抑制杂草是影响作物产量的重要因素。通过使用适当的除草剂,精确识别杂草种类将有助于自动除草,hoeing位置确定,并对特定植物进行深度播种,并减少作物伤害。然而,田间杂草数据集的缺乏限制了深度学习技术在杂草管理中的应用。在本文中,它提供了田间杂草的数据集,Weed25,其中包含25种不同杂草的14035张图像。单子叶和双子叶杂草图像资源均包括在该数据集中。同时,还记录了不同生长阶段的杂草图像。几种常见的深度学习检测模型-YOLOv3,YOLOv5和FasterR-CNN-被应用于使用该数据集的杂草识别模型训练。结果表明,相同训练参数下的平均检测准确率为91.8%,92.4%,和分别为92.15%。它表明Weed25可能是进一步开发现场实时杂草识别模型的潜在有效培训资源。该数据集位于https://pan。baidu.com/s/1rnUoDm7IxxmX1n1LmtXNXw;密码为rn5h。
Weed suppression is an important factor affecting crop yields. Precise identification of weed species will contribute to automatic weeding by applying proper herbicides, hoeing position determination, and hoeing depth to specific plants as well as reducing crop injury. However, the lack of datasets of weeds in the field has limited the application of deep learning techniques in weed management. In this paper, it presented a dataset of weeds in fields, Weed25, which contained 14,035 images of 25 different weed species. Both monocot and dicot weed image resources were included in this dataset. Meanwhile, weed images at different growth stages were also recorded. Several common deep learning detection models-YOLOv3, YOLOv5, and Faster R-CNN-were applied for weed identification model training using this dataset. The results showed that the average accuracy of detection under the same training parameters were 91.8%, 92.4%, and 92.15% respectively. It presented that Weed25 could be a potential effective training resource for further development of in-field real-time weed identification models. The dataset is available at https://pan.baidu.com/s/1rnUoDm7IxxmX1n1LmtXNXw; the password is rn5h.