Automatic Detection and Severity Assessment of Pepper Bacterial Spot Disease via MultiModels Based on Convolutional Neural Networks

Abstract

Pepper bacterial spot disease caused by Xanthomonas campestris is the most common pepper bacterial disease, which ultimately reduces productivity and quality of products. This work uses deep convolutional neural networks (CNNs) to serve fine-grained pepper bacterial spot disease severity classification tasks. The pepper bacterial spot disease leaf images collected from the PlantVillage dataset are further annotated by botanists and split into healthy samples (label1), general samples (label2), and serious samples (label3). To extract more effective and discriminative features, an integrated neural network denoted as MultiModel_VGR is proposed for automatic detection and severity assessment of pepper bacterial spot disease, which is based on three powerful and popular deep learning architectures, namely VGGNet, GoogLeNet and ResNet. Compared with state-of-the-art single CNN architectures and binary-integrated MultiModels, MultiModel_VGR yields the best overall accuracy of 95.34% on the hold-out test dataset, which may have great potential in crop disease control for modern agriculture.