Development of a Detection System for Types of Weeds in Maize ( Zea mays L.) under Greenhouse Conditions Using the YOLOv5 v7.0 Model

Oscar Leonardo García-Navarrete, Oscar Santamaria, Pablo Martín-Ramos, Miguel Ángel Valenzuela-Mahecha and Luis Manuel Navas-Gracia ()
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Oscar Leonardo García-Navarrete: TADRUS Research Group, Department of Agricultural and Forestry Engineering, Universidad de Valladolid, 34004 Palencia, Spain
Oscar Santamaria: Department of Crop Science and Forestry Resources, Universidad de Valladolid, 34004 Palencia, Spain
Pablo Martín-Ramos: TADRUS Research Group, Department of Agricultural and Forestry Engineering, Universidad de Valladolid, 34004 Palencia, Spain
Miguel Ángel Valenzuela-Mahecha: Department of Civil and Agricultural Engineering, Universidad Nacional de Colombia, Bogotá 111321, Colombia
Luis Manuel Navas-Gracia: TADRUS Research Group, Department of Agricultural and Forestry Engineering, Universidad de Valladolid, 34004 Palencia, Spain

Agriculture, 2024, vol. 14, issue 2, 1-13

Abstract: 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.

Keywords: deep-learning; precision agriculture; convolutional neural network (CNN); computer vision; precision weeding (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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