Song Yihong, Zhang Haoyan, Li Jiaqi, Ye Ran, Zhou Xincan, Dong Bowen, Fan Dongchen, Li Lin
China Agricultural University, Beijing 100083, China.
School of Computer Science and Engineering, Beihang University, Beijing 100191, China.
Plants (Basel). 2023 Aug 29;12(17):3105. doi: 10.3390/plants12173105.
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.
本研究针对农业生产中的玉米病害检测问题，提出了一种基于注意力生成对抗网络（Attention-GAN）和少样本学习的高精度检测方法。该方法引入了注意力机制，使模型能够更专注于图像的重要部分，从而提高模型性能。同时，通过生成对抗网络（GAN）进行数据增强，以生成更多训练样本，克服少样本学习的困难。实验结果表明，该方法在准确率、召回率和平均精度均值（mAP）方面均超过其他基线模型，分别达到了0.97、0.92和0.95。这些结果验证了该方法在处理玉米病害检测任务中的高精度和稳定性。本研究为解决实际应用中样本少的问题提供了一种新方法，并为后续研究提供了有价值的参考，有助于推动农业信息化和智能化的发展。
