A Comparative Study of Deep Learning Frameworks Applied to Coffee Plant Detection from Close-Range UAS-RGB Imagery in Costa Rica
Introducing 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,...
| Autores principales: | , , , , , |
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| Formato: | Artículo |
| Lenguaje: | Inglés |
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MDPI
2024
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| Acceso en línea: | https://repositorio.catie.ac.cr/handle/11554/12711 |
| _version_ | 1855487779807428608 |
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| author | Arriola-Valverde, Sergio Rimolo-Donadio, Renato Villagra-Mendoza, Karolina Chacón-Rodriguez, Alfonso García-Ramirez, Ronny Somarriba, Eduardo |
| author_browse | Arriola-Valverde, Sergio Chacón-Rodriguez, Alfonso García-Ramirez, Ronny Rimolo-Donadio, Renato Somarriba, Eduardo Villagra-Mendoza, Karolina |
| author_facet | Arriola-Valverde, Sergio Rimolo-Donadio, Renato Villagra-Mendoza, Karolina Chacón-Rodriguez, Alfonso García-Ramirez, Ronny Somarriba, Eduardo |
| author_sort | Arriola-Valverde, Sergio |
| collection | Repositorio CATIE |
| description | Introducing 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) |
| format | Artículo |
| id | RepoCATIE12711 |
| institution | Centro Agronómico Tropical de Investigación y Enseñanza |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | RepoCATIE127112024-12-11T23:15:51Z A Comparative Study of Deep Learning Frameworks Applied to Coffee Plant Detection from Close-Range UAS-RGB Imagery in Costa Rica Arriola-Valverde, Sergio Rimolo-Donadio, Renato Villagra-Mendoza, Karolina Chacón-Rodriguez, Alfonso García-Ramirez, Ronny Somarriba, Eduardo Coffea||Coffea||Coffea||Coffea Agricultura de precisión||precision agriculture||agriculture de précision Manejo del cultivo||crop management||gestão da colheita||conduite de la culture Imágen por satélite||satellite imagery||imagem por satélite||imagerie par satellite Costa Rica||Costa Rica||Costa Rica||Costa Rica Sede Central ODS 9 - Industria, innovación e infraestructura Introducing 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) 2024-12-10T17:06:03Z 2024-12-10T17:06:03Z 2024-11-10 Artículo https://repositorio.catie.ac.cr/handle/11554/12711 openAccess en Remote Sensing https://doi.org/10.3390/rs16244617 27 páginas application/pdf MDPI |
| spellingShingle | Coffea||Coffea||Coffea||Coffea Agricultura de precisión||precision agriculture||agriculture de précision Manejo del cultivo||crop management||gestão da colheita||conduite de la culture Imágen por satélite||satellite imagery||imagem por satélite||imagerie par satellite Costa Rica||Costa Rica||Costa Rica||Costa Rica Sede Central ODS 9 - Industria, innovación e infraestructura Arriola-Valverde, Sergio Rimolo-Donadio, Renato Villagra-Mendoza, Karolina Chacón-Rodriguez, Alfonso García-Ramirez, Ronny Somarriba, Eduardo A Comparative Study of Deep Learning Frameworks Applied to Coffee Plant Detection from Close-Range UAS-RGB Imagery in Costa Rica |
| title | A Comparative Study of Deep Learning Frameworks Applied to Coffee Plant Detection from Close-Range UAS-RGB Imagery in Costa Rica |
| title_full | A Comparative Study of Deep Learning Frameworks Applied to Coffee Plant Detection from Close-Range UAS-RGB Imagery in Costa Rica |
| title_fullStr | A Comparative Study of Deep Learning Frameworks Applied to Coffee Plant Detection from Close-Range UAS-RGB Imagery in Costa Rica |
| title_full_unstemmed | A Comparative Study of Deep Learning Frameworks Applied to Coffee Plant Detection from Close-Range UAS-RGB Imagery in Costa Rica |
| title_short | A Comparative Study of Deep Learning Frameworks Applied to Coffee Plant Detection from Close-Range UAS-RGB Imagery in Costa Rica |
| title_sort | comparative study of deep learning frameworks applied to coffee plant detection from close range uas rgb imagery in costa rica |
| topic | Coffea||Coffea||Coffea||Coffea Agricultura de precisión||precision agriculture||agriculture de précision Manejo del cultivo||crop management||gestão da colheita||conduite de la culture Imágen por satélite||satellite imagery||imagem por satélite||imagerie par satellite Costa Rica||Costa Rica||Costa Rica||Costa Rica Sede Central ODS 9 - Industria, innovación e infraestructura |
| url | https://repositorio.catie.ac.cr/handle/11554/12711 |
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