe, delve into the microscopic world, and gain deeper insights into various fields.

AI-driven animal husbandry is advancing through machine learning, computer vision, IoT, big-data analytics, wearable devices and automated equipment. This chapter considers future applications for monitoring animal health, behaviour, nutrition, reproduction and environmental conditions. Predictive systems can support early disease detection, precision feeding, improved breeding and reduced production costs. However, high implementation costs, data privacy, inadequate infrastructure and limited digital knowledge remain significant obstacles, particularly in developing regions. Future research should prioritize affordable, user-friendly and explainable technologies suited to smallholders. Ethical AI, climate adaptation, multimodal data integration, omics analysis and interdisciplinary collaboration among animal scientists, engineers, policymakers and farmers are identified as essential research directions.

6.1 Introduction

This chapter describes the development of data logger of agricultural field parameter monitoring system with LabVIEW. The system comprises of two sections- Fields device and data logger. Field device comprises of Arduino, smoke detector (for sensing hazardous gases), soil moisture sensor, temperature sensor, humidity sensor, water level sensor, battery, display unit, 2.4 GHz RF modem. Data logger comprises of Arduino, display unit, 2.4 GHz RF modem, power supply, PC with LabVIEW. The system is designed to monitor the environment and agriculture field parameters. The field device is designed to collect all physical parameters and communicate it to the receiver end with LabVIEW. For establishing communication between two sections 2.4GHz RF modem is used, which can be replaced with any other modem also.Fig.6.1 shows block diagram of field device and Fig.6.2shows block diagram of data logger.Table.6.1 & Table.6.2 shows the component list required to develop the field device and data logger.

IoT networks and smart sensors enable continuous livestock monitoring, overcoming the delay and subjectivity associated with manual observation. This chapter reviews sensor types, communication systems, cloud and edge computing, and their applications in animal-health and behaviour tracking. It proposes a framework integrating multimodal sensors with IoT networks and machine-learning models such as CNNs and LSTMs. The system can detect abnormalities, predict health conditions and provide real-time decision support. Practical benefits include earlier intervention, improved welfare, reduced labour and more efficient production. The chapter also addresses implementation problems, costs, connectivity, data security, technical reliability and ethical considerations associated with constant digital monitoring of animals.

4.1 Introduction
Having explored the direct, “hands-on” approach of in situ observations in the previous chapter, we now turn our attention to remote sensing. In contrast to in situ methods, remote sensing allows us to gather information about the atmosphere without physically touching it. Instead, it relies on the detection of electromagnetic radiation (or acoustic waves) that has been emitted, reflected, or scattered by the atmosphere or its constituents.
This chapter will focus specifically on ground-based remote sensing platforms. Positioned on or near the Earth’s surface, these instruments offer unique advantages for continuous monitoring of the atmosphere above a fixed location, providing valuable insights into vertical profiles, temporal evolution, and localized phenomena. From radars that peer into storms to lidars that probe aerosols and winds, and radiometers that measure emitted radiation, ground-based remote sensing provides a vital link between highly localized in situ measurements and the broader spatial coverage offered by satellite and
airborne systems.
We will delve into the fundamental principles behind several key groundbased remote sensing instruments used in meteorology. This will include an examination of how they interact with the atmosphere, the types of atmospheric parameters they can measure (such as precipitation, wind, temperature, humidity, aerosols, and trace gases), and the strengths and limitations of each technique. By understanding the capabilities of these ground-based “eyes” on the sky, we can better appreciate their contribution to weather forecasting, atmospheric research, and environmental monitoring.

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.

Precision feeding aims to supply animals with appropriate nutrients according to their changing physiological state, growth, production and environment. Conventional feeding plans are often static and may lead to waste, increased costs and environmental losses. This chapter introduces an AI-enabled framework integrating animal data, feed composition, growth characteristics and external factors. Regression, ensemble learning and other machine-learning models predict feed intake and weight gain, while linear programming and metaheuristic optimization generate efficient allocation plans. A decision-support system provides recommendations in real time. The approach can improve feed conversion, nutrient utilization, profitability and sustainability while reducing overfeeding, resource wastage and the environmental impact of livestock production.

8.1 Introduction
Having explored the intricate processes of meteorological data collection, storage, and the crucial steps of quality control and preprocessing, we nowturn our attention to how this vast and meticulously  prepared information is actually analyzed and interpreted. This brings us to the realm of statistical methods, which are indispensable tools for making sense of the inherent variability, uncertainty, and sheer volume of meteorological data. Meteorological phenomena are complex, influenced by numerous interacting factors, and often exhibit chaotic behavior. Statistical methods provide the rigorous framework necessary to quantify these complexities, identify underlying patterns, assess relationships between different atmospheric variables, and ultimately, draw robust conclusions. From summarizing observed climate patterns to evaluating forecast accuracy, and from detecting long-term trends to predicting future weather events, statistics permeate nearly every aspect of modern meteorology.
This chapter will introduce the fundamental statistical concepts and techniques applied in meteorological analysis. We will begin with descriptive statistics to summarize and visualize data, then move to inferential statistics to draw conclusions about atmospheric processes from samples. We will delve into methods for understanding relationships, such as correlation and regression analysis, and explore techniques for analyzing time series data to identify trends and periodicities. Furthermore, we will touch upon spatial statistics for gridded data and the growing role of machine learning approaches. By mastering these statistical tools, meteorologists can transform raw data into meaningful insights, enhance predictive capabilities, and advance our scientific understanding of the Earth’s atmosphere.

The history of Patent law in India starts from 1911 when the Indian Patents and Designs Act, 1911 was enacted. The present Patents Act, 1970 came into force in the year 1972, amending and consolidating the existing law relating to Patents in India. The Patents Act, 1970 was again amended by the Patents (Amendment) Act, 2005, wherein product patent was extended to all fields of technology including food, drugs, chemicals and micro organisms. After the amendment, the provisions relating to Exclusive Marketing Rights (EMRs) have been repealed, and a provision for enabling grant of compulsory license has been introduced.

Basic knowledge of statistics is important to understand animal breeding analysis. In this chapter, the hypothesis testing, principles of experimental design, types of data and probability distribution are described with examples using appropriate software.

Cartography has much back history than what we seen in portable form by its name or definition. Many prehistoric painting of caves had their own time frame record of maps, artifacts and design have been preserved hoping that they are sufficient to the era of forgotten habit and culture, and treasure explicit ancient world. The ancient wall painting of seventh millenium BC, might have indication of oldest maps in the world and the painting is assumed to be the location of ancient city Antolia as Mellaart, depicted 1967. Onward 600 BC, the modern form of cartography started to develop map. The pioneer contribution in map designing and development, old Greek and Roman were main contributor in this direction. One Greek Philoshopher Ptolemmy, multi-talented genius are recalled in this context, who later credited with first documented map of world during production of Geographica, is a treatise on cartography, very soon one Arabian Scholar Muhammad Al-Idrisi prepared a medieval Atlas involving knowledge of world collected by Arabic merchants in 1154.

Fundamentally toposheet represents a kind of map in which topographic features of particular area is given details on the map features in two dimensional land surfaces. It shows both horizontal and vertical portion of the terrain with a combination of contour lines, contours, symbols, labels and related representation together in one piece of paper. Toposheet portray the shapes, location of mountains, many natural and manmade features, and the toposheet combinedly uses for extracting accurate information about particular area containing land surface features.

Introduction

Indian fisheries and aquaculture is an important sector of food production providing nutritional security, besides livelihood support and gainful employment to more than 14 million people, and contributing to agricultural exports. With diverse resources ranging from deep seas to lakes in the mountains and more than 10% of the global biodiversity in terms of fish and shellfish species, the country has shown continuous and sustained increments in fish production since independence. The sector constitutes about 6.3 percent of the global fish production and contributes to 0.91 percent of the GDP and 5.23 percent of the agricultural GDP. The total fish production of 13.34 million metric tonnes presently has nearly 74 percent contribution from the inland sector and rest by the marine sector. Paradigm shifts in terms of increasing contributions from inland sector and further from aquaculture have been significant over the years. With high growth rates, the different facets, viz., marine fisheries, coastal aquaculture, inland fisheries, freshwater aquaculture, and coldwater fisheries are contributing to the food basket, health, economy, exports, employment and tourism of the country. More than 50 different types of fish and shellfish products are being exported to 75 countries around the world. Fish and fishery products have presently emerged as the largest group in agricultural exports from India, with 13.77 lakh tonnes in terms of quantity and ` 45,106.89 crore in value. This accounts for around 10% of the total exports and nearly 20% of the agricultural exports, and contribute to about 0.91% of the GDP and 5.23% to the Agricultural GDP of the country.

The Pradhan Mantri Matsya Sampada Yojana (PMMSY) is an initiative launched by the Government of India to establish a comprehensive framework and reduce infrastructural gaps in the fisheries sector. The scheme was announced by the Finance Minister, Smt. Nirmala Sitharaman during her speech in the Parliament of India while presenting the Union budget for 2019–20 on 5 July 2019. The government intends to place India in the first place in Fish production and processing by implementing Blue Revolution. This scheme is in line with government’s aim to double the farmers’ income by 2022–23.The policy envisages integrating all the fishermen with agricultural farmers and providing all the facilities available through various farmer welfare schemes to the fishermen. A new dedicated Department of Fisheries was constituted in a newly carved out Ministry of Fisheries, Animal Husbandry and Dairying to implement this and other policy initiatives of the government.

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

5.1    TRIPS
    
5.2    WTO
    
5.3    WIPO
    
5.4    Paris Convention
    
5.5    Berne Convention
    
5.6    Budapest Treaty
    
5.7    Madrid Agreement Marks (MAM)
    
5.8    The Patent Law Treaty (PLT)

Key Facts

• Global fish production peaked at about 171 million tonnes (MT) in 2016, with aquaculture representing 47 perc ent of the total and 53 per cent of non-food uses (Global capture fisheries production was 90.9 MT in 2016)

• World fish production is projected to increase by 15% in the next 10 years, reaching around 200 MT per year

• The total first sale value of fisheries and aquaculture production in 2016 was estimated at USD 362 billion, of which USD 232 billion was from aquaculture production

• Fish (including shellfish) provides essential nutrition for 3 billion people and at least 50% of animal protein and minerals to 400 million people from the poorest countries

• 10-12% people, i.e. over 870 million people, depend on fisheries and aquaculture

• Aquaculture is the world’s fastest growing food production system, growing at 7% annually

• Women account for 19% of all people directly engaged in the fisheries and aquaculture sector, and over 50% when including the post-harvest sector

Climate Change: A global externality

Climate Change (CC) is a result of the externality associated with greenhouse gas emissions. It entails costs that are not paid for by those who create the emissions.

Distinguished Features from Other Externalities

• Climate change is global in its causes and consequences;

• The impacts of climate change are long-term and persistent;

• Uncertainties and risks in the economic impact are pervasive;

• There is a serious risk of major, irreversible change with non- marginal economic effects

Definition of Climate Change

Climate change refers to a statistically significant variation in either the mean state of the climate or in its variability, persisting for an extended period (typically decades or longer).

Large Language Models (LLMs) have emerged as one of the most significant developments in artificial intelligence, transforming the way machines understand, process and generate human language. Their evolution reflects a progression from rule-based systems and statistical language models to neural networks, word embeddings, recurrent architectures and, ultimately, Transformer-based models. This chapter provides a comprehensive overview of this evolution and explains the technological breakthroughs that enabled modern LLMs. It discusses early language modelling approaches, the transition from N-gram models to distributed representations, and the contribution of deep learning and attention mechanisms. The development of influential models such as BERT, GPT, T5, XLNet, PaLM and LLaMA is considered to illustrate increasing capabilities in language understanding and generation. The chapter also examines core LLM capabilities, including question answering, dialogue, summarization, translation and multimodal processing. Finally, it highlights challenges involving hallucination, computational requirements, privacy, bias and responsible deployment, while outlining opportunities for future intelligent and trustworthy AI systems.

Human communication rarely depends on a single information modality. People simultaneously interpret language, images, sounds, gestures and contextual signals to understand their surroundings. Multimodal Large Language Models (MLLMs) seek to reproduce this capability by integrating text, vision and speech within unified computational frameworks. This chapter introduces the evolution and technological foundations of multimodal intelligence and explains how different data modalities can be aligned through shared representations. It examines the architectural components, training paradigms and evaluation methods used to develop multimodal systems. Applications are discussed across healthcare, autonomous systems, education, accessibility, conversational interfaces and intelligent assistants. For example, healthcare applications can combine electronic health records, spoken clinical interactions and medical images to provide richer contextual understanding. Similarly, autonomous vehicles can integrate visual road information, spoken instructions and textual navigation data. The chapter also considers challenges involving multimodal alignment, computational requirements, data quality, hallucination, interpretability and safety. It concludes by highlighting future research toward more natural, context-aware and human-centered AI systems capable of seeing, listening, reasoning and communicating

Large Language Models are increasingly transforming software engineering by enabling automated code generation, completion, debugging, translation and documentation. This chapter examines how Transformer-based architectures trained on source code and natural-language datasets have expanded the capabilities of intelligent programming systems. It begins with the architectural foundations of code-oriented LLMs and explains how self-attention enables models to capture complex relationships within programming languages. The chapter discusses applications throughout the software development lifecycle, including natural-language-to-code generation, code completion, code translation, refactoring, automated unit testing, bug detection and documentation generation. It also examines training and fine-tuning methodologies and evaluation metrics designed to assess code correctness, functionality and quality. Deployment considerations such as latency, security, computational cost, licensing and human oversight are addressed. Particular attention is given to risks associated with hallucinated code, unreliable outputs and generalization beyond training distributions. Emerging directions, including Retrieval-Augmented Generation, continual learning and formal verification, are considered as mechanisms for improving reliability and developer trust.

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