eChapters

Smart farming, otherwise called smart agriculture, uses progressed agriculture advancements to improve ranch the farm management. By consolidating Industry 4.0 advancements, Precision farming achieves doable outcomes like extend production, resource insurance, and lessened petroleum derivative result. Internet of Things (IoT) accepts a fundamental part in Smart farming, by Precision agriculture to screen and deal with things, as a matter of fact. Information examination is in like manner essential for isolating huge encounters from the colossal proportion of data created by IoT devices. The reconciliation of present day modern digital technology has change into smart farming as information empowered and information driven activity. After affiliation, further examination of each IoT and data analytics frameworks in the agricultural region can be positive.
The total population is predicted to increase by 10 billion in 2050, by enhancing interest for agricultural goods, and notable food. This has brought about a developing interest supply befuddle. Issues including restricted assets, natural changes, and work deficiencies increment the jumble, endangering food security and agricultural production. To defeat these worries, farming should be feasible and utilize state of the art innovation. One important choice is “smart farming,” that attempt to increased food productivity by meeting the population demand in a sustainable manner. This is steady with the Assembled Countries’ Sustainable Development Goals (SDGs), which hold back nothing security utilizing reasonable ways.
Smart farming strategy for agriculture business had changed Ancient farming into smart agriculture. Ancient times, human cultivated the soil to provide food for livelihood. Such activity is known as agriculture, and it follow a long path between agriculture 1.0 to agriculture 4.0. Producers employed agricultural implements including pitchforks and sickles for cultivation throughout the 17th century, a period called as Agriculture 1.0. Farming practices, a huge number of workers are needed to conduct agricultural tasks. Moreover, agricultural

GHG emissions as an Agriculture
Agriculture is significant source of GHG emission. Farming records for roughly 10-12 % of absolute anthropogenic GHG emissions, contributing 40 % of all out methane (CH4) and 60 % of nitrous oxide (N2O); agriculture is greatest wellspring of anthropogenic non-CO2 discharges. CH4 begins predominantly from metabolic action of methane-creating microscopic organisms in rice paddy soils and the gastrointestinal maturation of ruminants. N2O discharges get predominantly from farmland soil nitrification and de-nitrification under nitrogen manure application. Rural waste treatment, for example, fertilizing the soil and biogas creation, likewise straightforwardly brings about CH4 and N2O discharges. The change of land use from backwoods to cropland increments soil CO2 discharges. Notwithstanding immediate GHG discharges, farming exercises like apparatus tasks, and compost and feed inputs additionally by implication increment the carbon impression of farming by means of different pathways like petroleum product utilization.
Agriculture a Key Carbon Sinks
Farming moreover addresses a key carbon sink, in spite of the way that it is a huge wellspring of GHG. Horticultural perennials, for instance, field, backwoods, natural item estates and tea houses can get CO2 from the Environment through photosynthesis. It is evaluated that around 13 % of the CO2 emanated by oil based commodity use is consumed by natural vegetation reliably. Vegetation stores are changed into soil natural carbon (SOC) by the activities of microorganisms. In particular, microbial Nero mass can associate with soil minerals to shape a muddled that is a protected carbon pool and the soil carbon can subsequently be taken care of for a surprisingly long time; this carbon tirelessness instrument is known as a soil microbial-mineral carbon siphon. The soil carbon pool (2400 Gt) truly relies upon twice that set aside in the terrestrial vegetation and on different occasions that set aside in the air. The “4 for every 1000 soil carbon overhaul plan,” first proposed by the French service of agribusiness at the Collected Nations Environmental Change Gettogether held in 2015, relies upon the epic carbon-sink breaking point of soils.

Artificial brainpower (Artificial Intelligence) is quite possibly of the quickest developing innovation in this cutting edge world. Artificial intelligence is presently broadly utilized in different enterprises going from the improvement of mechanical technology to the observing of natural issues. Computer based intelligence innovation has evident extensions in medical services gadgets, self driving vehicles, satellite imaging, scene planning, Climate change checking, also medical services frameworks. Artificial intelligence has heightened the improvement of mechanical technology across the globe that has made the life very simpler and added to a greatly improved existence of prosperity. Artificial intelligence interceded robot of a few areas including farming will create great quality items with more positive effects on Climate. Artificial intelligence utilizes various Smart sensors that cycle information progressively with higher precision. Consequently, Artificial intelligence has upgraded the presentation of any gadget to a few folds with more prominent reasonableness and Precision in their results. Computer based intelligence with Smart sensors has lead to the improvement of a cutting edge innovation called as Internet of Things (IoT). IoT is center of remote frameworks and Artificial intelligence manages information progressively and creates suitable results. This innovation has been taken on in pretty much every area of science and examination. IoT based advanced advancements can possibly create inventive models for improved creation of buyer items including agro-items.
Agriculture is fundamental piece of people local area. It is overhauled with time to increment rural production. Present day advancements has additionally been utilized in farming, agriculture, also ranger service for better observing of plant development, sicknesses, and bug populace. Artificial intelligence and IoT have various expanding extensions in farming to settle different issues such as soil conditions, Climate status, bother discovery, assurance of collecting time, etc. Likewise computer based intelligence and IoT offers fantastic extensions for continuous observation of temperature, dampness, contamination, soil quality, water content, radiation, and others. Brilliant

Agriculture 4.0, otherwise called “Digital Agricultural Revolution”, addresses a change in outlook in agriculture, utilizing state of the art innovations to improve different parts of cultivating tasks. These innovations include the Internet of Things (IoT), Artificial brainpower (Artificial intelligence), Huge Information, distributed computing, Cloud computing, Decision Support System (DSS), high level detecting innovation, and independent robots. Sensors and mechanical technology assume a urgent part in gathering fundamental field information, which is then sent to a neighborhood or cloud server by means of IoT innovation for capacity, handling, and examination. Large information and Artificial intelligence-based strategies can be utilized to change over this information into significant experiences. To work with client connection and informed independent direction, a DSS outfits clients with the fundamental devices to improve the agricultural framework and embrace proper activities.
AI (ML), a subset of Artificial intelligence, has shown extraordinary potential in upgrading different parts of agriculture 4.0. It very well may be characterized as a PC program or framework that can learn explicit errands without being expressly modified to do so. An interaction includes the utilization of a PC to go with choices in light of various information inputs. For this situation, information means a bunch of models. Marked information is frequently utilized for managed learning undertakings (where the model gains from named models), and unlabeled information may be utilized for solo learning assignments (where the model tracks down examples and designs in the information).
ML for sure advantage from a lot of information to accomplish significant exactness in their errands. With regards to farming, acquiring immense and various information can be at times testing yet crucial for the outcome of ML models. IoT sensors are instrumental in gathering a different scope of farming information as they can be decisively sent across fields to catch pertinent data with respect to, for example, soil conditions, Climate factors, crop wellbeing, and animals’ measurements. The inescapable reception of IoT innovation works with constant and continuous information procurement, empowering

The stability of agriculture all over the planet and the accessibility of food are both signi?cantly affected by Climate change. Thus, it is desperately important to create and execute Climate -resilient farming techniques and advancements that can help farmers in acclimating to the e?ects of Climate change. It should be finished right away. It examines the issues welcomed on by Climate change, fundamentals of sustainable agriculture, and the instruments and strategies that can be utilized to expand farming’s protection from Climate change. This part essentially talks about Climate -friendly creature farming, viable water system frameworks, weather conditions checking and determining, crops that can endure dry spell and flooding, coordinated bug control, and sustainable power sources. To make sustainable more Climate change-safe, this part additionally talks about the need to empower feasible land-use rehearses and to make innovative work ventures. By using these innovation and methods, farmer might add to the maintainability and versatility of the food framework.
As a result of increasing temperatures and diminishing precipitation because of Climate change, farming is enduring enormously all over the planet. This is especially evident in India, where the greater part of the populace is utilized in farming. The effect of Climate change is perhaps of the main issue the world is at present encountering. The impacts of Climate change are felt and found in each part of society, including farming. Utilizing harmless to the ecosystem circular agriculture is critical now like never before as the total populace develops. One of these strategies is farming that is circular agriculture (CRA). Its central goal is to adjust cultivating practices to an evolving environment. Consistently, new people enter the earth. To take care of the growing populace, the land is feeling the squeeze to give more food, fiber, and fuel. Consistently, new people are brought into the world. Thus, the land is feeling the squeeze to create more food, fiber, and fuel to take care of the populace that is extending. As well as expanding crop efficiency, more Climate tough agricultural methods would likewise work on the soundness of the dirt and the biological system

In provincial areas of non-industrial nations, over 70% of the populace actually relies upon agriculture . Be that as it may, financial emergencies, informal land distribution and Climate change issues have ruined endeavored gains in agricultural efficiency and related rustic improvement results. Innovation pushed advancement has generally driven farming to the brink of another improvement that can influence plant variety and yield, yet additionally climatological and financial results. The idea of economical agriculture has become progressively advocated as examination and cultivating networks accept that efficiency with natural and social outcomes should be sensibly adjusted. Agriculture of created countries has basically benefitted of this idea overall during the 1990 s. To find success around the world, broad exploration should be led not just for enormous scope farmers in created countries yet additionally for limited scope ranches in agricultural countries. Climatic smart agriculture saves biodiversity by taking on sound, multifunctional, and multi-trimming approach through plan arranged spatial preparation and stream among them. Here, a spatiotemporal information driven worldview for detecting observing and coordinating agricultural Climate has been proposed to upgrade the utilization of Climatic smart agriculture framework. As an excursion towards maintainable coordinated cultivating, computerized farming offers different open doors for universal systems administration and computerized resources through information sway. The exploration issues are basically worried in the combination of different information archives with administration information, open access information, institutional information, and space explicit information from end clients. Because of the mix of heterogeneous sensors and advanced stages in agriculture, the information typology is getting more extravagant, and how much information is continually expanding.
Feedstocks assume a significant part in the comprehensive advancement of non-industrial nations that depend vigorously on agriculture. It has been standard that farmers must be actually present on their fields during each

Plant breeding is a revered custom that proceeds with the most common way of creating further developed plant cultivars, which traces all the way back to the beginning of agriculture . People started to observe levels of greatness among the plants in their fields not long after the underlying regulations of cereal grains, from which they put away the seeds of the best to develop new harvests. Early plant-reproducing advancements were the trailblazers to such simple choice methodologies. Individuals from the whole way across the world investigated and developed almost 7000 assortments of food plants during Breeding 1.0, which started quite a while back. Breeding stage 2.0 began in the late nineteenth and mid 20th hundreds of years when an inbreeding discouragement was found. During this time, many advances in plant breeding were made in the study of reproducing itself, like duplicated field preliminaries, controlled intersections, factual examinations, formal exploratory plans, crossover breeding, family-based appraisals of reproducing values, and exact yield estimation at scale (e.g., with Muldrow joins). Breeding 3.0 came to around a long time back, when sub-atomic markers and genomic information started to enhance phenotypic information. Breeding 4.0 is quickly drawing nearer, with immense omics information and the fast advancement of informatics innovations. Starting from the start of the plant breeding cycle, beginning from crop training, new methodologies interceded by various mechanical upheavals are being advanced in the science consistently to build the speed, exactness, and Precision in plant reproducing. In the previous 10 years, this science has proactively brought the green upheaval by creating semi-predominate, supplement responsive, and crossover cultivars. In any case, taking into account the populace development, diminishing arable land, and Climate changes, these have requested more exact, high throughput moves toward that can intervene to foster harvest cultivars at a more prominent speed, with higher exactness, and accuracy. In such manner, the field of Artificial brainpower has as of late arisen, which has been recommended to have unprecedented potential to help with breeding Climate versatile Precisionyields. Climate strong harvests keep up with or increment crop yields

Water system is considered an Arti?cial usage of the water on the dirt utilizing various strategies like siphons, cylinders and showers. Typically, the requirement for water system shows up where the precipitation is unpredictable, in dry times, or where lack of hydration is customary. An excessive number of water system frameworks are accessible with various sorts as indicated by the Climate of the dirt. The water utilized in water system has numerous assets, like underground water, through wells or springs; the surface water, from lakes, streams; or other a few sources, for instance, the treated wastewater or desalinated seawater. In this way, farmers need to save and safeguard their Agriculture water sources by limiting the capability of diseases. Since with any groundwater extraction, clients of water system water should be mindful so as not to deplete groundwater out with a rate more prominent than it is being recovered. There are two strategies of current water system systems, the conventional water system procedures and the Smart water system philosophies. Water system is like surface water system, trickle water system and sprinkler water system. Later on, a few extreme and confounded issues will be met by inundated agriculture. An illustration of a signi?cant issue is the low efficiency of the water assets for water system. An important safe guess is that over 40% of the diverted water system water is spent before at the level of the ranch, either through profound permeation or at the surface spillover. Nonetheless, frequently these misfortunes address botched open doors for water, as they keep water from showing up at the downstream redirections. One of the most noticeable coming issues is the expansion of various water prerequisites assets, for example, manufactural and metropolitan requirements. These are utilized to give water assets a higher worth, so as a result, researchers favor concentrating entirely on rehearses with a high inefficient rate. s worldwide populace is expanding quickly, rural efficiency in drylands needs improvement to fulfill food security needs. Subsequently, taking on Precision water system approaches is a suitable choice to more readily use the accessible water assets and further develop water efficiency in drylands. Water shortage has become one of the basic issues and undermines the supportable improvement

Agriculture addresses the essential food wellspring of the world, and it has been confronting serious difficulties because of the rising interest for food items, sanitation, and security worries as well as calls for ecological insurance, water conservation, and maintainability. This improvement is anticipated to go on since the total populace is assessed to reach 9.7 billion by 2050. Since farming comprises the most noticeable illustration of water utilization around the world, it is normal that food interest and water utilization will decisively increment within a reasonable timeframe. Moreover, the rising utilization of composts and pesticides combined with the increase of cultivating exercises could prompt future ecological difficulties. Essentially, arable land is restricted, and the quantity of farmers is diminishing around the world. These difficulties highlight the requirement for imaginative and practical cultivating arrangements.
Consolidating novel innovations has been distinguished as a promising answer for address these difficulties. Brilliant cultivating and Precision farming have arisen because of such discussions. The previous is an overall idea for information communication technologies (ICT) and other state of the art developments in cultivating exercises to build productivity and viability. The last option centers around site-explicit administration in which the land is separated into homogeneous parts, and each part gets the specific measure of agricultural contribution for crop yield enhancement through original advances. Unmistakable advances that certainly stand out in this field incorporate Wireless Sensor Networks (WSNs), the Internet of Things (IoT), artificial intelligence (AI) techniques, including machine learning and deep learning.
Notwithstanding the previously mentioned innovations, remote detecting has been viewed as a mechanical device with high potential to work on savvy and Precision farming. Satellites, human-maintained airplane, and robots are well known remote-detecting advances. Drones, popularly known as Unmanned

Precision agriculture includes information driven administration choices that further develop asset use productivity, bringing about decreased Agriculture expenses while bringing down the natural effects from agriculture. Consequently, information and information assortment frameworks, choice help devices, and information driven hardware and info changes are significant parts of Precision agriculture, participating in three vital farming advances: determination, direction, and performing separately. Before the coordination of shrewd innovations, ICT (data and correspondence innovation) was incorporated into farming devices and mechanical assembly to get certifiable data. Here, remote detecting, mechanized hardware and programming, telemetric, drones, autonomous vehicles, GPSs, and computerized developments were incorporated into cultivating rehearses. For example, the agro-tech association John Deere introduced GPSs for ranch farm haulers, anticipating extended yield and reduced input wastage. The previous status of accuracy agribusiness before brilliant cultivating can be summarized as follows.
Data Collection and Acquisition
Data, data collection, and decision support devices are critical for the distinctive verification and finish of alternate points of view in cultivating. In Accuracy agribusiness, data on individual fields and gathers are collected by seeing, assessing, and distinguishing with different sorts of sensors, yield and soil checking, and remote-detecting mechanical assemblies, such as imaging from drones, groups, airplane, or satellites. As such, “detecting” is a fundamental organization gadget of accuracy agribusiness, which is seeing distinct information and giving data on climatic circumstances, soil conditions, fertilizer necessities, water openness, nuisance and infection stresses, and other field limits. An extent of sensors is used in Accuracy farming. Biomass limits are huge in making decisions to screen the readiness and truly zeroing in on crops. Sensors for mass stream and dampness content are portions of yield screens, along with a differential global positioning system (DGPS) recipient. Appropriately aligned yield screens can produce precise constant

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/.

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

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

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

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

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

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)

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)

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.)

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))

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.

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

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())

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.

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.

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.

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.

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.

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”)

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()

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.

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.

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.

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.

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

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.

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).

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.

Pulses are seeds of leguminous plants used as food. They produce dal rich in protein. The important pulse crops are greengram, blackgram, redgram, bengal gram, soybean, cowpea, peas, lentil and lathyrus. The total area under pulses in India is 29.81 m ha with a production of 25.42 m t and the productivity is 853 kg / ha. Pulses can grow both in rainfed and irrigated conditions.

Mungbean is one of the most important pulse crops. It is grown in almost all parts of the country. Mungbean is primarily a crop of rainy season; however, with the development of early maturing varieties, it has proved to be an ideal crop for spring and summer seasons. Middle of March to last week of June is the most suitable growing period where there is a plenty of life-giving sunshine. During this period high temperature and low humidity keep insects and disease infestations at their lowest. Summer season offers an excellent opportunity for raising short duration pulses.

Oilseed crops are crops which are rich in fatty acid, cultivated for the production of vegetable oil. They are used either for edible or industrial or medicinal purposes. The important oilseeds are groundnut or peanut, sesame or gingelly, sunflower, rapeseed and mustard, soybean, castor, linseed or flax, niger and safflower.

The earlier view was that the cultivated sesame originated from Ethiopia. But Bedigian and Harlen (1984) established evidence to indicate that sesame is native of India. In vedic period, liberal mention about Til (Gingelly seed) and Taila (Gingelly oil) are mentioned. The Charred seeds of Gingelly were extracted from Harappa (2500-1500 B.C).

Cotton is often called as “white gold”. True to its name cotton supports the largest agro based industries in India and cotton industry ranks first in the agro based industries and engages 4 to 5 million people in cotton growing. The industrial sector employs 1.0 million people. At present there are 3660 ginning and 1570 textile mills in India. India earns foreign exchange to a tune of Rs. 2500 million through exports of cotton textiles and products. Cotton is cultivated for ‘lint’ which is used for preparing threads, yarns and medical cotton mixed with synthetic fibre and for various other purposes.

History of jute in India: In Mahabharata times the mention has been made regarding Jute garments as pattagam (patta produced) for jute fibre products and kitajam (insect produced) for silk products. In Ain-e-Akbari, there is mention of sack cloth made from jute, in Rangapur district.

Forages: The term forages denote plants that are used for feeding the animals that are cultivated in the farm, or it may be a wild used for stock feeding grown for the vegetative purpose. The vegetative phase is encouraged and the reproductive phase is suppressed.

Cumbu Napier hybrid grass is a cross between Cumbu (Pennisetum glaucum) and Napier grass (P. purpureum Schumach.), widely cultivated across India, Africa, Sri Lanka and South East Asian countries. As the hybrid is a triploid, it displays complete sterility and high vegetative growth. In Tamil Nadu, land area utilized for growing fodder is negligible, accounting only 1.6 per cent of the total cultivated area.

Cowpea is a quick growing leguminous forage crop. It is usually grown mixed with cereal fodders and grasses to improve the nutritive value of the herbage. It contains 20 – 24 % crude protein, 43 - 49 % neutral detergent fibre, 34 – 37 % acid detergent fibre, 23– 25 % cellulose and 5 – 6 % hemicelluloses on dry matter basis. The digestibility of cowpea fodder is above 70%. Cowpea can be grown under partial shaded conditions. It is an excellent cover crop, which suppresses weeds and enriches the soil. Cowpea requires warm climate with good atmospheric humidity. It can be grown in kharif as well as in zaid season.

Cartography has much back history than what we seen in portable form by its name or definition. Many prehistoric painting of caves had their own time frame record of maps, artifacts and design have been preserved hoping that they are sufficient to the era of forgotten habit and culture, and treasure explicit ancient world. The ancient wall painting of seventh millenium BC, might have indication of oldest maps in the world and the painting is assumed to be the location of ancient city Antolia as Mellaart, depicted 1967. Onward 600 BC, the modern form of cartography started to develop map. The pioneer contribution in map designing and development, old Greek and Roman were main contributor in this direction. One Greek Philoshopher Ptolemmy, multi-talented genius are recalled in this context, who later credited with first documented map of world during production of Geographica, is a treatise on cartography, very soon one Arabian Scholar Muhammad Al-Idrisi prepared a medieval Atlas involving knowledge of world collected by Arabic merchants in 1154.

In modem days, many informations receive through electronic media with instant connecting by satellite based information on social, economical, environmental etc. the informations are coded with feasible electronic encrypt and delivered to people. Similarly remote sensing, GIS (Geographical Information System) and GPS (Global Positioning System) also serve world for better understanding the ground reality along with reducing cost of operation, transforming data through electronic webs easily. In earlier phase, ground related information was not easily obtained because all kind of work had been done manually using analog maps of earth; even map draw was done by drafting from totposheet or hand maps, no interface of analog and digital world.

GPS stands for Global Positioning System, basically Navstar GPS is a radio based space aviation system developed by United state government which was operated by United State Air Force. It provides geographic location and time related information to GPS receivers under global navigation satellite from anywhere of earth where unobstructed line of sight to 4 or more GPS satellite co-ordinate (Anonymous, 2018). Even GPS not required any user for transmitting data, with working independently on any telephonic or internet receivers; through these technologies we can enhance usefulness of the GPS location based information. The GPS serves as critical positioning capacity to various fields like army operation, civil aviation, and commercial scope to wider scope in around world.