Early and on-ground image-based detection of poppy (Papaver rhoeas) in wheat using YOLO architectures

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Abstract
Poppy (also common poppy or corn poppy; Papaver rhoeas L., PAPRH) is one of the most harmful weeds in winter cereals. Knowing the precise and accurate location of weeds is essential for developing effective site-specific weed management (SSWM) for optimized herbicide use. Among the available tools for weed mapping, deep learning (DL) is used for its accuracy and ability to work in complex scenarios. Crops represent intricate situations for weed detection, as crop residues, occlusion of weeds, or spectral similarities between crop and weed seedlings are frequent. Timely discrimination of weeds is needed, because postemergence herbicides are used just when weeds and crops are at an early growth stage. This study addressed P. rhoeas early detection in wheat (Triticum spp.) by comparing the performance of six DL-based object-detection models focused on the “You Only Look Once” (YOLO) architecture (v3 to v5) using proximal RGB images to train the models. The models were assessed using open-source software, and evaluation offered a range of results for quality of recognition of P. rhoeas as well as computational capacity during the inference process. Of all the models, YOLOv5s performed best in the testing phase (75.3%, 76.2%, and 77% for F1-score, mean average precision, and accuracy, respectively). These results indicated that under real field conditions, DL-based object-detection strategies can identify P. rhoeas at an early stage, providing accurate information for developing SSWM.

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