Crop Yield Prediction using machine learning in pyhton

 

Crop Yield Prediction using machine learning

 

 PROJECT ID: PYTHON08

 

PROJECT NAME: Crop Yield Prediction using machine learning

 

PROJECT CATEGORY: MCA / BCA / BCCA / MCM / POLY / ENGINEERING

 

PROJECT ABSTRACT:

Machine learning is an important decision support tool for crop yield prediction, including supporting decisions on what crops to grow and what to do during the growing season of the crops. Several machine learning algorithms have been applied to support crop yield prediction research. In this study, we performed a Systematic Literature Review (SLR) to extract and synthesize the algorithms and features that have been used in crop yield prediction studies. Based on our search criteria, we retrieved 567 relevant studies from six electronic databases, of which we have selected 50 studies for further analysis using inclusion and exclusion criteria. We investigated these selected studies carefully, analyzed the methods and features used, and provided suggestions for further research. According to our analysis, the most used features are temperature, rainfall, and soil type, and the most applied algorithm is Artificial Neural Networks in these models. After this observation based on the analysis of machine learning-based 50 papers, we performed an additional search in electronic databases to identify deep learning-based studies, reached 30 deep learning-based papers, and extracted the applied deep learning algorithms. According to this additional analysis, Convolutional Neural Networks (CNN) is the most widely used deep learning algorithm in these studies, and the other widely used deep learning algorithms are Long-Short Term Memory (LSTM) and Deep Neural Networks (DNN)

Research questions

This SLR aims to get insight into what studies have been published in the domain of ML and crop yield prediction. To get insight, studies have been analyzed from several dimensions. For this SLR study, the following four research questions(RQs) have been defined.

RQ1- Which machine learning algorithms have been used in the literature for crop yield prediction?

RQ2- Which features have been used in literature for crop yield prediction using machine learning?

RQ3- Which evaluation parameters and evaluation approaches have been used in literature for crop yield prediction?

RQ4- What are challenges in the field of crop yield prediction using machine learning?

 

SOFTWARE REQUIREMENTS:

OS                                : Windows

Python IDE                  : Python 2.7.x and above

Language                             : Python Programming

Database                             : MYSQL

 

HARDWARE REQUIREMENTS:

 RAM                :  4GB and Higher

Processor          :  Intel i3 and above

Hard Disk         : 500GB Minimum

 

CONCLUSION

This study showed that the selected publications use a variety of features, depending on the scope of the research and the availability of data. Every paper investigates yield prediction with machine learning but differs from the features. The studies also differ in scale, geological position, and crop. The choice of features is dependent on the availability of the dataset and the aim of the research. Studies also stated that models with more features did not always provide the best performance for the yield prediction. To find the best performing model, models with more and fewer features should be tested. Many algorithms have been used in different studies. The results show that no specific conclusion can be drawn as to what the best model is, but they clearly show that some machine learning models are used more than the others. The most used models are the random forest, neural networks, linear regression, and gradient boosting tree. Most of the studies used a variety of machine learning models to test which model had the best prediction.

TABLE OF CONTENTS

·        Title Page      

·        Declaration

·        Certification Page

·        Dedication

·        Acknowledgements

·        Table of Contents

·        List of Tables

·        Abstract

 

CHAPTER SCHEME

CHAPTER ONE: INTRODUCTION

CHAPTER TWO: OBJECTIVES

CHAPTER THREE: PRELIMINARY SYSTEM ANALYSIS

·         Preliminary Investigation

·         Present System in Use

·         Flaws In Present System

·         Need Of New System

·         Feasibility Study

·         Project  Category

CHAPTER FOUR: SOFTWARE ENGINEERING AND PARADIGM APPLIED   

·         Modules

·         System / Module Chart

CHAPTER FIVE: SOFTWARE AND HARDWARE REQUIREMENT

CHAPTER SIX: DETAIL SYSTEM ANALYSIS

·         Data Flow Diagram

·         Number of modules and Process Logic

·         Data Structures  and Tables

·         Entity- Relationship Diagram

·         System Design

·         Form Design 

·         Source Code

·         Input Screen and Output Screen

CHAPTER SEVEN: TESTING AND VALIDATION CHECK

CHAPTER EIGHT: SYSTEM SECURITY MEASURES

CHAPTER NINE: IMPLEMENTATION, EVALUATION & MAINTENANCE

CHAPTER TEN: FUTURE SCOPE OF THE PROJECT

CHAPTER ELEVEN: SUGGESTION AND CONCLUSION

CHAPTER TWELE: BIBLIOGRAPHY& REFERENCES          

Other Information

 

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