RT info:eu-repo/semantics/article
T1 Development of a detection system for types of weeds in maize (Zea mays L.) under greenhouse conditions using the YOLOv5 v7.0 model
A1 García Navarrete, Óscar Leonardo
A1 Santamaría Becerril, Óscar
A1 Martín Ramos, Pablo
A1 Valenzuela Mahecha, Miguel Ángel
A1 Navas Gracia, Luis Manuel
K1 Machine learning
K1 Aprendizaje automático
K1 Precision agriculture
K1 Agricultura de precisión
K1 Sustainable agriculture
K1 Agricultura sostenible
K1 Agricultural innovations
K1 Agricultura - Innovaciones tecnológicas
K1 Neural networks (Computer science)
K1 Redes neuronales (Informática)
K1 Computer vision
K1 Visión artificial (Robótica)
K1 Agriculture
K1 Agricultural engineering
K1 3102 Ingeniería Agrícola
K1 1203.04 Inteligencia Artificial
K1 1203.17 Informática
AB 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.
PB MDPI
SN 2077-0472
YR 2024
FD 2024
LK https://uvadoc.uva.es/handle/10324/68064
UL https://uvadoc.uva.es/handle/10324/68064
LA eng
NO Agriculture, 2024, Vol. 14, Nº. 2, 286
NO Producción Científica
DS UVaDOC
RD 28-sep-2024