eChapters

Pulses may be defined as the dried edible seeds of cultivated legumes. They belong to the family of peas, beans and lentils (Family: Fabaceae). English word pulse is taken from the Latin word ‘Puls’, meaning pouage or thick pap. Pulses are a large family and various species are capable of surviving in very different climates and soils. Pulses are cultivated in all parts of the world and they occupy an important place in human diet.

In this chapter we will cover all the basics libraries and some frameworks of machine learning, deep learning and computer vision. We will discuss all about their use with an example and instruction which will states the commands to install to those libraries for building any project in our system. So let’s begin

What is a Library?

Library is an aggregate of features and methods that allow you to take many actions without getting your own code written.

For instance, the libraries that you have to know are if you work with data, numpy, scipy, pandas, etc. They have very simple data processing functions that save you time for small tricks.

3.1 Introduction
Understanding the Earth’s atmosphere—its intricate dynamics, ever-changing weather patterns, and long-term climate trends—hinges critically on the quality and detail of the observations we collect. While remote sensing technologies offer broad perspectives from a distance, a fundamental and indispensable category of atmospheric data comes from in situ observations. The term “in situ,” meaning “in place” or “on-site,” precisely describes the nature of these measurements: they are direct, physical readings taken within the atmospheric volume itself.
These direct observations are the bedrock of meteorological science. They provide the ground truth for atmospheric conditions, offering unparalleled precision in measuring parameters such as temperature, humidity, atmospheric pressure, wind speed and direction, and the characteristics of precipitation. Unlike indirect sensing, in situ methods involve instruments physically interacting with the air, yielding highly accurate and localized data points. This granular detail is crucial for a multitude of applications: it allows for the precise calibration of remote sensing instruments, provides essential validation for complex numerical weather prediction models, and enables scientists to dissect and understand fine-scale atmospheric processes that influence local weather.

In an era defined by rapid technological advancements and an ever-changing climate, the need for precise, adaptive, and actionable weather intelligence has never been greater. As global weather patterns become more erratic and extreme events increasingly disrupt lives and livelihoods, the integration of real-time field response into weather intelligence systems represents a transformative opportunity. Just as companies like Google and Tesla leverage real-time user inputs to continuously update their software, refine artificial intelligence (AI) models, and provide timely road-and-traffic guidance, weather intelligence systems that are continuously refined by feedback from farmers, fishermen, and other local stakeholders offer the promise of dynamic, “live” insights that can revolutionize agricultural practices and community resilience (Fig.10.1). This chapter delves into the critical importance of collecting and synthesizing feedback from those who are on the frontlines of weather impacts. It explores the methodologies and benefits of integrating real-time field data into weather intelligence systems, drawing parallels to the iterative processes observed in hightech industries. In doing so, we highlight how these innovations can maximize

Abstract

The main aim of the article is to identify the strengths, weaknesses, opportunities and threats of current IPR system in context to Indian regime and also to understand overall planning as well as important tactics and means to use IP legal protection system to fully safeguard one’s legitimate rights and interests, obtain and maintain competitive edges, restrain competitors and seek optimal economic performance by selection of appropriate IP strategies. This article also proposes ways to select IP strategy based on BCG matrix and product mix.

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.

Introduction

Spatial datasets always exhibit two common features (i) the occurrence of a few very large concentrations (hot-spots) and (ii) the presence of data below the detection limit (black spots). Extreme values can strongly affect the characterization of spatial patterns, and subsequently the prediction. Several approaches exist to handle strongly positively skewed histograms (Saito and Goovaerts 2000). One common approach is to first transform the data (e.g. normal, Box Cox or lognormal), perform the analysis in the transformed space, and back-transform the resulting estimates. Such transform, however, does not solve problems created by the presence of numerous black spot data, since either it yields a spike of similar transformed values or, in the case of the normal-score transform, it requires a necessarily subjective ordering of all equally-valued observations. Moreover, except for the normal score transform (Deutsch and Journel 1998), it does not guarantee the normality of the transformed histogram, which is required to compute confidence intervals for the estimates. Last, the back-transform of estimated moments is not straightforward and can introduce bias if not done properly (Saito and Goovaerts 2000); for example, lognormal kriging estimates cannot simply be exponentiated. Another way to attenuate the impact of extreme values is to use more robust statistics and estimators. The non-parametric approach of indicator kriging (IK) falls within that category (Journel 1983, Goovaerts 2001). The basic idea is to discretize the range of variation of the environmental attribute by a set of thresholds (e.g. deciles of sample histogram, detection limit, regulatory threshold) and to transform each observation into a vector of indicators of non-exceedence of each threshold. Kriging is then applied to the set of indicators and estimated values are assembled to form a conditional cumulative distribution function (ccdf). The mean or median of the probability distribution can be used as an estimate of the concentration of the material in question (Barabas et al. 2001, Cattle et al. 2002, Goovaerts et al. 2005).

Industrial ice cream manufacturing is an integrated system involving food science, dairy technology, mechanical processing, refrigeration, quality assurance, hygiene, packaging, maintenance, utilities, manpower, and cold-chain management. This chapter explains how each manufacturing stage influences the next, from raw-material receiving and mix preparation through pasteurization, homogenization, aging, freezing, filling, hardening, storage, and distribution. It emphasizes process discipline, equipment reliability, trained personnel, KPI-based management, preventive maintenance, and cold-chain integrity. The chapter also addresses quality control, food safety, sustainability, digitalization, and operational challenges. It provides practical guidance for factory managers seeking consistent quality, productivity, controlled costs, reduced losses, and long-term operational excellence

I. INTRODUCTION

Information technology has heralded a new era of Communication System. It has revolutionized the whole commercial world. The mother earth looks like a global village where people sitting thousands of miles away can talk face to face, like people sitting across the table. Business men and consumers are using computer to create, transmit and store information in the electronic form. Traditional form of paper documents are being discarded for the reason that electronic method is Cheaper, easier to store, retrieve and communicate. Though these advantages are obvious but people were reluctant to transact business in electronic form due to lack of legal frame work, validating these transactions. All the paper transactions involved writing and signature to conclude them. These writings and signatures posed a great hurdle for electronic governance and electronic commerce for validating and enforcing them. The law of Evidence is based on paper made documents which should have been signature. Since electronic commerce eliminates the need for paper based transactions, hence to facilitate e-commerce the need for legal change has become an urgent necessity. International trade, though the medium of e-commerce, is growing by leaps and bounds for the past few years. Many countries have switched over from traditional paper based commerce to e-commerce. The instantaneous means of communications through internet have been a great menace to copy right in different areas David Teather from London has reported that the International Federation of the Phonographic Industry say that the market in year 2000 was worth $36.9 billion, a fall of 1.3 per cent on the previous year. The biggest decline in the sale of C.D. singles was 39% and globally it fell by 14%. The growth in popularity of Napster and to contain the damage. Napster is in the process of removing copyrighted material from its site.¹ But the powers of the computer are so great that the law enforcement agencies are facing a real challenge to contain its onslaught. Internet telephony is a common sight at cyber cafe, despite the fact that it is illegal.2 B.B. Nanda and R.K.Tewari observe that “ internet paedophilia, computer network break-ins, industrial espionage, Child pornography password sniffling, spoofling, telecommunications fraud, software piracy, e- mail bombing and spamming, and the availability of illicit or unlicensed products and services are offences already making themselves felt. Emerging problems include credit card fraud, cyber terrorism, cyber laundering and criminal use of secure internet communications. The huge growth of internet will breed cyber fraud with great ease especially in the area of share-holdings of companies.3 The present weak electronic system of payment without adequate safeguard is posing a serious risk for unauthorised withdrawals from banks and counter money laundering operations. Software piracy is a boom business and video and phonographic industries are sinking day by day globally.4 The UNCITRAL Model law provides for equal legal treatment of users of electronic communication and paper based communication. In pursuance of a declaration made ‘by Member Countries, the World Trade Organisation (WTO) is about to form a work programme to handle its work in this area including the formation of multilateral trade deals through the medium of electronic commerce. The paper discusses, briefly, the meaning of cyber space the origin and development of intemet and e commerce its uses and abuses. Certain provisions relating to Information Technology Act, 2000 have been briefly discussed to apprise oneself about its effectiveness in meeting the challenges posed by e commerce in different areas.

I. PUBLIC POLICY CONCERNS

The working of the Trade Related Aspects of Intellectual Property Rights Agreement (TRIPS) Agreement, 1995 along with World Intellectual Property Organization (WIPO) and the jurisprudence developed for the last twenty years is a testimony to the fact that although TRIPS is geared to promote the protection of intellectual property rights internationally, yet some controversial public policy issues need to be addressed and solutions devised for addressing these public policy issues otherwise there is a possibility of seeing the demise of TRIPS Agreement or sidetracking it by some other methods which may not be conducive for maximizing the welfare of the international trading community. As all of us know that intellectual property and international trade are mutually inclusive and the minimum standards set forth in the TRIPS Agreement are economically value oriented. The trade offs among and between the members of the WTO need to be balanced on an even keel, otherwise the possibility of camaflouging the TRIPS Agreement and the attendant jurisprudence becomes real. The TRIPS Agreement addresses seven categories of IP rights such as; Copyright and related rights; Patents; Trademarks and Service marks; Geographical Indications; Undisclosed Information or Trade Secrets; Industrial Designs ; and Layout Designs of Integrated Circuits and read with other WIPO Treaties obligates the members of the World Trade Organization (WTO) to reduce distortions and impediments to international trade by taking into account the effective and adequate protection of intellectual property rights as well as the procedures for the enforcement should not become barriers to international trade. The TRIPS Agreement also recognizes the need to cater to the special needs of the developing and least developed countries.

Intellectual property (IP) encompasses creations of the human intellect, including inventions, artistic and literary works, as well as symbols, names, and designs. Intellectual property rights (IPR) are legal mechanisms thaprovide creators with exclusive rights to use, manage, and profit from their creations. These rights help protect the interests of innovators and promote continued creativity and innovation.
Intellectual Property Rights refer to the legal entitlements granted to individuals or organizations over their creative and intellectual works, such as patents, trademarks, and copyrights. These rights grant exclusive control or usage privileges for a specific duration, often creating a temporary monopoly. While the concept of intellectual property began to take shape in the 19th century, it was only during the 20th century that it was formally integrated into global legal frameworks.
Intellectual Property Rights (IPR) safeguard the creations of the human mind, such as inventions, artistic and literary works, brand names, logos, designs, and images used in commerce. These rights help foster innovation by allowing creators and inventors to benefit economically from their work.
Key Points
1. Intellectual Property Rights are legal protections acquired by creators or owners of intellectual works.
2. Intellectual property encompasses intangible assets that result from human creativity and thought.

Clinical decision support systems assist healthcare professionals in diagnosis, treatment planning and patient management. This chapter introduces intelligent clinical decision support systems and explains how computational intelligence overcomes the limitations of conventional rule-based approaches. It examines artificial neural networks, fuzzy logic, evolutionary algorithms, swarm intelligence and deep-learning models. A computational architecture integrating clinical data, learning algorithms, fuzzy inference and optimization is presented. Applications in heart-disease diagnosis, diabetes-risk prediction and medical-image analysis illustrate the practical value of these systems. The chapter also addresses interpretability, interoperability, data quality, privacy, legal responsibilities, real-time processing and the challenges involved in deploying intelligent decision-support tools in clinical environments.

Early and accurate crop-disease diagnosis is essential for preventing production losses and reducing unnecessary pesticide use. Manual diagnosis can be slow, subjective and difficult to apply across large agricultural areas. This chapter introduces computational approaches using artificial neural networks, fuzzy logic, support vector machines, deep learning and hybrid optimization models. It discusses image acquisition, preprocessing, feature extraction, pattern recognition and classification of disease symptoms from leaves, crop canopies and sensor data. Integration with precision-agriculture systems, mobile advisory applications and real-time monitoring enables timely intervention. Challenges involving data quality, field variability, model interpretation and large-scale deployment are examined through practical examples and case studies.

Decision Support Systems convert farm data into recommendations that improve livestock productivity, health and sustainability. This chapter explains data-driven, model-driven and knowledge-based systems and their principal components: data management, analytical models and user interfaces. Technologies such as IoT, artificial intelligence, machine learning and cloud computing support applications in feed optimization, disease prediction, reproductive management, milk production and animal-welfare monitoring. Dairy and poultry examples demonstrate how DSS can reduce operational costs and strengthen farm decisions. Implementation challenges include inadequate data quality, high costs, limited technical expertise and maintenance requirements. Future systems are expected to integrate blockchain, robotics, Edge AI and increasingly autonomous management technologies.

Computational crop-disease detection uses machine learning, neural networks and image-processing techniques to provide timely and accurate diagnoses. This chapter presents a complete framework covering agricultural intelligence, disease categories, image acquisition, preprocessing, model training and classification. It examines datasets collected in fields or through drones and considers deployment through mobile devices, cloud platforms and other digital systems. Evaluation measures are introduced for assessing model accuracy and reliability. The chapter also considers limitations caused by insufficient or poor-quality data and variation in field conditions. Future directions include multimodal data integration and explainable models that provide transparent recommendations for sustainable crop protection and improved food security.

Abstract

Describes the genesis, organization and Internet representation of International Patent Classification (IPC). Provides the outline of seventh edition and useful URLs related to it.

Keywords: International Patent Classification, Patent Classification, Classification, Internet representation

Introduction

The International Patent Classification, commonly known as the IPC, is based on an international multi-lateral treaty administered by World Intellectual Property Organization (WIPO). This treaty is called the Strasbourg Agreement concerning the IPC, which was concluded in 1971 and came into force on October 7, 1975. The Agreement aimed to overcome the difficulties caused by using diverse national patent classification system. The common classification system defined by the Agreement is applied to patent and utility model documents. The Agreement is open to States party to the Paris Convention for the Protection of Industrial Property.

The integration of Internet of Things technologies with artificial intelligence is creating increasingly connected and data-driven agricultural systems. This chapter examines how IoT and AI can support smart farming through continuous data collection, analysis and decision-making. It discusses technological infrastructure for advanced agriculture and improvements in field operations enabled by connected sensors and intelligent systems. Applications in precision farming, including orchard and vineyard management, are considered alongside emerging technologies and high-throughput phenotyping. IoT devices can provide information about environmental and crop conditions, while AI methods transform these data into useful management insights. The chapter concludes by examining future directions and research challenges associated with integrated smart farming systems.

The Internet of Things supports continuous monitoring of soil, crops, weather and other field conditions through distributed sensors and connected devices. This chapter proposes an edge-cloud architecture designed to address scalability, heterogeneous data, communication latency and energy requirements. Edge nodes conduct local processing and anomaly detection, while cloud platforms provide storage and run advanced machine-learning models. Applications include crop-status evaluation, irrigation scheduling, pest and disease detection and yield forecasting. Explainable artificial intelligence improves transparency by helping users understand model recommendations. The integrated architecture can reduce response time compared with conventional centralized systems and provides a foundation for scalable, efficient and responsive next-generation smart-farming operations.

Internet of Things (IoT) is networking of physical objects, devices, machines, systems etc to interconnect each other for exchanging data through standard internet protocols. The IoT is also termed as Machine-to-Machine (M-to-M) communication. The term ‘Things’ in IoT includes objects embedded with sensors, actuators, mobile phones, routers, hubs, computers, wearable’s and smart devices etc.

11.1 Concept of Internet

The internet is not a computer network. It is a network of networks, linking computer to computers. Each runs software to provide access and view information.

In other words, internet is the connection of two or more networks so that a computer on one network can communicate with a computer on another network. The communication between two computers takes place such that the user does not have to worry about the technology used by the networks. The router, also called gateway, acts as an interface between two networks. The internet covers the entire globe and includes large, international networks as well as many smaller, local area networks (LANs). A user can access data, graphics, sound, software, text and people through a variety of services and tools for communication and data exchange. To get, provide and compile information, internet is the cheapest and fastest medium. Some of the popular tools and services available for communication and data exchange are as follows: