Potato-Net: Classifying Potato Leaf Diseases Using Transfer Learning Approach

Abstract

Research on pertinent topics is more important than ever for the long-term development of agriculture, given the advancements in contemporary farming and use of artificial intelligence (AI) for identifying crop illnesses. There are numerous diseases, and they all significantly affect the amount and quality of potatoes. Early and automated detection of these illnesses during the budding phase can assist increase the output of potato crops, but it requires a high level of ability. Several models have already been created to identify various plant diseases. In this study, we use a variety of convolutional neural network designs to recognize potato leaf disease and assess their early detection accuracy against that of other researchers’ work. The learning sample for our algorithm included both the original and enhanced photos, as a learning option. The model was then evaluated to ensure that it was accurate. After being trained on the dataset for the potato leaf disease using the Inception-v3, Xception, and ResNet50 models, the model’s performance was evaluated using test images. ResNet50 has the highest accuracy and lowest error rate for detecting potato leaf disease, followed by Inception-v3 with an accuracy of nighty four point two five percent (94.25%) and Xception with an accuracy of eighty-nine point seven one percent (89.71%).