Computer vision-based precision weed control offers a promising avenue for reducing herbicide input and the associated costs of weed management. However, the substantial investments in time and labor required for the collection and annotation of weed image data pose challenges to develop effective deep learning models. The limitation also stems from the challenges in achieving accurate and reliable detection of weeds across varying growth stages, densities, and ecotypes in field scenarios. To address these issues, this research investigated a novel methodology employing a segmentation algorithm to accurately mark the contour information of crops in the image and detect weeds through image processing technology. Furthermore, a novel segmentation network was developed based on the YOLO architecture to address the substantial computing resource demands associated with segmentation algorithms. This was achieved through the design of a new backbone, incorporation of an attention mechanism, and modification of the feature fusion technique. The novel network achieved higher segmentation accuracy with less computational demands. The effectiveness of three different attention modules on segmentation tasks was additionally investigated. Experimental results showed that the insertion of Criss-cross Attention significantly improved the model's performance and was subsequently incorporated into our enhanced methodology. The enhanced model achieved a Mean Intersection over Union (mIoU50) of 90.9 %, with precision increasing by 5.9 % and Giga FLoating-point Operations Per Second (GFLOPs) reduced by 15.56 %, demonstrating enhanced suitability for deployment in resource-constrained computing environments. The findings presented in this study hold substantial theoretical and practical implications for precise weed management.

中文翻译：

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高效的作物分割网及新型杂草检测方法


基于计算机视觉的精确杂草控制为减少除草剂投入和杂草管理的相关成本提供了一条有前途的途径。然而，收集和注释杂草图像数据所需的大量时间和劳动力投资对开发有效的深度学习模型构成了挑战。这种局限性还源于在田间场景中准确可靠地检测不同生长阶段、密度和生态型的杂草所面临的挑战。为了解决这些问题，本研究研究了一种采用分割算法的新方法，该方法准确标记图像中作物的轮廓信息，并通过图像处理技术检测杂草。此外，基于 YOLO 架构开发了一种新的分割网络，以满足与分割算法相关的大量计算资源需求。这是通过设计新的主干、结合注意力机制和修改特征融合技术来实现的。这种新颖的网络以更少的计算需求实现了更高的分割精度。此外，还研究了三种不同的注意力模块对分割任务的有效性。实验结果表明，交叉注意力的插入显着提高了模型的性能，并随后被纳入我们的增强方法。增强模型实现了 90.9% 的平均交并比 （mIoU50），精度提高了 5.9%，每秒千兆浮点运算 （GFLOP） 降低了 15.56%，表明在资源受限的计算环境中部署的适用性增强。 本研究中提出的研究结果对精确的杂草管理具有重要的理论和实践意义。