eChapters

3.1 Introduction

Internet of Things (IoT) allows computational devices and sensory support to connect with each other and access services on the Internet. The IoT idea was introduced to connect devices through the Internet and facilitate access to information for users. The potential application of IoT includes agriculture. The aim of this chapter is to explain the role of NodeMCU in the agriculture field. NodeMCU is IoT module which can be used to present the IoT concept as a basis for monitoring and control. The systems used in farm production processes. NodeMCU play a key role, with a focus on their realization by available microcontroller platforms and appropriate sensors. IoT based system provides opportunity to users to monitor and control the process remotely.

Abstract

Systems biology is an interdisciplinary field that combines computer and mathematical analysis with biological research to understand the complex interactions within biological systems. This chapter explores the history and evolution of systems biology and its significant impact on modern biology. Systems biology uses a holistic approach to analyze biological systems at the molecular, cellular, and organismal levels, providing a better understanding of biological processes and the interactions between genes, proteins, and other molecules. Systems biology has immense potential in improving human health, agriculture, and the environment, and this chapter explores the ongoing efforts to translate systems biology research into practical applications.

Domestic livestock plays a major role in meeting the food requirement of human beings. Especially with dwindling resources, improvement in production efficiency is primary to meet the ever-increasing demand for animal protein. The role of an animal breeder is to bring about a sustained improvement in the quality and quantity of such products. Over the years, there has been an enormous improvement in the production ability of livestock and statistical analysis has played a predominant role in the application of principles of animal breeding to improve the productivity of animals. The combination of genetics and statistics developed into a separate branch of science called biometry.

The Madrid System for the International Registration of Marks is governed by two treaties. The first one is he Madrid Agreement, which was concluded in 1891 and revised at Brussels (1900), Washington (1911), The Hague (1925), London (1934), Nice (1957) and Stockholm (1967), and amended in 1979. The other one is the Protocol related to this agreement which was concluded in 1989. The aims of this protocol are to make the Madrid system more flexible and more compatible with the domestic legislation of certain countries or intergovernmental organizations that had not been able to accede to the Agreement. 

It is the most significant international convention in the field of protecting copyright. The Berne Convention deals with the protection of works and the rights of their authors. It is based on three basic principles and contains a series of provisions determining the minimum protection to be granted, as well as special provisions available to developing countries that want to make use of them. The Berne Convention, signed on 9 September, 1886, was revised at Paris in 1896 and at Berlin in 1908, completed at Berne (Switzerland) in 1914, revised at Rome in 1928, at Brussels in 1948, at Stockholm in 1967 and at Paris in 1971, and was amended in 1979.

R packages include functions, codes and sample data. The functions used in R are contained in packages. A package in R contains several functions which are related to some particular type of job. A package conatins at least one function and there is no maximum limit to the no. of functions in a package. The set of packages in R which are set by default in the software is called base package. It contains a lot of commonly used functions. The packages in R are stored under a directory called “library” in the R environment. The packages get installed by default while installing R in the computer. Also more packages can be added later on after installation, when required for some specific purposes. The base packages are available by default when we start using R. But the other packages which are already installed while installing R have to be loaded while using R. These packages can be viewed in the package window by clicking on package. To install the package we have to click on install menu in the package window and then write the name of the package.

The packages can also be installed by using the function install.packages(“abc”) where abc is the name of the package to be installed.

Once a package is installed, it remains in the hard disk of the PC/laptop. But the package will have to be called for use in the current R session. The package once installed can be loaded for use in the current R session by checking the box near the name of the package displayed after clicking on the install menu in the Files/ Plots/Packages/Help window. The package can also be loaded by using library() function. The command will be library(“abc”) where abc is the name of the package. The loaded package remains in the memory for the current R session. Once we quit the R session, the installed package has to loaded again if needed.

In R there is facility of reshaping the data. Reshaping of data includes joining of two or more vectors to create a data frame. Also two or more data frames can be merged. Appropriate functions are available for these jobs. While merging the data frames, we can join columns or rows.

Illustration of joining two vectors by using cbind() function.

The most important use of R programming lies in importing of data files which serves as input for the analysis work. After the analysis is over by using R codes, operators and functions, the output result must also be exported in form of files. The files are imported in form of csv files (comma separated variable), text files or excel files.

Before going to the actual content of the chapter we have to have an idea about csv files. CSV stands for Comma Separated value. It is used to store tabular data, such as a spreadsheet or database. CSV is delimited text file in which the values are separated by commas. CSV files occupy much less space than excel files and also gets imported in a program at a much faster rate than Excel files. CSV Files also does not manipulate data and stores as it is.

Let us consider that an excel file named as ‘plant’ which stores three variables, named as ph, pd and yield is created inside the folder named as ‘My Data’ which is placed in the desktop. Thus the path of the file is “C:\\Desktop\\My Data\\plant”

To import excel file the package will be readxl and the command will be library(readxl)

Need of Analytics in Fisheries Management

New technologies are introduced for industrial vessels every day: Fuel and gear sensors, cameras, electronic catch reports etc. There is a global push to track small-scale fishing vessel. In the coming years, fisheries monitoring centers will have to adjust from tracking a few hundred vessels to tens of thousands of vessels, meaning huge quantity of data. To make sense of all this information, it must be properly analysed. The future of fisheries depends on big-data analytics

This may help in many ways. Fishing trips could be tailored to meet quotas in the shortest time reducing fuel consumption and crew costs. Real-time catch data could help administrations close a fishing zone once quota is reached. Fisheries administrations can monitor catch efforts over an entire region and decide which zones to close, improving management of stocks and fishing licenses. Fighting the challenge of Illegal and Unreported fishing contributing to over-exploitation of fish stocks. Use of disruptive technologies in fisheries and aquaculture like block chains, sensors and automatic identification systems (AIS) has the potential to change the processes profitability and sustainability of the sector

Globally used popular Data Science Software

• MS-EXCEL

• SPSS (IBM)

• STATA • MINITAB

• SAS

• R

• PYTHON

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.

Experimental Designs

Experimental Designs are used widely to compare more than 2 groups, commonly called as treatments. It is very common in agriculture to conduct several experiments, mostly in fields to test various varieties, different levels of inputs, etc. When one is interested in crop modelling, it is necessary to identify the values of several constants, such as input efficiency, growth periods, growth rates, etc. Instead of guessing these values, it would be more appropriate to conduct experiments to derive these values empirically, which can improve the modelling efforts. The principles of experimentation were given by Sir R.A. Fisher during 1920’s, which are still relevant and found in every scientific research namely, (i) Randomization, (ii) Replication and (iii) Local Control. The following sections introduce some of the commonly used terms in Experimental Designs:

Factor: Variables which are set/considered by the experimenters in any experiment. Examples of factors are: Nitrogen, Varieties etc. Level: The various values of a particular factor considered in the experiment. Examples of levels are: Rates of nitrogen applied, different varieties considered in the experiments.

Treatment: The levels of one or more variables (Factors) set/considered by the experimenters.

Note that in many experiments, the number of variables (i.e. factors) considered may be one only.

There has been a surge of data in every field of research due to advancements in information technology. Data collection and storage has become much easier and cheaper in recent years. This voluminous data needs to be systematically stored for quick retrieval, process and to find useful patterns. Data mining techniques help in deriving useful patterns from data. These techniques which are also called as machine learning techniques majorly categorized as classification, clustering and association rule mining methods. Classification is a supervised learning method which needs labeled dataset for model building and prediction purposes. Clustering and association rule mining methods do not require labeled dataset and hence they are referred as unsupervised learning methods.

Clustering aims at grouping the objects based on a set of measured variables where similar objects are assigned to one group. For example, let us say, if we have collected information on 50 fish pond parameters (50 variables) and we wish to find the groups in terms of pond management (like well managed to poorly managed), with cluster analysis, we can group the ponds based on pond variables. In the process of clustering, each pond is assigned to one of the groups made. The question is how many natural clusters present in the data as in many cases we would not be getting perfect clusters of disjoint sets. We can achieve better accuracies in cluster formation only by trial and error. Good clustering criteria are to see that there is minimum with-in cluster variations and maximum between cluster variations.

Cluster analysis has several applications in fisheries research. It could be used to group fish stocks from different regions, categorise ponds based on their technical efficiencies, identify the farm clusters with high disease risk, group farmers based on their preferences, agro zoning based on climatic variables and culture practices. Cluster analysis is also applied in fish bioinformatics to group the similar sequences.

Introduction

Economists consider a producer as an income or profit maximiser. In our context, it may mean enhancement of fisheries contribution to the GDP, increased fish production, Nutritional security of those who need it most, not just those who can afford to pay for it improvement in the livelihood conditions of the stakeholders, augmenting exports to net more foreign exchange, growth with social equity or inclusive growth, etc. Realisation of a goal or target depends on development and enforcement of necessary policy strategies for which a reliable data base is inevitable. Analytical trends of the past data could help to project future trend which could indicate feasibility of the envisioned goal. Here, an outline of some of the Methodological issues in Fisheries data collection is provided.

The Resource Endowments

Fisheries is a sunrise sector in India, in terms of food and nutritional security, employment and income generation. India is the 3rd largest fish producer and the 2nd in aquaculture production, globally. It contributes about 1% of national GDP and 5 % of agriculture GDP. The exports of marine products account for about 5% of total Indian exports in 2018-19. The estimated fish production during 2017-18 was 12-59 million tones (mt). It was 10.14% increase over 11.43 million t in 2016-17. Inland fisheries grew at 14.05% while marine fisheries rose at 1.73% in 2017-18. Fisheries sub-sector provides livelihood support and gainful employment to 14 million people. Many more may be employed now as available data is yet to be updated.

10.1 Lambda Function

Lambda Function or anonymous function in python programming

A lambda function can take any number of arguments, but can only have one expression.

Syntax:

Lambda arguments: expression only single line

The expression is executed and the result is returned.

Example

x=lambda a:a+10

Print(x(5))

13.1 Statistical Functions

Statistical methods help in the understanding and analyzing the behavior of data. We will now learn a few statistical functions, which we can apply on Pandas objects.

Percent_change

Series, DatFrames and Panel, all have the function pct_change(). This function compares every element with its prior element and computes the change percentage.

Example

import pandas as pd

import numpy as np

s = pd.Series([1,2,3,4,5,4])

print s.pct_change()

df = pd.DataFrame(np.random.randn(5, 2))

print(df.pct_change())

Abstract

The two most important tasks for a patentee to undertake after being granted a patent, in any patent regime, would be to pay the renewal fees necessary to keep the patent alive, and provide proof of having worked the patent in the jurisdiction. This article seeks to highlight features of India’s legal requirements with regard to the working of patents on a commercial scale. The number of patents granted in a country is an indicator of the technological strength of the country. However, it should be noted that actual technological growth depends upon the working of patents. Working of a patent means the commercial exploitation of the invention that is embodied in the patent.

Introduction

In the perspective of the accelerating national growth rate of 8% recorded at the end of the 10th Plan to a projected growth rate of 10% by the end of the 11th Plan, yielding an average GDP growth rate of about 9% it was appearing that the nation is going to have a strong footing of development. However, it could not be fully achieved due to several reasons including the global recession and other factors related to international economic setback which may deter achieving a projected growth rate of 10% in the 12th Plan. In consideration of the international scenario of growing complicacy in economic growth more emphasis should be given on further efficient utilization of natural resources and intellectual properties of grass root innovators to improve per capita income leading to a promising national structure of economics ensuring wide spread benefits to the society. In this regard, true development of a nation like India can be primarily realized by recognition of wide spread enthusiasm of its innovative human resources living in village and rural areas. It can be made possible only through working with them by appreciating their struggle for survival using all round innovative measures coming out of their daily life experiences and traditional knowledge. Technopreneur Promotion Programme (TePP) is a unique scheme of Government of India for development of society especially by promoting the intellectual properties of grass- root level innovators coming out from the society with a source of inspiration arising out of their daily life struggle for existence. In spite of its limited publicity in remote areas an awareness of this programme has often been found to raise great hope to the villagers to do something for their society exploring the ideas generated from the experiences of life with the help of local resources. Such naturally grown innovation can be found undoubtedly novel and the idea can change their world of dreaming for a more prosperous life by bringing changes in socioeconomic pattern of trade and employment in rural areas with proper support from Government if India through the scheme like TePP.

ABSTRACT

This paper presents an outline on the patenting system prevailing in India. 

INTRODUCTION

Ideas and knowledge are increasingly important parts of trade. Most of the value of new medicines and other high technology products lies in the amount of invention, innovation, research, design and testing involved. Hence, protection of such values is paramount. This paper details how these values are protected in India.

ABSTRACT

This paper presents few measures to be taken up by academic institutions, professional societies, industrial bodies and governmental agencies for the propagation of information of IPR in terms of encouragement of publication of books on IPR, establishment of IPR cells in academic and professional institutions and making IPR education as part of curriculum in Universities and institutions of higher learning.

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