Development of a detection system for types of weeds in maize (Zea mays L.) under greenhouse conditions using the YOLOv5 v7.0 model
2024
García Navarrete, Óscar Leonardo | Santamaría Becerril, Óscar | Martín Ramos, Pablo | Valenzuela Mahecha, Miguel Ángel | Navas Gracia, Luis Manuel
Producción Científica
显示更多 [+] 显示较少 [-]Corn (Zea mays L.) is one of the most important cereals worldwide. To maintain crop productivity, it is important to eliminate weeds that compete for nutrients and other resources. The eradication of these causes environmental problems through the use of agrochemicals. The implementation of technology to mitigate this impact is also a challenge. In this work, an artificial vision system was implemented based on the YOLOv5s (You Only Look Once) model, which uses a single convolutional neural network (CNN) that allows differentiating corn from four types of weeds, for which a mobile support structure was built to capture images. The performance of the trained model had a value of mAP@05 (mean Average Precision) at a threshold of 0.5 of 83.6%. A prediction accuracy of 97% and a mAP@05 of 97.5% were obtained for the maize class. For the weed classes, Lolium perenne, Sonchus oleraceus, Solanum nigrum, and Poa annua obtained an accuracy of 86%, 90%, 78%, and 74%, and a mAP@05 of 81.5%, 90.2%, 76.6% and 72.0%, respectively. The results are encouraging for the construction of a precision weeding system.
显示更多 [+] 显示较少 [-]Union Europea, European Union’s Horizon 2020 - (grant HORIZON-CL6-2022-FARM2FORK-01)
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