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Finger millet (Ragi/ Mandua) is the principal small millet grown in South Asia. It is believed to be native of India (De condole, 1886) but according to Vavilov (1926) it originated in Abyssinia.

Sorghum is probably originated in Africa. Because of the great diversity of types grown in East Central Africa, in and around Ethiopia and Sudan, it seems probable that it originated in that area (De Condole, 1884).

Maize is one of the world’s leading crops cultivated over an area of about 194 million hectares with a production of about 1147 million tonnes of grain (2020). Among the maize growing countries, USA has the largest area followed by China, Brazil, Argentina, Ukraine and India. In respect of production also USA stands first followed by China. Regarding average yield per hectare USA ranks first followed by China. The average maize yield in India is only 3065 kg per hectare which is much lower than most of the maize growing countries of the world.

System of Rice Intensification (SRI) was first developed in Madagascar during the 1980s SRI uses less inputs. It uses less seed, water, chemical fertilizers and pesticides but uses more organic manures. Rice grown with SRI technology has large root volume, profuse and strong tillers with big panicles, more and wellfilled spikelets with higher grain weight

The field is ploughed once and harrowed twice or thrice until the soil becomes well puddle. Beds are made slightly raised of 1 – 1.5 m in width and conveniently large in size and drainage channels in between the beds. The total area of nursery required to plant one hectare of land is 20 cents (800 m2 ). This method is suited where assured irrigation facilities are available. The beds should not be allowed to dry because they develop cracks and the seedlings will get spoiled. This method keeps down the weeds in the nursery. Seedlings are transplanted at 21-25 and 25-30 days age for SDV and MDV, respectively. Sparse sowing is recommended in case of hybrid rice. One kg of hybrid seed is sown in one cent area of nursery. This helps in good tillering and robust seedlings and makes it possible to transplant single seedling per hill. Seedlings are planted at 20-25 days.

Cereal grain contains 60 to 70% of starch and is excellent energy rich foods for humans. In almost every country and region, cereals provide the staple food. In the world as a whole, only 5% of starchy staple food comes from root crops (mainly cassava, potato, and yams, depending on climate), whereas the rest is from cereal. Cereals are an excellent source of fat-soluble vitamin E, which is an essential antioxidant. Whole cereal grains contain 20 to 30% of the daily requirements of the minerals such as selenium, calcium, zinc and copper.

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.

The word cereal was derived from Ceres, the name of the Roman goddess of harvest and agriculture. Cereals are grasses (members of the monocot family Poaceae, also known as Gramineae) cultivated for the edible components of their grain (botanically, a type of fruit called a caryopsis), composed of the endosperm, germ and bran. Cereal grains are grown in greater quantities and provide more food energy worldwide than any other type of crop and therefore they are called staple crops.

20.1 Unit Testing in Python Programming

The process of testing wheather a particular unit is working properly or not is called unit testing. A unit test checks small component in your application

Example

def testsum1():

assert sum([1,2,3])==6 def testsum2():

assert sum([1,1,1])==6 #assertion error

testsum1()

testsum2()

20.1.1 Step Involved in Unit Testing

(1) Import unittest from library.

(2) Create a class for test and inherit (unittest.TestCase).

(3) Write the test function in the class.

(4) Call unittest.main()

19.1 Python Turtle Graphics

Turtle is a special feathers of Python. Using Turtle, we can easily draw in a drawing board.

First we import the turtle module. Then create a window, next we create turtle object and using turtle method we can draw in the drawing board.

19.2 Some Turtle Method

Example

from turtle import *

t = Turtle() #object in python

wn=Screen()

wn.title(?my first graphic”)

wn.bgcolor(?yellow”) #color filling in window

t.shape(?turtle”) #turtle shape given

t.color(?red”, “green”)

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.

17.1 Modules

• A module is a file containing python definitions and statements.

• A python module is (.py file) containing variables, class definitions, statement and function related to a particular task. The major feature of having module is that its content can be reused in other programs, without having to rewrite or recreate them.

• They are executed only the first time the module name is encounter in an import statement.

Type of modules:

• User defined modules

• Built-in modules

Ex.-array, math, numpy, sys, etc.

16.1 OOP in python

Object-oriented programming (OOP) is a programming language model which organized object and data.

16.1.1 What is Encapsulation

If same variable is reapet in two different palce then which problem is solved using encapsulation it means one variable is making private otherwise program generate error.

16.1.2 Abstraction

Abstraction in Python is the process of hiding the real implementation of an application from the user and emphasizing only on usage of it. For example, consider you have bought a new electronic gadget. Along with the gadget, you get a user guide, instructing how to use the application, but this user guide has no info regarding the internal working of the gadget.

Another example is, when you use TV remote, you do not know how pressing a key in the remote changes the channel internally on the TV. You just know that pressing + volume key will increase the volume.

15.1 Data Visualization

Data visualization is the graphical representation of information and data. By using different visual elements like charts, graphs and maps. Data visualization tool provides easy way to see the understand the data and related trend/pattern.

1. There are multiple tools available for data visualization using which we can perform data visualization.

2. In python we can use a library called “matplot” for data visualization.

3. Python based plotting library “matplotlib” enables us to produce 2D as well as 3D graphics i.e. charts/graphs.

4. Pyplot interface of matplotlib is used for representing 2D graphs.

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.

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())

12.1 Introduction Basic Functionality

By now, we learnt about the three Pandas DataStructures and how to create them. We will majorly focus on the DataFrame objects because of its importance in the real time data processing and also discuss a few other DataStructures.

12.2 Series Basic Functionality

Let us now create a Series and see all the above tabulated attributes operation

Example

import pandas as pd

import numpy as np

#Create a series with 100 random numbers

s = pd.Series(np.random.randn(4))

print(s)

Its output is as follows:

0 0.967853

1 -0.148368

2 -1.395906

3 -1.758394 dtype: float64

11.1 Introduction

Pandas is an open-source Python Library providing high-performance data manipulation and analysis tool using its powerful data structures. The name Pandas is derived from the word Panel Data - an Econometrics from Multidimensional data.

In 2008, developer Wes McKinney started developing pandas when in need of high performance, flexible tool for analysis of data.

Prior to Pandas, Python was majorly used for data munging and preparation. It had very little contribution towards data analysis. Pandas solved this problem. Using Pandas, we can accomplish five typical steps in the processing and analysis of data, regardless of the origin of data - load, prepare, manipulate, model, and analyze.

Python with Pandas is used in a wide range of fields including academic and commercial domains including finance, economics, Statistics, analytics, etc.

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))

9.1 Introduction Numpy

Numpy: is apython package which stands for “Numerical python”.

This was created in 2005 by Travis Oliphant.

Using numpy we can perform the following functionalities:

1. Mathematical and logical calculation on array.

2. Fourier transforms and routines for shape manipulation.

3. It has built in functions for linear algebra random number generation.

4. Used for scientific calculations.

5. It is faster than python list.

6. It is fast because it is associated with c programming.

NumPy is often called as an alternate for MatLab (a programming platform designed specifically for engineers and scientists for data analysis, developing algorithms, create models and application etc.)

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)

7.1 Tuple Introduction

A tuple is a collection which is order and unchangeable. In python tuples are written with round brackets. Tuples in python are very similar to list which contains different types of elements with the following major differences.

It is declared as

tuple1=(1,2,3,“ram”,“shyam”)

Program implementation import timeit

listtime=timeit.timeit(stmt=“[1,2,3,4,5,6,7,8,9]”,number=1000000)

tupletime=timeit.timeit(stmt=“(1,2,3,4,5,6,7,8,9)”,number=1000000)

print(?List takes time:”, listtime)

print(?Tuple takes time:”, tupletime)

6.1 List Introduction

1. List is a collection of different values or different types of items.

2. Unlike array in c/c++/java a list is capable of storing different types of values under one roof.

3. The items in the list are separated with the comma (,) and enclosed with the square brackets [].

4. List provides us the facility to store multiple types of in a single unit.

Example:

a=[“ram”,1,“shyam”,12.5]

b=[1,2,3,4]

a[0]=ram

a=[“ram”,1,“shyam”,12.5]

print(a)

print(a[0])

ram

5.1 String Statement

String in python is stored as individual characters i.e. indexed wise. The index by default starts with 0.

So, here P will be represented by str[0] or str[-6], y will be represented by str[1] or str[-5]

Note: strings are immutable and hence you cannot change the individual letters of string using assignment operator.

Example:

name=“shyam”

Name[0]=“A”

Result error

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

3.1 Control statement in python

Python statements are executed one by one in the same order as they appear in the program. But there may be some requirement when we want to execute the statements based upon a condition. Even there may be some situation where we want to execute a single statement or multiple statement (block of statement) “n” times till the condition is true. So in order to control the flow of execution of the program python provides us control statements.

There are two types of control statements:

(a) Selection statement

(i) if

(b) Looping condition

(i) While (ii) for

2.1 Operators

Operators are those who perform some specific operation. Python has a wide variety of operators. The various python operators are categorized into:

1. Arithmetic operators

2. Comparison/relational operators

3. Assignment operators

4. Logical operators

5. Identity operators

6. Membership operators

7. Bitwise operator

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/.

With the invention of bitcoin in 2008, the world was exposed to a new technology paradigm called blockchain. Blockchain was conceptualized by an unknown person named as Satoshi Nakamoto in 2008. The identity of Satoshi Nakamoto is still not known. Nakamoto proposed the design of blockchain which uses digital signature and timestamps to enable secure and trusted online transaction without the need of any trusted regulatory or third party.

Big Data refers to extremely large, very fast, highly diverse, and complex data that cannot be managed with conventional data management systems, tools or processes. In the perfect world, Big Data may include all kind of data, which can deliver the right information, to right person, in the right quantity, at the right time, to help in making the right decisions.

The term ‘geo’ is a Greek word, it means the earth, and the term ‘spatial’ means parameter/feature measured, mapped or modeled with reference to distance or space. The geospatial technology refers to the technology that measure, model, analyze, map, or predict the features or phenomena that occur on earth and its surface.

The term robot is czech word, first time used by play write Karel Capek, in year 1920, applicable to the forced labour or serfs or slaves. The first commercial, digital and programmable robot, named ‘Unimate’ was built by George Devol in 1954.

One of the major goals in agricultural crop management is to achieve maximum yield with minimum investment while maintaining a good quality of the crop produce. Crop infestation due to insects, diseases, and weeds are some of the major factors responsible for loss in crop yield. Further, an efficient Precision Agriculture Crop Management System is one, which is capable of precisely detecting the crop yield indicators (or crop health parameters) at an early stage and can predict the yield at any point of time of the crop cycle.

Sending a message from one place to another place, which are physically separated from each other, is called Communication. The Communication Engineering or Communication Technology deals with the design and development of engineering systems to transmit information from its source, and at the same time, to receive the same information at a remote destination with reliability, accuracy and quality.

The human civilization and environmental eco-system are the two most important components of real world. The technology intervention to safeguard human civilization and the environment has always been the foremost priority of scientific and engineering community through their innovative expeditions.

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.

Artificial Intelligence (AI) is technology which can store, retrieve, manipulate, manufacture, transmit or receive, analyze any digital information and can apply machine vision for increasing efficiency and workability of the system. The AI permits systematically scope of ‘3S’ i.e. Safety, Security and Surveillance, which is also known as manmade intelligence. In fact, it needs more reliable and accurate data or information for processing and correction for predicting performance of any system and devices.

Sensor is a physical device used to detect manifestations or variations in a process value and collect data in real time assessment mode for measuring and predicting system/device performance and finally analyzing it to attain a specific task or application. Sensor technology is now-a-days used for interdisciplinary applications for data collection, system analysis and monitoring.

The term digital is derived from the word digit or digitus, a Latin word for finger, as fingers are often used for discrete counting. A variable is discrete when it is countable in a finite time and its instantaneous value at any timing/ instant belongs to a set of finite values.

The branch of statistics which describes the data is called descriptive statistics. After collection, the data are presented in form of tables, diagrams or graphs. This facilitates the further analysis of data. The most basic form of data analysis is descriptive analysis of data collected and presented for the study. The descriptive statistics summarises the data and helps to find the representative value of the entire data set and the variation of the other values in the data set around the representative value. The descriptive statistics includes the measures of central tendency or averages, measures of dispersion or scatteredness, measures of skewness and the measures of kurtosis. Before going to discuss the R codes for various measures of descriptive analysis, we would first discuss in brief about these measure. This will help in better understanding of the R codes.

Measures of central tendency or averages: They are the representative values of the data series. They give an idea about the concentration of the values in the central part of the distribution of the data. There are five types of averages. They are 1. Arithmetic Mean (A.M.) 2. Geometric mean (G.M.) 3. Harmonic Mean (H.M.) 4. Median 5. Mode.

Diagrams and graphs are the effective and attractive ways of presenting data. Widely used diagrams for data presentation are pi chart and bar diagrams. Bar diagrams are also of various types; (i) Simple bar diagram (ii) Component or sub-divided bar diagram (iii) Multiple bar diagram (iv) Percentage bar diagram. The widely used graphical presentation are histograms, frequency polygon and frequency curve.

There are numerous libraries to create charts and graphs in R programming language.

Pie-chart- A pie-chart is a representation of values of variable as sectors of a circle with different colours/shadings. The colours/shadings used for different sectors are indexed at the side of the graph. pie() function is used to create pie chrt in R.

Let us create an exel file names as ‘exp_sports’ which contains the expenditure made (in Rs.) under various sports in a sports club. The name of the sports are cricket, hockey, tennis and football. The names of sports are placed under the column head ‘sports’. The corresponding expenditure (in Rs.) are 7200, 3600, 1800, 1800 and they are placed under the column head ‘expenditure’.

The basic syntax for creating a pie-chart using the R is.

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.

Matrix is a rectangular array of numbers arranged in rows and columns. The order of a matrix is written as r x c, where r is the no. of rows and c is the no. of columns. The numbers included in a matrix are called elements of the matrix. The name of the matrix is denoted by an uppercase English alphabet.

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)

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.

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.

This chapter deals with the objects used in R which contains the data in various formats. The data types used in R has also been mentioned in this chapter. The chapter also includes the functions used in R as the codes for accomplishing several jobs related to data analysis.

Unlike C language and Java, the variables in R are not declared as some data type. The variables are assigned with R object and the data type of R object becomes the data type of the variable. The commonly used R objects in R are vectors, lists, matrices, arrays, factors and data frames. The vector object is the simplest one and other R-objects are built upon the vectors. The different data types of these vectors are logical, numeric, integer, complex, character and raw. The objects in R are stored by the name of a variable and the data type of the object is determined by its content. To know the data type of a variable the class() function is used. The various data types are described as below:

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.

A simple random sample (SRS) of size n consists of n individuals from the population chosen in such a way that every set of n individuals has an equal chance to be the sample actually selected.

A simple random sample is a subset of a statistical population in which each member of the subset has an equal probability of being chosen. An example of a simple random sample would be the names of 25 employees being chosen out of a hat from a company of 250 employees. In this case, the population is all 250 employees, and the sample is random because each employee has an equal chance of being chosen.

The two-way ANOVA compares the mean differences between groups that have been split on two independent variables (called factors). The primary purpose of a two-way ANOVA is to understand if there is an interaction be- tween the two independent variables on the dependent variable. For example, you could use a two-way ANOVA to understand whether there is an interac- tion between gender and educational level on test anxiety amongst university students, where gender (males/females) and education level (undergraduate/ postgraduate) are your independent variables, and test anxiety is your depen- dent variable. Alternately, you may want to determine whether there is an in- teraction between physical activity level and gender on blood cholesterol con- centration in children, where physical activity (low/moderate/high) and gender (male/female) are your independent variables, and cholesterol concentration is your dependent variable.

The interaction term in a two-way ANOVA informs you whether the effect of one of your independent variables on the dependent variable is the same for all values of your other independent variable (and vice versa). For example, is the effect of gender (male/female) on test anxiety influenced by educational level (undergraduate/postgraduate)? Additionally, if a statistically significant interaction is found, you need to determine whether there are any “simple main effects”, and if there are, what these effects are (we discuss this later in our guide).