Advancements in Plant Disease Detection: Leveraging Convolutional Neural Networks for Bell Pepper Health Assessment

In the field of plant-based disease diagnosis, convolutional neural networks (CNNs) have become an essential tool due to their exceptional ability to recognize sick foliage. With an emphasis on bell pepper plants specifically, this study provides a thorough examination of modern plant disease detection technologies. Several modeling techniques were investigated, with a particular emphasis on neurons and layers, by utilizing CNNs that were trained on a carefully selected dataset of photos of bell pepper plants. Since that early disease identification has a direct impact on crop quality, yield, and economic viability, its importance in agriculture cannot be emphasized. Our methodology, which makes it easier to identify sick bell pepper plants quickly, is a key component of proactive management plans that reduce agricultural losses and stop the spread of disease. This study emphasizes how critical it is to eliminate diseased plants as soon as possible in order to prevent illness from spreading across the garden and preserve crop health in general. As a result of these efforts, our CNN model was able to attain an incredible 96.5% accuracy, highlighting its potential to be a game-changing instrument in contemporary agriculture.