A Comparative Study of Deep Learning Frameworks Applied to Coffee Plant Detection from Close-Range UAS-RGB Imagery in Costa Rica

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dc.creatorArriola-Valverde, Sergio
dc.creatorRimolo-Donadio, Renato
dc.creatorVillagra-Mendoza, Karolina
dc.creatorChacón-Rodriguez, Alfonso
dc.creatorGarcía-Ramirez, Ronny
dc.creatorSomarriba, Eduardo
dc.date2024-12-10T17:06:03Z
dc.date2024-12-10T17:06:03Z
dc.date2024-11-10
dc.date.accessioned2026-06-09T02:02:44Z
dc.descriptionIntroducing artificial intelligence techniques in agriculture offers new opportunities for improving crop management, such as in coffee plantations, which constitute a complex agroforestry environment. This paper presents a comparative study of three deep learning frameworks: Deep Forest, RT-DETR, and Yolov9, customized for coffee plant detection and trained from images with a high spatial resolution (cm/pix). Each frame had dimensions of 640 × 640 pixels acquired from passive RGB sensors onboard a UAS (Unmanned Aerial Systems) system. The image set was structured and consolidated from UAS-RGB imagery acquisition in six locations along the Central Valley, Costa Rica, through automated photogrammetric missions. It was evidenced that the RTDETR and Yolov9 frameworks allowed adequate generalization and detection with mAP50 values higher than 90% and mAP5095 higher than 54%, in scenarios of application with data augmentation techniques. Deep Forest also achieved good metrics, but noticeably lower when compared to the other frameworks. RT-DETR and Yolov9 were able to generalize and detect coffee plants in unseen scenarios that include complex forest structures within tropical agroforestry Systems (AFS)
dc.format27 páginas
dc.formatapplication/pdf
dc.identifierhttps://repositorio.catie.ac.cr/handle/11554/12711
dc.identifieropenAccess
dc.identifier.urihttp://hdl.handle.net/123456789/1747
dc.languageen
dc.publisherMDPI
dc.relationRemote Sensing
dc.relationhttps://doi.org/10.3390/rs16244617
dc.subjectCoffea||Coffea||Coffea||Coffea
dc.subjectAgricultura de precisión||precision agriculture||agriculture de précision
dc.subjectManejo del cultivo||crop management||gestão da colheita||conduite de la culture
dc.subjectImágen por satélite||satellite imagery||imagem por satélite||imagerie par satellite
dc.subjectCosta Rica||Costa Rica||Costa Rica||Costa Rica
dc.subjectSede Central
dc.subjectODS 9 - Industria, innovación e infraestructura
dc.titleA Comparative Study of Deep Learning Frameworks Applied to Coffee Plant Detection from Close-Range UAS-RGB Imagery in Costa Rica
dc.typeArtículo

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