S. K. Noon, Muhammad Amjad, Muhammad Ali Qureshi, A. Mannan, Tehreem Awan
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引用次数: 0
摘要
在农业领域使用基于深度学习的工具自动检测植物叶片病害已有多年历史。然而,在农业领域的特定背景下,在存在其他叶片和土壤的情况下,如何优化这些工具的使用仍然是一个公开的挑战。本研究提出了一种基于 YOLOv6s 的深度学习模型,该模型包含(1)骨干中的高斯误差线性单元;(2)基本 RepBlock 中的高效通道注意;以及(3)SCYLLA-Intersection Over Union (SIOU) 损失函数,以提高基本模型在真实田间背景条件下的检测精度。实验是在一个自收集的数据集上进行的,该数据集包含 3305 幅棉花、小麦和芒果(健康和患病)叶片的真实田野图像。结果表明,所提出的模型在检测准确率方面优于许多最先进和最新的模型,包括基本的 YOLOv6s。此外,研究还发现,在计算成本没有显著增加的情况下,该模型的性能也得到了提高。因此,所提出的模型是在不增加计算负担的情况下检测真实田间条件下植物叶片病害的有效技术。
An Improved Detection Method for Crop & Fruit Leaf Disease under Real-Field Conditions
Using deep learning-based tools in the field of agriculture for the automatic detection of plant leaf diseases has been in place for many years. However, optimizing their use in the specific background of the agriculture field, in the presence of other leaves and the soil, is still an open challenge. This work presents a deep learning model based on YOLOv6s that incorporates (1) Gaussian error linear unit in the backbone, (2) efficient channel attention in the basic RepBlock, and (3) SCYLLA-Intersection Over Union (SIOU) loss function to improve the detection accuracy of the base model in real-field background conditions. Experiments were carried out on a self-collected dataset containing 3305 real-field images of cotton, wheat, and mango (healthy and diseased) leaves. The results show that the proposed model outperformed many state-of-the-art and recent models, including the base YOLOv6s, in terms of detection accuracy. It was also found that this improvement was achieved without any significant increase in the computational cost. Hence, the proposed model stood out as an effective technique to detect plant leaf diseases in real-field conditions without any increased computational burden.