Maize leaf disease identification using deep transfer convolutional neural networks

Abstract

Gray leaf spot, common rust, and northern leaf blight are three common maize leaf diseases that cause great economic losses to the worldwide maize industry. Timely and accurate disease identification can reduce economic losses, pesticide usage, and ensure maize yield and food security. Deep learning methods, represented by convolutional neural networks (CNNs), provide accurate, effective, and automatic diagnosis on server platforms when enormous training data is available. Restricted by dataset scale and application scenarios, CNNs are difficult to identify small-scale data sets on mobile terminals, while the lightweight networks, designed for the mobile terminal, achieve a better balance between efficiency and accuracy. This paper proposes a two-staged deep-transfer learning method to identify maize leaf diseases in the field. During the deep learning period, 8 deep and 4 lightweight CNN models were trained and compared on the Plant Village dataset, and ResNet and MobileNet achieved test accuracy of 99.48% and 98.69% respectively, which were then migrated onto the field maize leave disease dataset collected on mobile phones. By using layer-freezing and fine-tuning strategies on ResNet and MobileNet, fine-tuned MobileNet achieved the best accuracy of 99.11%. Results confirmed that disease identification performance from lightweight CNNs was not inferior to that of deep CNNs and transfer learning training efficiency was higher when lacking training samples. Besides, the smaller gaps between source and target domains, the better the identification performance for transfer learning. This study provides an application example for maize disease identification in the field using deep-transfer learning and provides a theoretical basis for intelligent maize leaf disease identification from images captured with mobile devices.

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