Bell pepper leaf disease detection: A comprehensive review

Bell pepper (Capsicum annuum L.) production faces significant threats from various diseases caused by pathogens. This paper reviews advancements in digital image processing and machine learning (ML) techniques for early disease diagnosis and classification. We compare several ML models, including k-nearest Neighbours, Support Vector Machines, Random Forests, Convolutional Neural Networks (CNN), and Long Short-Term Memory networks. Results indicate that CNNs achieve the highest accuracy (95%) in disease detection. The integration of ML with Internet of Things technologies is discussed for real-time monitoring. This review highlights the potential of AI-driven approaches to enhance sustainable agriculture and improve crop yields.