eChapters

ANALYSIS OF VARIANCE

Analysis of Variance (ANOVA) is a hypothesis-testing technique used to test the equality of two or more population (or treatment) means by examining the variances of samples that are taken. ANOVA allows one to determine whether the differences between the samples are simply due to random error (sampling errors) or whether there are systematic treatment effects that cause the mean in one group to differ from the mean in another.

Most of the time ANOVA is used to compare the equality of three or more means, however when the means from two samples are compared using ANOVA it is equivalent to using a t-test to compare the means of independent samples.

ANOVA is based on comparing the variance (or variation) between the data samples to variation within each particular sample. If the between variation is much larger than the within variation, the means of different samples will not be equal. If the between and within variations are approximately the same size, then there will be no significant difference between sample means.

The chi square is a continuous distribution based on the assumption that the ul- timate class frequency is not less than 5 otherwise the value of χ₂ will be over- estimated and may cause too many rejections of null hypothesis. Therefore, to maintain the continuity of χ₂ and to draw the correct inference in 2×2 we apply a correction which is known as Yates’s correction for continuity.

CHI- SQUARE TEST (χ₂ TEST)

The chi square test is used to determine if the two attributes are independent of each other. Chi square is a measure to evaluate the difference between observed frequencies and expected frequencies to examine whether the difference so obtained is due to a chance factor or due to sampling error.

In a contingency table if each attribute is divided into two classes it is known as 2×2 contingency table. When one attribute is divided into two classes and another into r or c resultant contingency table is known as r×2 or 2×c contingency table. Here, r denotes the number of rows and c the number of columns. For such data, the statistical hypothesis under test is that the two attribute are independent of one another. To test this hypothesis we use the test statistic

STANDARD NORMAL DISTRIBUTION

If μ = 0 and σ = 1, the distribution is called the standard normal distribution or the unit normal distribution, and a random variable with that distribution is a standard normal deviate. The approximate test is given by

F TEST (VARIANCE RATIO TEST)

F distribution is applied in several tests of significance relating to the equality of two sampling variances drawn on the basis of independent samples from a normal population.

t TEST FOR PAIRED OBSERVATION

This test is used for testing whether two series of paired observations are gen- erated from the same population on the basis of the difference in their sample means.

t TEST FOR TWO SAMPLE MEAN:

Comparison of two sample means  and assumed to have been obtained on the basis of random samples of sizes n₁ and n₂ from the same population which is assumed to be normal.

t TEST FOR SINGLE MEAN

A t-test is any statistical hypothesis test in which the test statistic follows a Student’s t distribution if the null hypothesis is supported. It can be used to determine if two sets of data are significantly different from each other, and is most commonly applied when the test statistic would follow a normal distribution if the value of a scaling term in the test statistic were known. When the scaling term is unknown and is replaced by an estimate based on the data, the test statistic (under certain conditions) follows a Student’s t distribution.

REGRESSION

Regression means to revert or return back. The term was first introduced by Sir Francis Galton in1877. Regression technique is applicable in all those fields where two or more relative variables have the tendency to go back to the mean. Regression analysis refers to the methods by which estimates are made of the values of a variable from the knowledge of the values of one or more other variables and to the measurement of the errors involved in this estimation pro- cess.

In statistical modeling, regression analysis is a statistical process for estimat- ing the relationships among variables. It includes many techniques for mod- eling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. Regres- sion analysis helps one understand how the typical value of the dependent variable (or ‘criterion variable’) changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables – that is, the average value of the dependent variable when the independent variables are fixed.

TEST OF SIGNIFICANCE FOR CORRELATION COEFFICIENT

Coefficient of correlation is a numeric characteristic of a population and is estimated from a sample of the bi-variate population. If ρ be the coefficient of correlation in the population and r be its sample estimate based on n pairs of observations, then standard error of r is given

CORRELATION

In case of bivariate or multivariate normal distributions, we may be interested in discovering and measuring the magnitude and direction of the relationship between two or more variables. For this purpose we use correlation. The sys- tematic interrelationship between the variables is termed as correlation. When only two variables are involved the correlation is known as simple correlation. If more than two variables are involved the correlation is known as multiple correlation. An increase in one variable may cause an increase in the other variable, or a decrease in one variable may cause a decrease in the other vari- able, then they are said to be positively correlated. If a decrease in one variable causes an increase in the other variable or vise versa, the variables are said to be negatively correlated.

KURTOSIS

Kurtosis refers to the extent to which unimodal frequency curve is peaked. Kurtosis is a measure that refers to the peakedness of the top of the curve. Kurtosis gives the degree of flatness in the region about the mode of a frequency distribution.

Types of Kurtosis

There are three categories of kurtosis that can be displayed by a set of data. All measures of kurtosis are compared against a standard normal distribution, or bell curve.

The first category of kurtosis is a mesokurtic distribution. This type of kurtosis is the most similar to a standard normal distribution in that it also resembles a bell curve. However, a graph that is mesokurtic has fatter tails than a standard normal distribution and has a slightly lower peak. This type of kurtosis is considered normally distributed but is not a standard normal distribution.

The second category is a leptokurtic distribution. Any distribution that is leptokurtic displays greater kurtosis than a mesokurtic distribution. Characteristic of this type of distribution is one with extremely thick tails and a very thin and tall peak. The prefix of “lepto-” means “skinny,” making the shape of a leptokurtic distribution easier to remember. T-distributions are leptokurtic.

The final type of distribution is a platykurtic distribution. These types of distributions have slender tails and a peak that’s smaller than a mesokurtic distribution. The prefix of “platy-” means “broad,” and it is meant to describe a short and broad-looking peak. Uniform distributions are platykurtic.

SKEWNESS

Skewness means lack of symmetry. Skewness denotes the tendency of a distri- bution to depart from symmetry. A frequency distribution is said to be skewed if the frequencies decrease with markedly greater rapidity on one side of the central maximum than on the other side. This characteristic of a frequency distribution is known as skewness and the measures of asymmetry are usually called measures of skewness.

The main object of measuring skewness is to know the direction of the varia- tion from an average and to compare the frequency distribution and the shape of their curves.

There are two types of skewness

(i) Positive Skewness (ii) Negative Skewness

MOMENTS

The term moment in mechanics refers to the measure of a force with reference to its tendency to produce rotation. The strength of this tendency is depen- dent upon the amount of the force and the distance from the origin at which the force is applied. If on both the sides of the origin the force are equal than there will be a balance, at a balanced position the positive product equals the negative product.

Moment is used in Statistics in a quite analogous sense. The class frequencies are looked upon as the forces and the deviations of the different values from the mean are taken as the distances. In other words, moment is the mean of the first, second, third, fourth, etc. powers of deviations of the different values from the arithmetic mean. The first moment about the mean is always zero and second moment about the mean is variance.

If the moment calculated from arithmetic mean are called central moments and denoted by μ with a subscript which indicate the order of the moment. Moments are the mean values of powers of the deviations in any frequency distribution taken about three points

FOR GROUPED DATA

Objective: Data regarding lives of two new models of refrigerators collected in a recent survey are

COEFFICIENT OF VARIATION:

The most important of all the relative measure of dispersion is the coefficient of variation. This word is variation not variance. There is no such thing as coefficient of variance. The coefficient of variation( C.V.) is defined

FOR GROUPED DATA

Objective: The following data shows the marks of students and their frequencies find the standard deviation and its coefficient.

STANDARD DEVIATION AND THEIR COEFFICIENT

The standard deviation is a measure of the spread of scores within a set of data. Usually, we are interested in the standard deviation of a population. However, as we are often presented with data from a sample only, we can estimate the population standard deviation from a sample standard deviation. These two standard deviations - sample and population standard deviations - are calculat- ed differently. In statistics, we are usually presented with having to calculate sample standard deviations.

COEFFICIENT OF THE MEAN DEVIATION

A relative measure of dispersion based on the mean deviation is called the coefficient of the mean deviation or the coefficient of dispersion. It is defined as the ratio of the mean deviation to the average used in the calculation of the mean deviation.

MEAN DEVIATION

The mean deviation is the first measure of dispersion that we will use that ac- tually uses each data value in its computation. It is the mean of the distances between each value and the mean. It gives us an idea of how spread out from the center the set of values is.

The mean deviation is based on all the observations, a property which is not possessed by the range and the quartile deviation. The formula of the mean de- viation gives a mathematical impression that is a better way of measuring the variation in the data. Any suitable average among the mean, median or mode can be used in its calculation but the value of the mean deviation is minimum if the deviations are taken from the median.

A relative measure of dispersion based on the quartile deviation is called the coefficient of quartile deviation. It is defined as

Paratition Value

Median is calculated for dividing the series into two equal parts. If the series is divided into four, eight, ten or hundred parts then the measures are called Quartile, Octile, Decile and Percentile respectively denoted by Q_(1,2,3), O_(1,2..7), D_(1,2…9) and P_(1,2…99).

Objective: The distribution of age of males at the time of marriage was as follows

Measures of Central Tendency

The term ‘Mean” or ‘average’ is something, we have been familiar with from a very early age when we start analyzing our marks on report card. We add together all of our test results and then divide it by the sum of the total number of marks there are. We often call it the average. However, statistically it’s the Mean!

The Median is the ‘middle value’. When the totals of the list are odd, the me- dian is the middle entry in the list after sorting the list into increasing order. When the totals of the list are even, the median is equal to the sum of the two middle (after sorting the list into increasing order) numbers divided by two. Thus, remember to line up values, the middle number is the median!

The Mode in a list of numbers refers to the list of numbers that occur most frequently. A trick to remember this one is to remember that mode starts with the same first two letters that most does. Most frequently - Mode.

Pie charts are useful to compare different parts of a whole amount. They are often used to present financial information. E.g. A farmer’s expenditure on farm can be shown to be the sum of its parts including different expense cate- gories such as cultivation, irrigation, sowing, harvesting, and general running costs (i.e. rent, electricity, fuel etc).

A pie chart is a circular chart in which the circle is divided into sectors. Each sector visually represents an item in a data set to match the amount of the item as a percentage or fraction of the total data set.

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

Frequency Distribution: A tabular presentation of the data in which the fre- quencies of values of a variable are given along with them is called a frequen- cy distribution. Two types of frequency distribution are available

1. Discrete Frequency Distribution or Ungrouped Frequency Distribution
2. Continuous Frequency Distribution or Grouped Frequency Distribution

Objective (a): Prepare a discrete frequency distribution from the following sentence in English: “Today there is hardly a phase of Endeavour which does not find statistical devices at least occasionally useful.”

Theory: This problem relates to discrete frequency distribution i. e. a frequency distribution which is formed by distinct values of a discrete or continuous variable.

12.1 Meaning and Definition

A flow chart is a graphical or symbolic representation of a process. Each step in the process is represented by a different symbol and contains a short description of the process step. The flow chart symbols are linked together with arrows showing the process flow direction. This diagrammatic representation can give a step-by-step solution to a given problem. Flowcharts are used in analyzing, designing, documenting or managing a process or program in various fields.

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:

MS-ACCESS is a very powerful RDBMS (Data Base Management System) which manages data in term of special table called relation. Using MS-ACCESS you can manage all your information from a single database file. Database files are stored with extension. MDB.

ACCESS stores information in the form of tables and you build your database on its basis. Each row in a table holds all information’s & for a particular person it is called a record. And each column contains different pieces of information regarding that person, like Roll No, Name, Class, Address etc. Same as MSACCESS involves.

In ACCESS, you have few things known as Forms, Reports and Queries. They allow you to check and get desired information as and when required.

Solution:

Steps used for plotting the bar graph are as:

Step 1: Open Excel sheet and feed the above data as given in figure. 9.1.

Step 2: Select data and go to insert menu and select column chart. (figure 9.2)

Step 3: Select 2-D column, option first from dialog Box (figure 9.3).

Step 4: The desired output will be as shown (figure 9.4).

Remark The data labels, axis name, title of the graph etc. can be inserted through selecting option formatting chart area and add data labels by right clicking on the graph output (figure 9.5).

Some basic and most common analysis tools used in research are summary statistics. These can be analyzed in Ms-Excel through the following ways.

8.1 Summary Statistics

The following data are the yield (q/ha) of 60 varieties of paddy maintained in a research station for breeding trails:

53 ,74, 89, 65, 88, 65, 45, 70, 25, 80, 75, 85, 47, 63, 78, 44, 42, 51, 59, 86, 76, 48, 30, 38, 67, 78, 74, 68, 58, 59, 36, 76, 89, 48, 67, 69, 35, 46, 89, 75, 79, 56, 42, 41, 60, 65, 68, 69, 61, 63, 95, 94, 85, 83, 76, 71, 68, 62, 64, 62.

Calculate the summary statistics for the study of the data.

Solution: The summary statistics of the data using MS – excel can be obtained / analyzed through following steps:

Step 1: Open Excel sheet and feed the data as given in Fig. 8.1.

Step 2: Go to data menu and select Data Analysis. Sometimes this menu is not available in the data, it can be added through add in → check in, analysis tool pack → go. It will appear as the way showing in Fig. 8.1.

7.1 The Formula Palette

By clicking on the equals sign in the formula bar a drop – down panel called the formula palette appeared.

Consider an example, type number 25 in cell A1 and move the cell pointer to cell B2. type or click the equal sign in the formulabar. Adrop – down panel will display formula result = and wait for further inputs. Enter sqrt result to 5 at B2.

The dialog box divides into parts as

(a) Name of the function at the top left of the argument entry box.

(b) Below of this there is the definition or description of the function.

(c) Next, there is an entry explanation.

(d) Formula result.

Things to remember: Any statistical or mathematical function can be activated with = (equal to) sign.

Now we have to learn how to enter numbers and text, how to edit any erroneous entry and then how to do simple calculations. Open empty excel spread sheet by clicking on the start menu in the window tool bars, click on Programs and then click on the Excel menu item.

6.1 Entering Numbers

Numeric entries are constant, can be integers, decimal fractions, integer fractions and scientific nations which can be entered by typing the number in the active cell [cell which can be made active by clicking the mouse on the cell.

5.1 Introduction

According to Croxton and Cowden, “Either for one’s own use or for the use of other, the data must be presented in some suitable form”.

Now – a – days everybody uses tables in one form or another, and accountants use them quite often. The work become tedious, especially if you had to do for 12 columns for the 12 months of the year, and more so if there were some modification to be done after having set up the entire table. The Americans have a special name for such tables- spreadsheet which makes life easy.

Steve Jobs and Steve Wozniak two young Americans in 1976 AD developed the first desktop computer – The Apple which was not user friendly and needed programming for useful work. Dan Bricklin, Robert Frankston and Dan Fylstra proposed a program to turn the computer’s memory into a giant virtual spreadsheet where necessary corrections and modifications could be done fairly, quickly and easily and marketed the program in 1978 under the trade name VisiCalc. VisiCalc was soon succeeded by Sugercal and other computer programs which were more user friendly. The Lotus 1-2-3 designed and developed by Larry Kaplan put further step, which set the standard for many applications under the IBM, Now a days Microsoft under the leadership of Bill Gates had been working on the sidelines perfecting graphical user Interface (GUI) packages for the PC range of computers that would widen its reach. Now Microsoft office program Suite the integrated word processing, spreadsheet, presentation and data base management.

The spreadsheet package in Microsoft office is called Excel – our basic software, or program for this manual.

There are manyreasons for using computerstoday. Word processing is area which has proved computers to be powerful than ever. There are many different kinds of word processors availabletoday, however, most share the same basic functions and do not require advanced computeror typing skills to use. These basicfunctions include typing, savingdocuments,openingdocuments, copying, pasting, cutting printing, checking spelling, etc. The commonly used word processors include Microsoft Word, ClarisWorks, Word Perfect, Word Pad, Apple Works, etc.

4.1 Microsoft Word

4.1.1 Introduction

Microsoft Word, or Word as it is commonly known, is a software application that allows the user to perform word processing. The word processing means the creation, input, editing, and production of documents and texts by means of computers systems. Microsoft Word allowthe user to format text with attributes such as bold, italics, font size, and margins. It is part of the Microsoft Office suite of applications and is a highly popular programme for the workplace.

Microsoft Word was first released in 1983 under the name Multi-Tool Word for Xenix systems. Versions were later writtenfor several other platformsincluding IBS PCs running DOS in 1983,the Apple Macintosh in 1984 and Microsoft Windows in 1989. The latest release is Word 2010.

Windows is an operating system with a Graphical User Interface (GUI). GUI displays graphical objects called icons, which represents the items you use. Also it is a menu driven interface, where you have menus and submenus to perform even the most basic tasks such as File open, Copy, and Help etc. The user need not know the computer commands, he just need to know simple English language to operate windows. This feature of windows made it a widely accepted operating system. Also windows allow multiprocessing environment, where we can perform more than one task simultaneously. For example, if you need to take a print out of a very long report which may take a couple of hours, you may give the command, and leave it at the background, and then you can keep working on the other applications. Windows supports a very useful pointing device called a mouse. With the mouse you can go anywhere on the screen and click where ever you want and pass instructions.

2.1 Definition and Concept

An operating system (abbreviated as ‘OS’) is the program that, manages all the other programs in a computer. Every general-purpose computer must have an operating system. It provides support for the loading and execution of other programs which are known as Application Programs.

1.1 Introduction

The word ‘computer’comes from the word ‘compute’, which means to calculate or perform a sequence of operations. Hence, a computer is normally considered to be a calculating device, which can perform arithmetic operations at enormous speed.

In other words, a computer takes information (or input), processes it according to a set of instructions (or process), and gives back a result (or output). In this respect, it is very similar to a calculator, but obviously somewhat more complex.

A computer is a multi-function platform. The same machine can perform many different tasks by using different programs. The programs (also termed as applications) are written to do everything imaginable. In other words, we can say that a program is a set of instructions issued to a computer in order to get the desired output.

Definition

A Computer can defined as an electronic device that takes input from its user, stores, processes data and generates the required output as per the processing instructions given to it by the user.

In this Chapter the precision farming/ Agriculture is discussed along with its steps of implementation. It also includes how artificial Intelligence plays role to perform precision farming/ agriculture in various way along with its scope, limitation and challenges.

Introduction

The primary source of livelihood is agriculture in India, where two thirds of India’s population, services and the private sector account for the remainder. Agricultural land accounts for about 43 percent of the geographical area of India. In earlier days, India was largely dependent in terms of food imports, but over the years, the country’s production of grain and seeds was self-sufficient, and this effort led to the formation of the Green Revolution. In fact, there was strong effort to achieve food self-sufficiency. This trend has continued up to now and continuous improvements have occurred. Precision agriculture or precision farming means the right thing to do, in the right way, on the right place and at the right time. Precision agriculture is expected to suit the agro-climate activities to improve application precision. Farming land has declined a bit in the last 40 years but the number of farmers has increased. According to the 2010–11 Agriculture Census, a total of 138.35 million operating holdings were estimated (single farmers) and 159,59 million hectares were covered by operations.

Precision agriculture is a management technique that collects, processes and analyzes temporary, spatial and individual data and integrates this with other information in order to support management decisions based on an estimated variability to enhance efficiency of use of agricultural production resources, productivity, quality, profitability and sustainability. Precision Agriculture manages every input of crop production (fertilizer, specifically found calestone, herbicide, seeds, insecticide, etc. Reduce waste, boost income and maintain efficiency Place. Precision Agriculture adapts soil and crop management carefully to suit the different field conditions.

Introduction

The conventional approach of the human person for plant classification is comparing the color and shape of the leaves, but by using Artificial Intelligence analysis can provide quicker and more detailed results by analyzing the morphology of the leaves and provides more details about the leaf properties. Species Recognition has been a very important part of agriculture and there are thousands of species in plants which can be identified by a single person and even if it does so It will take a huge lot of time to do it.

In this chapter we will discuss and built a project related to identify species in flower with an image. In this project we will be using Artificial Intelligence different branches like machine learning, deep learning and computer vision to build such an amazing project. So, let’s begin

Introduction

In agriculture, plant diseases have always been a major problem, as they cause crop quality to decrease, resulting in yield. Plant disease has a range of effects from minor symptoms to serious damages in whole crop areas, which lead to high financial costs and significantly impact the agricultural economy, particularly in developing countries dependent on a single crop or on a few crops.

Various approaches have been developed to detect disease in order to avoid significant losses. Causal agents have been precisely defined by methods developed in molecular biology and immunology. However, for many farmers these techniques are inaccessible and require detailed knowledge of the field or substantial time and energy to be used. According to the United Nations Food and Agriculture Organization, most farms in the world are small and family-owned in developed countries. Such families provide food for a significant proportion of the population of the planet. However, hunger and food insecurity are not rare and there is restricted access to markets and services. For the above reasons, a great deal of work has been undertaken in order to establish methods that are sufficiently reliable and available to most farmers. Where Plant disease is an essential factor contributing to a major decline in plant production quality and quantity. Plant disease identification and classification are a significant role in improving plant production and economic development.

In this chapter of machine learning algorithms we will cover the basic algorithms of machine learning with their implementation using dataset and discuss their pro’s and cons of algorithms for better understanding and how to use them in any project or with any dataset to get an correct result which can useful for research or study.

Key Component

We defined a data set composed of audio, images and binary labels that provided an understanding of how we could train a model from snippets to classifications. This sort of problem is one of many kinds of machine learning problems when we try and predict a specified unknown label given known inputs, provided that a dataset of examples for which labels are known is called supervised learning. Let’s discus what are the major key components we have to get there

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.

In this chapter we will talk about the tools which are required to make project in artificial intelligence. All this bellow discussed tools are uses python programming language for making any project in any respective field. We have discussed about how anyone can install and use the tools to built a real time project in artificial Intelligence.

Using Python IDE

To download and install Python visit the official website of Python https://www.python.org/downloads/and choose your version. We have chosen Python version as per requirement all the codes in this book were on python version 3.6 as you can see in the Fig. 7.1.

In this chapter we will look at the Expert system which can guide and assist an individual once it is provided with the enough Knowledge to do so. Here we will discuss about expert system and its architecture with proper steps of making an expert system in any field by several tools which are available to do so. For expert systems the most important is Knowledge Engineering which is the collection of Knowledge from an expert and Engineering it in such a form that a machine could understand it.

Introduction

One of the pioneering fields of AI study is Expert Systems (ES). It is proposed by researchers in the computer science department of Stanford University. An expert system is a computer system which emulates a human expert’s decision-making skills. Expert systems are developed by reasoning through information structures, representing laws, rather than through traditional procedural code, in order to overcome complex problems.

Expert Programs use professional expertise systematically to address human expert problems. An expert is an individual with experience in a specific field. The expert has expertise or special credentials which most people do not learn or care about. An expert can solve or solve problems more effectively than people can solve. Expert System actually emulates all these qualities of expert and solve the problem in a better way and fast.

Today, Expert System (ES) is used in the fields of finance, research, engineering, technology, medicine and many others where there is a well-defined problem area. The fundamental principle is that an expert method should also define the steps to illustrate why a problem can be solved. Unless an individual can justify his reasoning for Expert System.

Introduction

Agriculture has played an important role in the global economy in recent years. The further growth of the population is leading to a gradual decrease in the area of the agricultural land and further growing stresses on the farm system. Agricultural food production methods are rising in demand for successful and healthy. Innovative sensoring and motive technologies and advanced information and communications systems and Artificial Intelligence must supplement conventional agricultural management methods to speed up farm productivity growth more accurately, thereby promoting the production of high-quality and high-performance agriculture. Computer vision monitoring systems have become valuable tools in farm operations over the past decades, and their use has increased significantly.

What is Computer Vision?

Computer vision can be characterized as a field of AI for extracting information from digital images. The type of data obtained from an image may be different from recognition, navigational space measurements or applications of increased reality.

The applications of computer vision can also be described. Computer vision builds algorithms that recognize and use the quality of pictures for other purposes.

Deep learning emerged as a serious contender in the field from a 10-year explosive computational development. Deep learning is therefore a special form of machine learning, the algorithms of which are based on the structure and function of the human brain.

Machine learning VS Deep learning

Deep learning is arguably the most efficient form of machine learning. This is so important because they know the best way to solve the problem and how to fix everything. The following is a description of deep learning and machine Learning:

As we know there are different branches of artificial Intelligence and machine Learning of one of them which has become a most advance and widely used branch of artificial Intelligence in every field of this generation whether it may be either electronics, civil, mechanics, Bioengineering, medical or agriculture etc., In this chapter we will study about machine learning and its types with the most common algorithms of machine learning which are being mostly used in different scenarios and their implementation using python language has been discussed in the implementation part of this book. Here we will also see where machine learning can be used.

i. Introduction

Machine learning is a subset of Artificial Intelligence and one of the most important part of artificial Intelligence. It is a field of Computer science which is totally differs from traditional computational approaches. In traditional approaches, a set of instruction is explicitly provided to the algorithm to solve any problem. Whereas in Machine Learning is a domain of an AI which uses a statistical analysis and can enables a system to always learn from its data without any external inputs in order to give an essential output. Due to this machine learning makes computers in advance to make model which provide decision based on input data.

Machine Learning involves a lot of algorithms which usually based on their requirements and functionalities to use where and when. To make a machine learning model we always require the data on which model can be made. Machine learning uses the algorithm which continuously learns from the data available as training data. A machine learning model is generated after the training of algorithm from the data. After the training completes, when trained model is provided with other real-life data then you can get an essential outcome. For example, in figure 3.1 Machine learning Model we use a predictive algorithm and make a model by providing it with some data, and later on we can receive prediction’s based on real data as input.

As we have seen how artificial Intelligence can be useful in agriculture but the nest question always come in mind is that from where and how we can start to learn and what are the model of learning an artificial Intelligence. As Artificial Intelligence is a huge part of Computer Science which means we need an language as medium to perform it and we always prefer Python for that which is very dynamic and easy to learn. In this chapter we will learn about python basics so that in further implementation of artificial Intelligence it will be helpful to us.

I. Introduction

Python is a remarkable language that can claim to be easy and strong. You will be amazed to see how simple it is not to focus on the language you are programming, but on solving the problem. Guido van Rossum was the creator of the Python language which is named after the BBC show “Monty Python’s flying Circus”.

Python is formally introduced as:

“Python is an easy to learn, powerful programming language. It has efficient high-level data structures and a simple but effective approach to object-oriented programming. Python’s elegant syntax and dynamic typing, together with its interpreted nature, make it an ideal language for scripting and rapid application development in many areas on most platforms.”