Machine Learning Approach to Predict Tomato Leaf Disease

Abstract

The machine learning approach used in this thesis uses a large dataset of leaf photos and associated disease labels to predict tomato leaf diseases. A large number of tomato leaf photos, comprising both healthy and diseased leaves, is gathered for the research. Techniques for preprocessing are used to enhance picture quality and extract relevant characteristics. The objective of the technique is to find distinguishing traits in leaf pictures that distinguish between various disease classifications. Convolutional neural networks (CNNs), decision trees, random forests, support vector machine, and other machine learning methods are assessed for disease prediction. To evaluate the performance of the models, the cross-validation methods are used to verify them on the labelled dataset. To learn more about the visual patterns connected to each condition, feature importance analysis is done. Techniques for transfer learning are investigated to make better use of taught models. The experimental findings show a high degree of prediction accuracy for tomato leaf diseases, with transfer learning-based CNN-based models outperforming more conventional methods. The research helps create an automated method for early disease identification, which helps farmers and specialists execute effective disease control techniques on time, reducing crop losses, and maintaining sustainable tomato production. The paper highlights the potential of machine learning in plant pathology and encourages further investigation into related methodologies for additional plant species and disease classes.