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Pixel‐level supervision resolves overlap: Benchmarking YOLOv12 segmentation for accurate multi‐cluster dry bean stand counting from time series unoccupied aerial systems imagery

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Published in:Plant Phenome Journal
Format: Online Article RSS Article
Published: 2026
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container_title Plant Phenome Journal
description
discipline_display Agronomy and Crop Science
discipline_facet Agronomy and Crop Science
format Online Article
RSS Article
genre Journal Article
id rss_article:84567
institution FRELIP
journal_source_facet Plant Phenome Journal
last_indexed 2026-06-20T21:41:14.952Z
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle Pixel‐level supervision resolves overlap: Benchmarking YOLOv12 segmentation for accurate multi‐cluster dry bean stand counting from time series unoccupied aerial systems imagery
Agronomy and Crop Science
General
Agronomy and Crop Science
sub_discipline_display General
sub_discipline_facet General
subject_display Agronomy and Crop Science
General
Agronomy and Crop Science
subject_facet Agronomy and Crop Science
General
Agronomy and Crop Science
title Pixel‐level supervision resolves overlap: Benchmarking YOLOv12 segmentation for accurate multi‐cluster dry bean stand counting from time series unoccupied aerial systems imagery
title_alt La supervisión a nivel de píxel resuelve la superposición: evaluación comparativa de YOLOv12 segmentación para el conteo preciso de múltiples grupos de frijol seco a partir de imágenes de sistemas aéreos no tripulados de series temporales
La supervision au niveau du pixel résout le chevauchement : évaluation de la segmentation YOLOv12 pour le comptage précis de plusieurs grappes de haricots secs à partir d'images de systèmes aériens sans pilote en séries temporelles
Supervisão em nível de pixel resolve sobreposição: Benchmarking da segmentação YOLOv12 para contagem precisa de múltiplos aglomerados de feijão seco a partir de imagens de séries temporais de sistemas aéreos não tripulados
title_auth Pixel‐level supervision resolves overlap: Benchmarking YOLOv12 segmentation for accurate multi‐cluster dry bean stand counting from time series unoccupied aerial systems imagery
title_es_txt La supervisión a nivel de píxel resuelve la superposición: evaluación comparativa de YOLOv12 segmentación para el conteo preciso de múltiples grupos de frijol seco a partir de imágenes de sistemas aéreos no tripulados de series temporales
title_fr_txt La supervision au niveau du pixel résout le chevauchement : évaluation de la segmentation YOLOv12 pour le comptage précis de plusieurs grappes de haricots secs à partir d'images de systèmes aériens sans pilote en séries temporelles
title_full Pixel‐level supervision resolves overlap: Benchmarking YOLOv12 segmentation for accurate multi‐cluster dry bean stand counting from time series unoccupied aerial systems imagery
title_fullStr Pixel‐level supervision resolves overlap: Benchmarking YOLOv12 segmentation for accurate multi‐cluster dry bean stand counting from time series unoccupied aerial systems imagery
title_full_unstemmed Pixel‐level supervision resolves overlap: Benchmarking YOLOv12 segmentation for accurate multi‐cluster dry bean stand counting from time series unoccupied aerial systems imagery
title_pt_txt Supervisão em nível de pixel resolve sobreposição: Benchmarking da segmentação YOLOv12 para contagem precisa de múltiplos aglomerados de feijão seco a partir de imagens de séries temporais de sistemas aéreos não tripulados
title_short Pixel‐level supervision resolves overlap: Benchmarking YOLOv12 segmentation for accurate multi‐cluster dry bean stand counting from time series unoccupied aerial systems imagery
title_sort pixel‐level supervision resolves overlap: benchmarking yolov12 segmentation for accurate multi‐cluster dry bean stand counting from time series unoccupied aerial systems imagery
topic Agronomy and Crop Science
General
Agronomy and Crop Science
url https://acsess.onlinelibrary.wiley.com/doi/10.1002/ppj2.70083?af=R