This document presents a project to create a reliable crop information system for farmers. It aims to develop a model to increase crop production and an application for farmers to predict the best seasonal and yearly crops, provide historical crop data, and details on seeds and pesticides for predicted crops. The project uses linear regression algorithms and input labels like rainfall, season, soil type, and production data to predict the best yielding crop as the output. It describes a working mobile application model that takes location and soil data from farmers to provide personalized best crop recommendations and information through voice and images. Technologies involved include Scratch, MIT App Inventor, Android App Studio, Java, JavaScript, IBM Bluemix, and machine learning courses.

1. Creating reliable and efficient
crop information systems for
farmers
presenting by :
M Balachandra reddy,
B.tech II year,
Computer science and
Engineering,
AP IIIT RGUKT RK Valley,
Kadapa.
S Vijay,
B.tech II year,
Computer science and
Engineering,
AP IIIT RGUKT RK Valley,
Kadapa.

2. Understanding the challenge
Task 1: Develop a model to increase the crop production.
Task 2: Develop an application, which a farmer can use.
 Predicting the best seasonal and yearly crops.
 Providing historical data of each crop.
 Providing the details of the seeds and pesticides of the predicted crop.
 Designing interactive and farmer friendly application.
Main theme of our project

3. Regression algorithm in predicting the best crop:
 Regression Algorithm is a multivariate analysis technique which analyzes the
factors groups them into explanatory and response variables and helps to obtain
a decision.
 How this algorithm is useful in our project?
 What is Linear regression?
 How is the Linear regression helps in predicting the best crop?
 What are dependent and independent variables in the prediction of the crop?

4. Input labels used in predicting the best yielded crop:
 Annual rainfall
 Season
 Type of the soil
 Food Price Index
 Area under cultivation
 Number of production units
 Subsidy
Output label:
 Crop

5. Working model of the application
 Farmers click on to the button and they have to say their state name, district
name, region in that district and also the type of the soil.
 In the application, we have the details of the location and by using some
inbuilt models, we also include the current temperature conditions there by
predicting the crop even in more efficient manner .
 After processing the above given data, the application is going to provide you
the details of the best crop through voice and images and also the details of the
seeds and pesticides related to that crop.

6. Technologies involved in the project
 Scratch.
 MIT App Inventor.
 Basic idea on Android App Studio.
 Beginner in Java and JavaScript.
 Basic idea on IBM Bluemix.
 Started course on Machine Learning in coursera.

7. ANY QUERIES?

8. THANK YOU