Abstract
Currently, various agricultural image classification tasks are carried out on high-resolution images. However, in some cases, we cannot get enough high-resolution images for classification, which significantly affects classification performance. In this paper, we design a crop disease classification network based on Enhanced Super-Resolution Generative adversarial networks (ESRGAN) when only an insufficient number of low-resolution target images are available. First, ESRGAN is used to recover super-resolution crop images from low-resolution images. Transfer learning is applied in model training to compensate for the lack of training samples. Then, we test the performance of the generated super-resolution images in crop disease classification task. Extensive experiments show that using the fine-tuned ESRGAN model can recover realistic crop information and improve the accuracy of crop disease classification, compared with the other four image super-resolution methods.
Highlights
Crop diseases are generally caused by the environment, soil, pests and pathogens
We have proposed a method for crop disease identification on LR images by transferring LR images to SR images based on Generative Adversarial Network (GAN)
Due to insufficient crop data, we apply transfer learning to fine-tune the model trained on ImageNet
Summary
Crop diseases are generally caused by the environment, soil, pests and pathogens. They pose a severe threat to the quality and security of agricultural production [1,2]. With the development of computer science, it has become a hot topic to identify crop diseases based on computer vision and machine learning techniques. Image analysis methods based on deep learning have been used for crop disease identification and other purposes in agriculture, such as plant phenotypic analysis. Jia et al [5] used transfer learning to classify tomato pests and diseases on leaf images based on VGG16 network. In order to construct a cost-effective system to diagnose diseases and symptoms of mango leaves, a multi-layer convolutional neural network (MCNN) [7] was proposed to classify mango leaves infected by anthracnose disease
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