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SMART FARMING USING MACHINE LEARNING
SMART FARMING USING MACHINE LEARNING

Abstract 

Agriculture plays an important role in Indian economy. But now-a-days, agriculture in India is undergoing a structural change leading to a crisis situation. The only remedy to the crisis is to do all that is possible to make agriculture a profitable enterprise and attract the farmers to continue the crop production activities.

As an effort towards this direction, this research paper would help the farmers in making appropriate decisions regarding the cultivations with the help of machine learning. This paper focuses on predicting the appropriate crop based on the climatic situations and the yield of the crop based on the historic data by using supervised machine learning algorithms. In addition, a web application has been developed.

INTRODUCTION

In the past farmers used to predict their yield from previous year yield experiences. Thus, for this kind of data analytics in crop prediction, there are different techniques or algorithms, and with the help of those algorithms, we can predict crop yield. Nowadays, modern people don't have awareness about the cultivation of the crops at the right time and at the right place. By analyzing all the issues and problems like weather, temperature, and several factors, there is no proper solution and technologies to overcome the situation faced. Accurate information about history of crop yield is an important thing for making decisions related to agricultural risk management.

The main objectives are:

a. To use machine learning techniques to predict crop and yield of the crop.

b. To analyze the data properly and to process the data to get better predictions.

c. To improve the performance of machine learning models.

d. To build an easy to use web application.

PROBLEM STATEMENT

Crop selection and crop yield prediction are important agricultural problems. The aim of this project is to predict suitable crop based on the given climate parameters and location and also to predict the yield of that crop based on the season and area of the field using machine learning algorithms. 

ANALYSIS AND DESIGN

                                                 farming  

IMPLEMENTATION

For the first module which is basically a multiclass classification problem, we built the following models and evaluated their performance.

1. KNN

2. Support vector Machine

3. Random Forest

4. Naive Bayes

The metric we used for the evaluation is Cohen’s Kappa Score.. It is a very good measure that can handle very well both multi-class and imbalanced class problems.

For the second module which is basically a regression problem, we built the following models and evaluated their performance.

1. Multi-linear Regression

2. Random Forest Regression

3. Support Vector Regression

4. KNN Regression

CONCLUSION AND FUTURE SCOPE 

Crop and yield of the crop prediction using intelligent machine learning techniques may improve the crop planning decisions. For the Crop Prediction Module, the Cohen’s Kappa score we got for the Naive Bayes Classification Model is about 95%. For the Crop Yield Prediction Module, the R-Squared value we got for the Random Forest Regression Model is more than 81%.

Accurate forecasts of the climate parameters and better historic data of the crop would result in accurate crop and its yield forecast in the future. Also, the developed webpage is user friendly and can be made more informative by

providing additional useful information like intercropping, fertilizers etc. to the user. We can create more interactive User Interface by adding chatbots and speech recognition systems.

 

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