Mapping gaps in sugarcane fields in unmanned aerial vehicle imagery using YOLOv5 and ImageJ.

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dc.contributorINACIO HENRIQUE YANO, CNPTIA, CENTRO ESTADUAL DE EDUCAÇÃO TECNOLÓGICA PAULA SOUZA, FACULDADE DE TECNOLOGIA DE SANTANA DO PARNAÍBA; JOÃO PEDRO NASCIMENTO DE LIMA; EDUARDO ANTONIO SPERANZA, CNPTIA; FABIO CESAR DA SILVA, CNPTIA.
dc.creatorYANO, I. H.
dc.creatorLIMA, J. P. N. de
dc.creatorSPERANZA, E. A.
dc.creatorSILVA, F. C. da
dc.date2024-08-26T11:53:31Z
dc.date2024-08-26T11:53:31Z
dc.date2024-08-26
dc.date2024
dc.date.accessioned2026-07-07T04:17:48Z
dc.descriptionAbstract: Sugarcane plays a pivotal role in the Brazilian economy as a primary crop. This semiperennial crop allows for multiple harvests throughout its life cycle. Given its longevity, farmers need to be mindful of avoiding gaps in sugarcane fields, as these interruptions in planting lines negatively impact overall crop productivity over the years. Recognizing and mapping planting failures becomes essential for replanting operations and productivity estimation. Due to the scale of sugarcane cultivation, manual identification and mapping prove impractical. Consequently, solutions utilizing drone imagery and computer vision have been developed to cover extensive areas, showing satisfactory effectiveness in identifying gaps. However, recognizing small gaps poses significant challenges, often rendering them unidentifiable. This study addresses this issue by identifying and mapping gaps of any size while allowing users to determine the gap size. Preliminary tests using YOLOv5 and ImageJ 1.53k demonstrated a high success rate, with a 96.1% accuracy in identifying gaps of 50 cm or larger. These results are favorable, especially when compared to previously published works.
dc.identifierApplied Sciences, v. 14, n. 17, 7454, Sept. 2024.
dc.identifier2076-3417
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1166767
dc.identifierhttps://doi.org/10.3390/app14177454
dc.identifier.urihttp://hdl.handle.net/123456789/456827
dc.languageeng
dc.rightsopenAccess
dc.subjectFalha no plantio
dc.subjectEstimativa de produtividade
dc.subjectImagens de veículo aéreo não tripulado
dc.subjectImagens de drones
dc.subjectVisão computacional
dc.subjectPlanting failure
dc.subjectProductivity estimation
dc.subjectSemi-perennial
dc.subjectDrone imagery
dc.subjectCana de Açúcar
dc.subjectSugarcane
dc.subjectUnmanned aerial vehicles
dc.subjectComputer vision
dc.titleMapping gaps in sugarcane fields in unmanned aerial vehicle imagery using YOLOv5 and ImageJ.
dc.typeArtigo de periódico

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Embrapa Agricultura Digital (CNPTIA)