ABSTRACT
The main goal of this research was to accurately diagnose diseases in pepper plants using convolutional neural network (CNN) models and visual parameters features. Thus, we aimed to build a computing system that can utilise visual cues to identify plant diseases with almost 100% reliability. A total of 2,200 images were trained and tested on a dataset, which was divided into healthy leaves and those with bacterial ailments. The images were preprocessed, resized to 300x300 pixels, and then segmented to extract the relevant traits from them. This CNN model, in a word, is accurate because it detects diseases with 99.65% accuracy. If we want to speak of a high accuracy rate, that's the CNN model. It learns to identify the intricate patterns and features in pepper plants that are associated with diseases. CNN can make revolutionary changes to the identification of plant diseases and help prevent crop damage through timely treatments. Automatic identification of the disease makes life easier for both agriculturists and scientists. The results underscore the importance of image preprocessing, segmentation, and feature extraction in improving the ability of CNN models to locate diseases. Additionally, the performance of the CNN model was compared with other machine learning models such as KNN, Decision Tree, ANN, and Random Forest. It was found that the developed CNN model can predict the response with greater accuracy than the others. Modern high-tech precision farming systems might utilise proposed CNN models for plant disease diagnosis.
