Use of open-source object detection algorithms to detect Palmer amaranth (Amaranthus palmeri) in soybean

Abstract

AbstractSite-specific weed management using open-source object detection algorithms could accurately detect weeds in cropping systems. We investigated the use of object detection algorithms to detect Palmer amaranth (Amaranthus palmeri S. Watson) in soybean [Glycine max (L.) Merr.]. The objectives were to (1) develop an annotated image database of A. palmeri and soybean to fine-tune object detection algorithms, (2) compare effectiveness of multiple open-source algorithms in detecting A. palmeri, and (3) evaluate the relationship between A. palmeri growth features and A. palmeri detection ability. Soybean field sites were established in Manhattan, KS, and Gypsum, KS, with natural populations of A. palmeri. A total of 1,108 and 392 images were taken aerially and at ground level, respectively, between May 27 and July 27, 2021. After image annotation, a total of 4,492 images were selected. Annotated images were used to fine-tune open-source faster regional convolutional (Faster R-CNN) and single-shot detector (SSD) algorithms using a Resnet backbone, as well as the “You Only Look Once” (YOLO) series algorithms. Results demonstrated that YOLO v. 5 achieved the highest mean average precision score of 0.77. For both A. palmeri and soybean detections within this algorithm, the highest F1 score was 0.72 when using a confidence threshold of 0.298. A lower confidence threshold of 0.15 increased the likelihood of species detection, but also increased the likelihood of false-positive detections. The trained YOLOv5 data set was used to identify A. palmeri in a data set paired with measured growth features. Linear regression models predicted that as A. palmeri densities increased and as A. palmeri height increased, precision, recall, and F1 scores of algorithms would decrease. We conclude that open-source algorithms such as YOLOv5 show great potential in detecting A. palmeri in soybean-cropping systems.