eChapters

Milk yield is one of the most important economic traits and modelling the lactation curve is important with respect to the unique shape with ascending, persistent and descending phases. The non-linear relationship of daily milk yield with days in milk has led to extensive research in modelling the lactation curve. The nature of the lactation curve, different models, identifying the best model of fit, factors affecting the curve parameters and orthogonal polynomials to model the curve are discussed in this chapter.

R software is a programming language used for statistical analysis, graphical representation and reporting, and is currently developed by the R Development Core Team. R software is available freely under the GNU General Public License, and the binary versions are provided for the Windows, Linux and Mac operating systems. To develop R, effort from all over the world is made in a collaborative manner.

What is GNU?

GNU stands for GNU’s Not Unix. It is a Unix like computer operating system, but different from Unix. GNU consists of GNU packages which are program specific and released by GNU project. Free software means that the users of the software have freedom to run and use the software and also can copy, change, improve and distribute the software freely.

Development of R

R software was initially written by Robert Gentleman and Ross Ihaka also known as “R & R” of the statistics department of the University of Auckland, New Zealand. R appeared first in 1993. Since then various contribution to R has been made by a large group of talented individuals by sending code and bug reports. A core group, known as the “R Core Team” modify the R source code archive since 1997. Thus R is a collaborative project with many contributors.

Preparation of base map being in mind, the forest, agriculture, water, road and boundary are almost same for all but theme may be different as per requirement. Base information is to survey and need terrain information, the preparation includes land forms by separation of water, kart area, coastal area. Meanwhile preparation of focus might be on DEM (Digital Elevation Model) and water movement helps in demarcation of boundaries on map.

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:

Aerial photography is objective of getting panoramic view of earth surface to analyze the features of earth and their interrelation at particular time period which may be changed in further events. In this concerned, the observation may play a vital role in achieving the goals of photography. Undoubtedly it is widely used in social function and so on, but still awaited for scientific or research use. Drone can measure the geometry of ground surface at lower altitude with more accuracy in the form of visual feature in natural colour. flight height of drone mainly limits the large area coverage due to lower flying and drone can work perfectly at low altitude planning with good quality feature/photography.

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.

2.1    Specification Drafting
    
2.2    Methods and Precautions in Drafting Patent Specification
    
2.3    Disclosure Requirements
    
2.4    Patent Claims
    
2.5    Filing of A Patent Application

1.1 Introduction

Python is an interpreter, high level, general purpose programming language developed by Guido Van Rossum and its first version was released in 1991. There are two major Python versions: Python 2 and Python 3. Both are quite different.

1.2 Beginning with Python Programming

1.2.1 Finding an Interpreter

Before we start Python programming, we need to have an interpreter to interpret and run our programs.

Windows: There are many interpreters available freely to run Python scripts like IDLE (Integrated Development Environment) that comes bundled with the Python software downloaded from http://python.org/.

Linux: Python comes preinstalled with popular Linux distros such as Ubuntu and Fedora. To check which version of Python you’re running, type “python” in the terminal emulator. The interpreter should start and print the version number.

macOS: Generally, Python 2.7 comes bundled with macOS. You’ll have to manually install Python 3 from http://python.org/.

Introduction

Indian agriculture, including fisheries, is highly monsoon dependent and effect of climate change is immense on its large tracts of rainfed farms. The effects of climate change could be thwarted through better adaptation techniques using all information available with various agencies concerned with agriculture and using advanced analytical tools. Presently, in the country, there is a gap in the capability in capturing, processing and analyzing huge source of information generated in Indian agriculture. This task can be accomplished using Big Data Platform which works in cluster mode and provides the capability of handling huge volume of data, capturing streaming data or real time data and also can accommodate unstructured data. As per FAO, 70 percent in increase in food production in another 30 years is required to overcome the increasing global food demand. It may not be possible to achieve that target due to variety of problems such as reduction in land for cultivation, climate change, and various other factors, such as increasing the fish production etc. Improvement in crop genetics, application of fertilizer in higher dosages, irrigation facilities, etc. may not be possible in future. We need to create a big data platform involving important parameters on crop production viz., soil, soil temperature, water requirements, and relative humidity, pests and diseases, and micro and macro nutrients in temporal and spatial. Now, since available data is properly utilized for making valid analytics by the farming community due to certain issues. Even the data created is incompatible, data available from various sources is also not compatible among themselves due to which decision making is also a big challenge. Due to the incompatibility of agricultural data from different sources, farmers do not have the accessibility to vast pool of available information for making effective, efficient and timely decisions.

Big Data in Agriculture (including fisheries)

Big data, an emerging data driven system helps in precision agriculture based on data collected. This helps farmers in making efficient decisions on sowing, planting, fertilizing and various other farm operations including

harvesting crops at the appropriate time. Of late, sensors are placed in the fields to collect data on related parameters viz., soil temperature and moisture relative humidity, wind speed, weather, etc. which help in crop growth. Images obtained using satellites are helpful in monitoring the crop growth during crop period. Agro-produce includes not only yields obtained from crops but also include produce from horticulture, livestock, fisheries, and other systems also. Agriculture is dependent on various parameters such as climate, seed, soil cultivation practices, agronomic practices, irrigation requirement, fertilizers pesticides, weeds, harvesting, post harvesting techniques, etc. Data collected on all these parameters would help in estimation of yield and other relevant parameters. Governments, Universities, Research departments, Agro-business and Agri-input companies generate, maintain and use voluminous data related to various farm management practices and other related issues say insurance marketing, supply chain, packaging, distribution, etc. This voluminous and wide variety of data poses several challenges for making effective decisions. Information on various fish related parameters can be collected from the stakeholders and a big data platform can be developed for effective decision making.

4.1 Introduction to Function in Python

Python Functions is a block of related statements designed to perform a computational, logical, or evaluative task. The idea is to put some commonly or repeatedly done tasks together and make a function so that instead of writing the same code again and again for different inputs, we can do the function calls to reuse code contained in it over and over again. Functions can be both built-in or user-defined.

Function means sub-program

1. Function definition

2. Function call

Example:

def message: #function definition

print(?hello python”)

message() #function call

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.

14.1 Basic Plotting: plot

This functionality on Series and DataFrame is just a simple wrapper around the matplotlib libraries plot() method.

Example

import pandas as pd

import numpy as np

df = pd.DataFrame(np.random.randn(10,4),index=pd.date_range(‘1/1/2000’, periods=10), columns=list(‘ABCD’))

df.plot()

Its output is as follows:

If the index consists of dates, it calls gct().autofmt_xdate() to format the x-axis as shown in the above illustration.

18.1 Library Introduction

This library contains modules for various types of functionalities. Some commonly use module of python standard library are:

(i) math module: which provides mathematical function to support difference types of calaculations.

(ii) cmath modules: which provides mathematical function for complex number.

(iii) random module: which provides function generation random number.

(iv) staticstics module: which provides mathematical satical function.

(v) urllib module: which provides URL handling function so that you can access websites from within your program.

1. NmPy library, provides some advance math functionalities along with tools to create and manipulate numeric arrays.

2. Scipy library, provides algorithmic and mathematical tools for scientific calculations.

The integration of Large Language Models into software development has created powerful capabilities for automated code generation, debugging, testing and application development. However, AI-generated code can introduce security vulnerabilities when models produce incorrect, unsafe or poorly validated programming solutions. This chapter examines the need for security-aware frameworks that combine LLM-based code generation with verification, rule-based evaluation and risk management. It considers the limitations of conventional rule-based systems, including rule explosion, inflexibility, maintenance overhead and limited personalization, and explores how AI-driven approaches can improve adaptability while maintaining safeguards. Particular attention is given to secure integration in sensitive domains such as healthcare, where generated code and automated analytical systems may influence clinical information and decision-making. The chapter considers data-driven evaluation, machine learning, genomic information and risk detection as components of secure AI systems. It emphasizes the importance of validating generated code rather than treating model output as automatically reliable. Secure deployment requires testing, human oversight, access controls, verification mechanisms and continuous monitoring. The chapter therefore presents safe LLM integration as a multidisciplinary challenge combining AI capability, software engineering, cybersecurity and responsible governance.

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.

ABSTRACT

This paper provides an overall view on the history, structure and functions of World Intellectual Property Rights Organization.

Introduction

Human genius is the source of all works of art and invention. These works are the guarantees of a life worthy of men.  It is duty of the state to ensure with diligence the protection of the arts and inventions,” reads an inscription on the copula in the WIPO Headquarters building in Geneva. True to this inscription, WIPO functions through various mechanisms fostering judicious utilization of human intellect.  This paper intends presenting an overview on WIPO.

ABSTRACT

This paper attempts to portray prevailing scenario of IPR in the light of globalization with special emphasis on harmonization of IPR systems and laws of different countries. 

INTRODUCTION

The globalization of information, facilitated by the Internet, has significant implications for intellectual property regimes of national and international levels. Assessment of these implications and their probable outcomes is unavoidably value-driven. Globalization represents ever-increasing influence of developed nations and transnational entities over developing and less developed nations. The process has also brought in significant changes in understandings of sovergenity. An analysis of the implications of globalization of information on international and domestic intellectual property rights and legal regimes is a complex endeavor.

ABSTRACT

This paper is an attempt to analyze the plausible impacts on the economy and social issues of developing countries with reference to enforcement of IPR.

Introduction

From commodity based nature, business has changed during the past hundred years in a way that ideas and knowledge have taken higher value than commodities. Today, many companies find their most valuable assets being intangible and they are therefore dependent on functioning IPR systems. But does the rigid protection of IPR serve all the people in our globalized world? The purpose of this paper is to discuss the impact of the introduction of IPR protection on developing and under developed countries in the light of TRIPS Agreement.

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

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.