**Juan Wen, Yangjing Shi, Xiaoshi Zhou and Yiming Xue \***

College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; wenjuan@cau.edu.cn (J.W.); sy20183081433@cau.edu.cn (Y.S.); zhouxiaoshi0713@163.com (X.Z.)

**\*** Correspondence: xueym@cau.edu.cn

Received: 1 July 2020; Accepted: 13 August 2020; Published: 16 August 2020

**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.

**Keywords:** super-resolution; Generative Adversarial Networks; Convolutional Neural Networks; disease classification
