Timely determination of weed distributions in fields is crucial for the precise spraying of herbicides. This facilitates weed control while saving costs and protecting the environment. Existing weed detection strategies often rely on the utilization of numerous weed samples to train detection models directly, which presents challenges in situations involving limited weed samples. To address this issue, a novel weed detection strategy was proposed in this study to identify weeds accurately in fields with varying coverage levels. For this purpose, red–green–blue (RGB) images of maize fields with different weed coverage levels were captured via a vertical take-off and landing fixed-wing unmanned aerial vehicle (UAV). The UAV images were first mosaicked, and a new weed detection strategy was developed and assessed. In this process, the MeanShift segmentation method, coupled with the local variance (LV) segmentation evaluation function and the Otsu automatic classification method, was initially employed to extract vegetation areas. The you-only-look-once (YOLO) v5n model was subsequently improved and used to detect maize plants. Finally, weed mapping was achieved by removing the identified maize plants from the vegetation through overlay analysis. The evaluation of the proposed method via an external dataset yielded favorable weed detection results, with an value of 0.96 and a root mean square error value of 3.08 % under the different weed coverage levels. Specifically, in addition to adjusting the activation function and the nonmaximum suppression method, the impacts of integrating various attention modules at different positions on the performance of the YOLO v5n model for maize plant detection were analyzed. Improving the YOLO v5n model by incorporating the efficient channel attention (ECA) module into the backbone of the original model and utilizing the Hardswish activation function is recommended. Overall, this study offers support for precise weed control.

中文翻译：

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玉米田杂草检测的新策略


及时确定田间杂草分布对于除草剂的精确喷洒至关重要。这有利于杂草控制，同时节省成本并保护环境。现有的杂草检测策略通常依赖于利用大量杂草样本来直接训练检测模型，这在杂草样本有限的情况下提出了挑战。为了解决这个问题，本研究提出了一种新颖的杂草检测策略，可以在不同覆盖水平的田地中准确识别杂草。为此，通过垂直起降的固定翼无人机（UAV）捕获了不同杂草覆盖水平的玉米田的红绿蓝（RGB）图像。首先对无人机图像进行镶嵌，并开发和评估了新的杂草检测策略。在此过程中，初步采用MeanShift分割方法，结合局部方差（LV）分割评价函数和Otsu自动分类方法来提取植被区域。 you-only-look-once (YOLO) v5n 模型随后得到改进并用于检测玉米植株。最后，通过叠加分析从植被中去除已识别的玉米植物，从而实现杂草绘图。通过外部数据集对所提出的方法进行评估，得到了良好的杂草检测结果，在不同杂草覆盖水平下，杂草检测结果为0.96，均方根误差值为3.08%。具体来说，除了调整激活函数和非极大值抑制方法外，还分析了在不同位置集成各种注意力模块对YOLO v5n模型用于玉米植株检测的性能的影响。 建议通过将高效通道注意（ECA）模块合并到原始模型的主干中并利用 Hardswish 激活函数来改进 YOLO v5n 模型。总体而言，这项研究为精确杂草控制提供了支持。