Abstract
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.
Highlights
Weeds are unwanted plants that grow in the field and compete with the crops for water, light, nutrients, and space
The training process of Faster RCNN might appear to oscillate more than Single Shot Detector (SSD), which could be due to the different batch sizes and optimizers being used by the two
The different batc1h2 osfi2z5e and optimizer could be the reason for the Faster RCNN model converging to high validation mean Average Precision (mAP) earlier than SSD, since a batch size of 1 for Faster RCNN leads to 24 times more gradient updates than SSD with a batch size of 24
Summary
Weeds are unwanted plants that grow in the field and compete with the crops for water, light, nutrients, and space. The spatial density of weeds is not uniform across the field, thereby leading to overuse of chemicals which results in environmental concerns and evolution of herbicide-resistant weeds. To overcome this issue, a concept of site-specific weed management (SSWM), which refers to detecting weed patches and spot spraying or removal by mechanical means, was proposed in the early 1990s [3,4,5]. Prescription maps for post-emergence application can be created from late-season weeds detected during the previous seasons [13,14,15,16,17]. The detection and control of late-season weeds can be complementary to early season weed control
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