Yao Yumeng, Deng Xiaodun, Zhang Xu, Li Junming, Sun Wenxuan, Zhang Gechao
School of Engineering, Xi'an International University, Xi'an, China.
Qilu University of Technology (Shandong Academy of Sciences), Jinan, China.
PeerJ Comput Sci. 2024 Nov 4;10:e2365. doi: 10.7717/peerj-cs.2365. eCollection 2024.
Recognition methods have made significant strides across various domains, such as image classification, automatic segmentation, and autonomous driving. Efficient identification of leaf diseases through visual recognition is critical for mitigating economic losses. However, recognizing leaf diseases is challenging due to complex backgrounds and environmental factors. These challenges often result in confusion between lesions and backgrounds, limiting information extraction from small lesion targets. To tackle these challenges, this article proposes a visual leaf disease identification method based on an enhanced attention mechanism. By integrating multi-head attention mechanisms, this method accurately identifies small targets of tomato lesions and demonstrates robustness in complex conditions, such as varying illumination. Additionally, the method incorporates Focaler-SIoU to enhance learning capabilities for challenging classification samples. Experimental results showcase that the proposed algorithm enhances average detection accuracy by 10.3% compared to the baseline model, while maintaining a balanced identification speed. This method facilitates rapid and precise identification of tomato diseases, offering a valuable tool for disease prevention and economic loss reduction.
识别方法在各个领域都取得了重大进展，如图像分类、自动分割和自动驾驶。通过视觉识别有效识别叶片病害对于减轻经济损失至关重要。然而，由于复杂的背景和环境因素，识别叶片病害具有挑战性。这些挑战常常导致病变与背景之间的混淆，限制了从小病变目标中提取信息。为应对这些挑战，本文提出了一种基于增强注意力机制的视觉叶片病害识别方法。通过集成多头注意力机制，该方法准确识别番茄病变的小目标，并在复杂条件下（如光照变化）表现出鲁棒性。此外，该方法结合了Focaler-SIoU来增强对具有挑战性的分类样本的学习能力。实验结果表明，与基线模型相比，所提出的算法将平均检测准确率提高了10.3%，同时保持了平衡的识别速度。该方法有助于快速、精确地识别番茄病害，为病害预防和减少经济损失提供了有价值的工具。
