Thesis Abstract

Abstract
This thesis explores the application of machine learning algorithms for disease prediction in crop plants, with the aim of enhancing crop management practices and increasing agricultural productivity. The research addresses the growing need for more efficient and accurate methods of disease detection in crops, which is crucial for minimizing yield losses and ensuring food security. The study focuses on leveraging the capabilities of machine learning techniques to analyze large datasets of crop plant images and identify patterns associated with disease presence. The introduction provides a comprehensive overview of the importance of disease prediction in crop plants and the challenges faced by farmers and researchers in this domain. The background of the study delves into the current methods of disease detection in agriculture and highlights the limitations of traditional approaches. The problem statement emphasizes the need for more advanced and automated techniques to improve disease prediction accuracy and speed. The objectives of the study outline the specific goals and aims of the research, including the development of a machine learning model for disease prediction. The literature review chapter explores existing studies and research in the field of machine learning for disease prediction in agriculture. It covers topics such as image processing, feature extraction, and classification algorithms used in similar projects. The chapter provides a comprehensive overview of the state-of-the-art techniques and methodologies employed in disease prediction models for crop plants. The research methodology chapter details the experimental setup, data collection process, feature selection methods, and model training procedures used in the study. It also discusses the evaluation metrics and validation techniques employed to assess the performance of the machine learning model. The chapter highlights the importance of data preprocessing and model optimization in achieving accurate disease prediction results. The discussion of findings chapter presents the results of the experiments conducted to evaluate the performance of the machine learning model. It analyzes the accuracy, precision, recall, and other metrics to assess the effectiveness of the model in predicting crop plant diseases. The chapter also discusses the implications of the findings for crop management practices and future research directions in the field. In conclusion, this thesis demonstrates the potential of machine learning algorithms for disease prediction in crop plants. By harnessing the power of artificial intelligence and image analysis techniques, farmers and researchers can improve disease detection capabilities and make informed decisions to protect crop yields. The study contributes to the advancement of precision agriculture and lays the foundation for further research in this important area. Keywords Machine learning, Disease prediction, Crop plants, Agriculture, Image analysis, Precision agriculture.

Thesis Overview