ArticleSensors (Basel, Switzerland)2020
Crop Disease Classification on Inadequate Low-Resolution Target Images.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed.
- Smart crop disease monitoring system in IoT using optimization enabled deep residual network.Scientific reports · 2025Article
- Advancements in Imaging Sensors and AI for Plant Stress Detection: A Systematic Literature Review.Plant phenomics (Washington, D.C.) · 2024Article
- Artificial Intelligence-Based Drone System for Multiclass Plant Disease Detection Using an Improved Efficient Convolutional Neural Network.Frontiers in plant science · 2022Article
- Plant diseases and pests detection based on deep learning: a review.Plant methods · 2021Review
Corrections and comments
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Authors and funding
4 authors.
Funding
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.
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Registered trials
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