Potato Plant Leaf Disease Detection Using Custom CNN Deep Net: A Step Towards Sustainable Agriculture

Detecting potato plant leaf diseases using convolutional neural networks (CNNs) is a significant step towards sustainable agriculture. Computer vision-based automated disease detection in agriculture is essential for the early detection and treatment of plant diseases, assisting farmers in minimizing crop losses, maximizing yields, and guaranteeing food security. This study proposes a novel method for detecting potato leaf disease using a customized 5-layer Convolutional Neural Network (CNN). The model’s performance is compared with MobileNet and a 4-layer CNN as the backbone architectures. Based on experimental results, the 5-layer customized CNN achieves an accuracy rate of 97.16%, which is significantly higher than that of the 4-layer CNN (73.21%) and MobileNet (78.43%). Additionally, the 5-layer CNN model shows promising results for other evaluation metrics, including F1 score (97.18%), recall (97.16%), and precision (97.24%). Furthermore, out of all the models that were tested, the 5-layer CNN model shows the least amount of loss. Using a threshold of 0.6, and the custom CNN as backbone architecture to Faster R-CNN (FRCNN) the model achieved an Intersection over Union (IoU) of 0.76 for disease detection. Additionally, a comparative study of various optimizers (Adam, SGD, Adadelta, and AdamW) and loss functions is done; the Adam optimizer and the unique 5-layer CNN model yielded the best results. This study advances automated methods for detecting potato leaf diseases, offering a dependable and effective way to identify diseases early in agricultural settings.