ArticlePlants (Basel, Switzerland)2023
High-Accuracy Maize Disease Detection Based on Attention Generative Adversarial Network and Few-Shot Learning.
Article in Plants (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- YOLOv11n-DualPC-Lite: a lightweight, high-precision real-time detection model for maize leaf diseases.Frontiers in plant science · 2026Article
- Cross-Modal and Contrastive Optimization for Explainable Multimodal Recognition of Predatory and Parasitic Insects.Insects · 2025Article
- An Efficient Model for Leafy Vegetable Disease Detection and Segmentation Based on Few-Shot Learning Framework and Prototype Attention Mechanism.Plants (Basel, Switzerland) · 2025Article
- A Diffusion-Based Detection Model for Accurate Soybean Disease Identification in Smart Agricultural Environments.Plants (Basel, Switzerland) · 2025Article
- Efficient Tomato Disease Detection Using MaxMin-Diffusion Mechanism and Lightweight Techniques.Plants (Basel, Switzerland) · 2025Article
- A Novel Few-Shot Learning Framework Based on Diffusion Models for High-Accuracy Sunflower Disease Detection and Classification.Plants (Basel, Switzerland) · 2025Article
- Few-shot object detection for pest insects via features aggregation and contrastive learning.Frontiers in plant science · 2025Article
- Review
- Automated lesion detection in cotton leaf visuals using deep learning.PeerJ. Computer science · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
This study addresses the problem of maize disease detection in agricultural production, proposing a high-accuracy detection method based on Attention Generative Adversarial Network (Attention-GAN) and few-shot learning. The method introduces an attention mechanism, enabling the model to focus more on the significant parts of the image, thereby enhancing model performance. Concurrently, data augmentation is performed through Generative Adversarial Network (GAN) to generate more training samples, overcoming the difficulties of few-shot learning. Experimental results demonstrate that this method surpasses other baseline models in accuracy, recall, and mean average precision (mAP), achieving 0.97, 0.92, and 0.95, respectively. These results validate the high accuracy and stability of the method in handling maize disease detection tasks. This research provides a new approach to solving the problem of few samples in practical applications and offers valuable references for subsequent research, contributing to the advancement of agricultural informatization and intelligence.
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Registered trials
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