Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1169056
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dc.contributor.authorMARTINS, J. A. C.
dc.contributor.authorHIGUTI, A. Y. H.
dc.contributor.authorPELLEGRIN, A. O.
dc.contributor.authorJULIANO, R. S.
dc.contributor.authorARAUJO, A. M. de
dc.contributor.authorPELLEGRIN, L. A.
dc.contributor.authorLIESENBERG, V.
dc.contributor.authorRAMOS, A. P. M.
dc.contributor.authorGONÇALVES, W. N.
dc.contributor.authorSANT’ANA, D. A.
dc.contributor.authorPISTORI, H.
dc.contributor.authorMARCATO JUNIOR, J.
dc.date.accessioned2024-11-12T14:53:39Z-
dc.date.available2024-11-12T14:53:39Z-
dc.date.created2024-11-12
dc.date.issued2024
dc.identifier.citationAgriculture, v. 14, n. 11, p. 1-15, 2024.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1169056-
dc.descriptionAbstract: Crop segmentation, the process of identifying and delineating agricultural fields or specific crops within an image, plays a crucial role in precision agriculture, enabling farmers and public managers to make informed decisions regarding crop health, yield estimation, and resource allocation in Midwest Brazil. The crops (corn) in this region are being damaged by wild pigs and other diseases. For the quantification of corn fields, this paper applies novel computer-vision techniques and a new dataset of corn imagery composed of 1416 256 × 256 images and corresponding labels. We flew nine drone missions and classified wild pig damage in ten orthomosaics in different stages of growth using semi-automatic digitizing and deep-learning techniques. The period of crop-development analysis will range from early sprouting to the start of the drying phase. The objective of segmentation is to transform or simplify the representation of an image, making it more meaningful and easier to interpret. For the objective class, corn achieved an IoU of 77.92%, and for background 83.25%, using DeepLabV3+ architecture, 78.81% for corn, and 83.73% for background using SegFormer architecture. For the objective class, the accuracy metrics were achieved at 86.88% and for background 91.41% using DeepLabV3+, 88.14% for the objective, and 91.15% for background using SegFormer.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectDrone
dc.titleAssessment of uav-based deep learning for corn crop analysis in midwest Brazil.
dc.typeArtigo de periódico
dc.subject.thesagroAgricultura de Precisão
dc.subject.thesagroMilho
dc.subject.thesagroJavali
dc.subject.thesagroComportamento Animal
dc.subject.thesagroDano
dc.subject.thesagroFotografia
dc.subject.nalthesaurusWild boars
dc.subject.nalthesaurusAnimal behavior
dc.subject.nalthesaurusCrop damage
dc.subject.nalthesaurusPrecision agriculture
dc.description.notesOnline first.
riaa.ainfo.id1169056
riaa.ainfo.lastupdate2024-11-12
dc.identifier.doihttps://doi.org/10.3390/agriculture14112029
dc.contributor.institutionJOSÉ AUGUSTO CORREA MARTINS, UNIVERSIDADE FEDERAL DO MATO GROSSO DO SUL
dc.contributor.institutionALBERTO YOSHIRIKI HISANO HIGUTI, UNIVERSIDADE FEDERAL DO MATO GROSSO DO SULeng
dc.contributor.institutionAIESCA OLIVEIRA PELLEGRIN, CPAPeng
dc.contributor.institutionRAQUEL SOARES JULIANO, CPAPeng
dc.contributor.institutionADRIANA MELLO DE ARAUJO, CPAPeng
dc.contributor.institutionLUIZ ALBERTO PELLEGRIN, CPAPeng
dc.contributor.institutionVERALDO LIESENBERG, UNIVERSIDADE DO ESTADO DE SANTA CATARINAeng
dc.contributor.institutionANA PAULA MARQUES RAMOS, UNIVERSIDADE DO OESTE PAULISTAeng
dc.contributor.institutionWESLEY NUNES GONÇALVES, UNIVERSIDADE FEDERAL DO MATO GROSSO DO SULeng
dc.contributor.institutionDIEGO ANDRÉ SANT’ANA, INSTITUTO FEDERAL DE MATO GROSSO DO SULeng
dc.contributor.institutionHEMERSON PISTORI, UNIVERSIDADE FEDERAL DO MATO GROSSO DO SULeng
dc.contributor.institutionJOSÉ MARCATO JUNIOR, UNIVERSIDADE FEDERAL DO MATO GROSSO DO SUL.eng
Appears in Collections:Artigo em periódico indexado (CPAP)

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