Crop Disease Classification on Inadequate Low-Resolution Target Images

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...

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Bibliographic Details
Main Authors: Juan Wen, Yangjing Shi, Xiaoshi Zhou, Yiming Xue
Format: Article
Language:English
Published: MDPI AG 2020-08-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/20/16/4601
Description
Summary: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.
ISSN:1424-8220