High-Accuracy Maize Disease Detection Based on Attention Generative Adversarial Network and Few-Shot Learning.

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.

Plant phenotyping relevance

トウモロコシ病害を画像から検出する手法の開発と性能検証が研究の中心であり、植物の病害状態を直接推定しているため、植物フェノタイピング手法として収録する。

Code and data availability

The paper's maize disease image dataset is self-collected (field + web crawler) with no public deposit or availability statement; no author code, models, or supplementary data URLs are provided. The only URLs present (YOLOv5 GitHub, Kaggle Global Wheat Detection, CC BY license) are generic third-party resources or the

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