Vigorous Deep Learning Models for Identifying Tomato Leaf Diseases

Abstract

AbstractIdentifying and detecting a plant disease is a primary challenge in the farming segment. Specifically, for tomato farming: Early Blight, Late Blight, Mosaic Virus, Target Spot, Yellow Leaf Curl Virus (YLCV), Bacterial Spot, Leaf mold, Septoria leaf spot and two-spotted spider mite are nine general diseases that severely affect the yield. In this work, we propose convolutional neural networks (CNNs) based deep learning (DL) model for the identification of tomato leaf diseases. In our study, we take the Tomato Leaf Disease Dataset (TLDD) consisting of 18,160 images of the infected and the healthy tomato leaves from PlantVillage. We first select the most suitable and accurate deep learning models for disease identification experiments. Four popular models viz. SqueezeNet, ResNet50, InceptionV3 and DenseNet are chosen for the analysis. Lastly, using ImageAI library on Google Colaboratory (Colab), we train all the four models on the collected tomato-leaf-image dataset to identify the presence of above mentioned nine common tomato leaf diseases. Results from our experiments on the comparative study of the selected deep learning models identify nine different tomato leaf diseases. We infer that the InceptionV3 model provides the highest accuracy of 99.64%. Our chosen model provides faster detection with higher accuracy as compared to the other models. In future work, we intend to modify the algorithm to develop our own model for disease identification for other crops as well.KeywordsDeep learningConvolutional neural networkTomato leaf diseaseImageAI