Mid- to late-season weeds that escape from the routine early-season weed management threaten agricultural production by creating a large number of seeds for several future growing seasons. Rapid and accurate detection of weed patches in field is the first step of site-specific weed management. In this study, object detection-based convolutional neural network models were trained and evaluated over low-altitude unmanned aerial vehicle (UAV) imagery for mid- to late-season weed detection in soybean fields. The performance of two object detection models, Faster RCNN and the Single Shot Detector (SSD), were evaluated and compared in terms of weed detection performance using mean Intersection over Union (IoU) and inference speed. It was found that the Faster RCNN model with 200 box proposals had similar good weed detection performance to the SSD model in terms of precision, recall, f1 score, and IoU, as well as a similar inference time. The precision, recall, f1 score and IoU were 0.65, 0.68, 0.66 and 0.85 for Faster RCNN with 200 proposals, and 0.66, 0.68, 0.67 and 0.84 for SSD, respectively. However, the optimal confidence threshold of the SSD model was found to be much lower than that of the Faster RCNN model, which indicated that SSD might have lower generalization performance than Faster RCNN for mid- to late-season weed detection in soybean fields using UAV imagery. The performance of the object detection model was also compared with patch-based CNN model. The Faster RCNN model yielded a better weed detection performance than the patch-based CNN with and without overlap. The inference time of Faster RCNN was similar to patch-based CNN without overlap, but significantly less than patch-based CNN with overlap. Hence, Faster RCNN was found to be the best model in terms of weed detection performance and inference time among the different models compared in this study. This work is important in understanding the potential and identifying the algorithms for an on-farm, near real-time weed detection and management.

中文翻译：

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无人机图像中后期杂草检测中目标检测和基于补丁的分类深度学习模型的比较

摆脱常规早季杂草管理的中期至后期杂草通过为未来几个生长季节产生大量种子而威胁农业生产。快速，准确地检测田间杂草斑块是特定地点杂草管理的第一步。在这项研究中，基于对象检测的卷积神经网络模型在低空无人机图像上得到训练和评估，用于大豆田中后期的杂草检测。评估并比较了两个对象检测模型Faster RCNN和单发检测器（SSD）的性能，并使用联合平均交点（IoU）和推断速度对杂草检测性能进行了比较。结果发现，具有200个框提议的Faster RCNN模型在精度，召回率，f1得分和IoU以及相似的推理时间方面具有与SSD模型类似的良好杂草检测性能。使用200个提案的Faster RCNN的精度，召回率，f1得分和IoU分别为0.65、0.68、0.66和0.85，对于SSD分别为0.66、0.68、0.67和0.84。但是，发现SSD模型的最佳置信度阈值比Faster RCNN模型的最佳置信度阈值低得多，这表明对于使用UAV的大豆田中后期杂草检测，SSD的泛化性能可能比Faster RCNN低。图像。还将对象检测模型的性能与基于补丁的CNN模型进行了比较。Faster RCNN模型比有和没有重叠的基于补丁的CNN产生更好的杂草检测性能。Faster RCNN的推理时间类似于不带重叠的基于补丁的CNN，但明显少于带重叠的基于补丁的CNN。因此，在本研究比较的不同模型中，就杂草检测性能和推断时间而言，Faster RCNN被认为是最佳模型。这项工作对于了解农场的潜力以及确定农场中近实时杂草检测和管理的算法非常重要。在本研究中，在不同模型之间，杂草检测性能和推断时间方面，最快的RCNN被认为是最佳模型。这项工作对于了解农场的潜力以及确定农场中近实时杂草检测和管理的算法非常重要。在本研究中，在不同模型之间，杂草检测性能和推断时间方面，最快的RCNN被认为是最佳模型。这项工作对于了解农场的潜力以及确定农场中近实时杂草检测和管理的算法非常重要。