Deep Learning Models for Potato Leaf Disease Identification: A Comparative Analysis

Plant diseases are the most crucial factor in the agriculture sector, which causes a reduction in yield and economic loss. Therefore, early and accurate detection of these diseases can control the infection spread to other crops and minimize production loss. Traditional methods use the handcrafted features of the images to detect the infection part of the leaves and infection type. Furthermore, the extraction of these features is expensive and time-consuming. However, in light of recent advances in agricultural technology, such as the use of artificial intelligence in diagnosing plant diseases, appropriate research must be conducted toward the development of agriculture in a sustainable manner. However, manually interpreting these leaf diseases can be time-consuming and laborious, and they significantly impact potato quality and yield due to diseases like early blight and late blight. In addition, this study seeks to optimize cutting-edge deep learning (DL) models for detecting potato leaf disease. The deep learning models such as ResNet50, Inception V3, VGG16, and VGG19 are evaluated and their performances are compared. The experimental findings show that the VGG19 model outperforms the other models with an accuracy of 99%.