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Introduction:

Developing AI-based yield prediction models is a critical area of research in agricultural science and technology. With the increasing demand for food production to feed the growing population, there is a pressing need for accurate and reliable tools to predict crop yields. AI-based models have shown great potential in accurately predicting crop yields by analyzing a vast amount of data and making precise predictions. This thesis aims to explore the development of AI-based yield prediction models and their application in improving agricultural productivity.

Table of Contents:

Chapter 1: Introduction
1.1 Introduction
1.2 Objective of Study
1.3 Limitation of Study
1.4 Scope of Study

Chapter 2: Literature Review
2.1 Overview of AI-based yield prediction models
2.2 Applications of AI in agriculture
2.3 Previous studies on yield prediction models
2.4 Challenges and opportunities in AI-based yield prediction

Chapter 3: Research Methodology
3.1 Data collection and preprocessing
3.2 Feature selection and model development
3.3 Evaluation metrics
3.4 Experimental setup

Chapter 4: Discussion of Findings
4.1 Analysis of model performance
4.2 Comparison with existing models
4.3 Interpretation of results
4.4 Implications for agriculture

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Contributions to the field
5.3 Future research directions
5.4 Conclusion

Thesis Overview:

Developing AI-based yield prediction models is a critical area of research that aims to leverage the power of artificial intelligence in predicting crop yields. This thesis explores the development of AI-based models and their application in improving agricultural productivity. The study begins with an introduction to the topic, outlining the objective, scope, and limitations of the study. It then delves into a comprehensive literature review that covers the overview of AI-based yield prediction models, applications of AI in agriculture, previous studies in the field, and challenges and opportunities in AI-based yield prediction.

The research methodology chapter details the data collection and preprocessing process, feature selection, model development, evaluation metrics, and experimental setup. The discussion of findings chapter analyzes the model performance, compares it with existing models, interprets the results, and discusses the implications for agriculture. Finally, the conclusion and summary chapter provides a summary of findings, outlines the contributions to the field, suggests future research directions, and concludes the thesis.

Overall, this thesis aims to contribute to the development and application of AI-based yield prediction models in agriculture, providing insights and recommendations for future research in this area.

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