Deep Learning Approaches for Tomato Plant Disease Detection

Agriculture is the mainstream to keep pace in the Bangladeshi economy. Plant disease became a threat to food security as it is a very important factor to deteriorate the quality and quantity of harvest. Therefore, it is important to detect the plant diseases early which results in interrupting from falling the massive destruction of harvest. But, an erroneous diagnosis of the disease results in the inappropriate use of pesticides. In order to enhance the production quality and quantity, a deep learning-based approach is proposed to detect the tomato leaf diseases, and then classify the types of the disease using image dataset. This proposed approach trained two model architectures: inception V3 and Convolutional Neural Network (CNN). Inception V3 performs well and reaches a success rate with a 96.11% in order to identify whether the specific plant leaf is infected or healthy. The success rate is significant and makes this approach as a very useful way or early forewarning tool, and this approach might be an essential system to operate real agriculture fields. As the detection accuracy is recorded as 94.72% for CNN. We confirm that it achieves the experimental results with 94.72% and 96.11% for the detection and classification of infected leaves from dataset for CNN and Inception V3 respectively. 1