Yang Zhanpeng, Wu Jun, Yuan Xianju, Chen Yaxiong, Guo Yanxin
School of Automotive Engineering, Hubei University of Automotive Technology, Shiyan, China.
School of Mathematics, Physics and Optical Engineering, Hubei University of Automotive Technology, Shiyan, China.
PeerJ Comput Sci. 2024 Nov 26;10:e2545. doi: 10.7717/peerj-cs.2545. eCollection 2024.
Traditional disease retrieval and localization for plant leaves typically demand substantial human resources and time. In this study, an intelligent approach utilizing deep hash convolutional neural networks (DHCNN) is presented to address these challenges and enhance retrieval performance. By integrating a collision-resistant hashing technique, this method demonstrates an improved ability to distinguish highly similar disease features, achieving over 98.4% in both precision and true positive rate (TPR) for single-plant disease retrieval on crops like apple, corn and tomato. For multi-plant disease retrieval, the approach further achieves impressive Precision of 99.5%, TPR of 99.6% and F-score of 99.58% on the augmented PlantVillage dataset, confirming its robustness in handling diverse plant diseases. This method ensures precise disease retrieval in demanding conditions, whether for single or multiple plant scenarios.
传统的植物叶片病害检索与定位通常需要大量的人力和时间。在本研究中，提出了一种利用深度哈希卷积神经网络（DHCNN）的智能方法来应对这些挑战并提高检索性能。通过集成抗碰撞哈希技术，该方法在区分高度相似病害特征方面表现出更强的能力，在苹果、玉米和番茄等作物的单株病害检索中，准确率和真阳性率（TPR）均超过98.4%。对于多株病害检索，该方法在增强的PlantVillage数据集上进一步实现了令人印象深刻的99.5%的精确率、99.6%的TPR和99.58%的F值，证实了其在处理多种植物病害方面的稳健性。无论对于单株还是多株植物场景，该方法都能在苛刻条件下确保精确的病害检索。
