Tomato Leaf Disease Detection and Classification using Deep Learning.

Abstract

Tomato is also a profitable crop in Bangladesh and many other countries because of its high price and market value. But the development and productivity of tomato are constantly threatened by several kinds of leaf diseases, which rapidly damage these plants. The early diagnosis of plant illness and continuous monitoring of plant health are important for the removal of factors that affect crop yield. This work presents an automatic way to detect and recgnize tomato leaf diseases by a novel deep learning mechanism. For tomato forest-based diseases, we create the custom dataset which has 600 labeled images includes 150 images for Tomato Early Blight, 150 images for Tomato Late Blight, 150 images for Tomato Leaf Mold, and 150 images are healthy for leaves. These images were captured directly from local farm fields to be as practical as possible. Four pretrained CNN models VGG16, VGG19, ResNet50V2, and InceptionV3 were tested to find the optimum performing model based on transfer learning. VGG19 and InceptionV3 obtained the maximum accuracy of 97% from them. This technology can assist farmers to quickly and correctly identify crop diseases, make timely corrective measures, therefore enhancing the quality and yield of the crops, especially referring to remote rural areas where expert consultation is not easily accessible.