TOWARDS IMPROVED DISEASE IDENTIFICATION WITH PRETRAINED CONVOLUTIONAL NEURAL NETWORKS AS FEATURE EXTRACTORS FOR CHILI LEAF IMAGES

Abstract

Chili is a popular crop that is widely grown due to its flavorful and spicy fruit that is nutritionally beneficial. For the benefit of economic growth, it is important to precisely assess the chili health. With the advancement of computer vision-based applications, methods such as feature descriptors have been utilized to assist farm owners in identifying chili diseases via chili leaf images. However, these feature descriptors still require the manual extraction of disease features in order to accurately identify chili diseases. In this research, pretrained Convolutional Neural Networks (CNNs) are proposed as feature extractors to identify healthy and diseased chili leaf images. Three pretrained CNN models, DenseNet-201, EfficientNet-b0, and NasNet-Mobile, are utilized for their ability to identify healthy and diseased chili leaf using five indexes: accuracy, recall, specificity, precision, and F1-score. These indexes are validated through a five-fold cross-validation method during the experiments. The experimental results show that the EfficientNet-b0 model achieved the highest identification performance, with indexes of accuracy, recall, specificity, precision, and F1-score of 97.05%, 0.97, 0.92, 0.92, and 0.94, respectively. Therefore, the use of pretrained CNNs as feature extractors has the capability to enhance the efficiency and accuracy of chili disease identification in agricultural settings.