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Cloud-based software is used for the management of financial and field activities of farms. Prior to computers, farmers maintained data manually by keeping lengthy records on papers. This method was prone to human calculation errors. After the computer boom in the 1980s, it was not long before finance software such as Money Counts came to market. 

Smart Farming is a hi-tech and effective system of doing agriculture and growing food in a sustainable way. It is an application of implementing connected devices and innovative technologies together into agriculture. Smart Farming majorly depends on IoT thus eliminating the need of physical work of farmers and growers and thus increasing the productivity in every possible manner.

With the recent agriculture trends dependent on agriculture, Internet of Things has brought huge benefits like efficient use of water, optimization of inputs and many more. 

Since the industrial revolution in the 1800s, automation got more advanced to efficiently handle sophisticated tasks and increase production. With increasing demands and shortage of labor across the globe, agriculture robots or commonly known as Agribots are starting to gain attention among farmers. Crop production decreased by an estimated 213 crores approx ($3.1 billion) a year due to labor shortages in the USA alone. Recent advancements in sensors and AI technology that lets machines train on their surroundings have made agrobots more notable. We are still in the early stages of an ag-robotics revolution, harnessing the full potential of the Internet of Things in agriculture, with most of the products still in early trial phases and R&D mode.

Easykrishi is a young enterprise that uses digital interventions with an aim to change agricultural practices from Reactive Mode to Proactive Mode. It is a consortium of 12 FPOs and 150 Ground Organizations. 14 In a world of declining land-holdings, we realize aggregation of agricultural resources is the only way to keep this planet sustainable for human survival. Aggregation ensures viability and profitability of Agriculture. Easykrishi’s baby steps in the entire cycle of aggregation of agricultural resources have made significant impact for Food Producer Corporations across Karnataka and they have made a mark on organizations and individuals that are dedicated for the betterment of Agriculture and farmers.

The goal of sustainable agriculture is to meet society’s food and textile needs in the present without compromising the ability of future generations to meet their own needs.

Practitioners of sustainable agriculture seek to integrate three main objectives into their work: a healthy environment, economic profitability, and social and economic equity. Every person involved in the food system—growers, food processors, distributors, retailers, consumers, and waste managers—can play a role in ensuring a sustainable agricultural system.

Although “e-agriculture” and other initiatives to bridge the rural digital divide were already underway around the world, the Community (and the term “e-agriculture”) emerged following the World Summits on the Information Society in 2003 and 2005. The WSIS global participants recognized that when addressing the challenges of the digital divide, particularly in the context of rural livelihoods, problems extend beyond technology. It is a multifaceted problem characterized by ineffective knowledge exchange and information content management, as well as a lack of human resources, institutional capacity, and sensitivity to gender and the diverse needs of various groups. With WSIS participants identifying and naming “e-agriculture” as a key action line to address the Millennium Development Goals, the United Nations Food and Agriculture Organization (FAO) was tasked with leading the development and subsequent facilitation activities that would truly engage stakeholders at all levels.

Technology spurs Agricultural productivity through various scientific methods and innovations which is evidenced by the success of the Green revolution leading to agricultural development.

The Green Revolution arrived in India in 1968 and had a remarkable impact on the world of agriculture by increasing production yield by 2.5 times, amounting to approximately 2 billion tones, which has been noted as the world’s highest growth rate in the agriculture sector. Because of the science and technologies used in the form of

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)