Dynamic weed control using selective laser application with object tracking and target scheduling

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dc.article.number111004
dc.catalogadorjca
dc.contributor.authorVargas Fernández, Juan Ignacio
dc.contributor.authorWane, Sam
dc.contributor.authorArévalo Ramírez, Tito
dc.contributor.authorAuat Cheein, Fernando
dc.date.accessioned2025-10-01T18:56:55Z
dc.date.available2025-10-01T18:56:55Z
dc.date.issued2025
dc.description.abstractSelective laser application for weed control is emerging as one of the most sustainable practices for various crops. The system targets weeds using a laser beam with specific time and intensity settings to eliminate undesired plants through thermal damage. However, this process — commonly known as static weed laser treatment — reduces machinery efficiency, as the platform must remain stationary until all visible weeds are treated. To address this limitation, the current work proposes a dynamic laser weeding approach that predicts weed positions while the platform is in motion, thereby improving operational efficiency. Several deep learning architectures (e.g., YOLO series for weed detection and DeepSORT for weed tracking) are evaluated to identify the most effective models for detecting and tracking multiple weeds in RGB images. In addition, a time-constrained scheduling strategy is implemented to determine the order in which weeds are treated, minimizing the number of missed targets. We find that receding horizon control offers the best performance, particularly under strict time and energy constraints. Field deployment results show that YOLOv7 achieves the highest precision, recall, and mean Average Precision (mAP) for weed detection. The dynamic laser weeding system significantly outperforms the static counterpart, enabling up to 2.8 times faster movement while successfully treating 90% of detected weeds. This work presents a proof of concept for dynamic weeding, laying the foundation for future developments in intelligent, autonomous crop protection systems.
dc.fechaingreso.objetodigital2025-09-30
dc.format.extent9 páginas
dc.fuente.origenORCID
dc.identifier.doi10.1016/j.compag.2025.111004
dc.identifier.issn0168-1699
dc.identifier.urihttps://doi.org/10.1016/j.compag.2025.111004
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/105859
dc.information.autorucEscuela de Ingeniería;Vargas Fernández, Juan Ignacio;0009-0002-2906-6620;1086977
dc.information.autorucEscuela de Ingeniería; Arevalo Ramirez, Tito Andre; 0000-0003-2542-6545; 1300544
dc.language.isoen
dc.nota.accesocontenido completo
dc.pagina.final9
dc.pagina.inicio1
dc.revistaComputers and Electronics in Agriculture
dc.rightsacceso abierto
dc.rights.licenseAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectWeed detection
dc.subjectMachine learning
dc.subjectHyperweed selection
dc.subjectLaser
dc.subject.ddc550
dc.subject.deweyCiencias de la tierraes_ES
dc.subject.ods15 Life on land
dc.subject.odspa15 Vida de ecosistemas terrestres
dc.titleDynamic weed control using selective laser application with object tracking and target scheduling
dc.typeartículo
dc.volumen239
sipa.codpersvinculados1086977
sipa.codpersvinculados1300544
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