Plant disease classification using deep learning techniques is a popular research area due to the numerous opportunities for introducing advance and robust classifiers. Nevertheless, classifying chilli plant diseases accurately from images under uncontrolled environment and various imaging conditions remains unsolved due to the lack of chilli disease image datasets. In this study, the efficacy of three high-performance deep learning algorithms, namely VGG16, InceptionV3, and EfficientNetB0, in classifying three types of chilli leaves diseases, namely upward curling, mosaic/mottling, and the bacterial spot, is demonstrated. These methods are popularly used for other plant disease classifications due to their effectiveness. The experiments were performed on the 3,000 chilli plant disease images collected from three different field environments in Selangor, Malaysia. The images were captured with a complex background and various illuminations, angles, and distances to reflect the real-life scenarios. The complexity of the collected images was created based on the taxonomic information of chilli leaves diseases and the unavailability of chilli disease images under various imaging conditions in the publicly available plant disease databases. Experimented using appropriate specifications, the models demonstrated outstanding performance with more than 95% accuracy with the highest accuracy of 98.83% by InceptionV3

This study tackles the shortage of chilli disease image datasets by creating 3,000 images of leaves with upward curling, mosaic/mottling, and bacterial spot, captured in real farming conditions across Selangor, Malaysia. The images include different lighting, angles, distances, and complex backgrounds to make them realistic and practical. Three deep learning models—VGG16, InceptionV3, and EfficientNetB0—were tested, with InceptionV3 achieving the best accuracy at 98.83%. The results show that deep learning can reliably detect chilli diseases in uncontrolled conditions, helping farmers diagnose problems faster, reduce losses, improve crop quality, and support the development of smart farming tools.