Detecting Leaf Diseases in Bell Pepper, Potato, and Tomato Plants using Convolutional Neural Network

Using advanced machine learning techniques, specifically Convolutional Neural Networks (CNNs), this study focuses on detecting leaf diseases in three vital crops: Bell Pepper, Potato, and Tomato, crucial for global food production. A curated dataset contains various healthy and diseased plant images, covering diseases like Bacterial Spot, Early Blight, Late Blight, and more. The methodology involves data preprocessing, including augmentation techniques to improve the model's robustness. CNN is superior to SVM, pretrained models, Random Forest, MLP, and ensemble methods because CNN provides high scalability, which is crucial for our dataset consisting of 20,000 images. Additionally, CNN outperforms other methods with an impressive prediction accuracy of 98-99% on the training data. This work offers a scalable and adaptable solution for early disease detection, aiding farmers in implementing targeted disease management strategies and reducing crop losses. It represents a practical contribution to agriculture, leveraging CNNs to combat plant diseases effectively.

Keywords:- Convolutional Neural Networks (CNNs), Data Preprocessing, Data Augmentation, CNN Architecture, Machine Learning, Deep Learning, Image Classification, Food Security.