DSpace Collection:https://repositorio.ufu.br/handle/123456789/180532026-08-24T06:31:45Z2026-08-24T06:31:45ZValidação da plantabilidade em soja com uso de visão computacionalhttps://repositorio.ufu.br/handle/123456789/495922026-08-18T06:21:54Z2026-03-07T00:00:00ZTitle: Validação da plantabilidade em soja com uso de visão computacional
Abstract: This study presents a workflow for assessing soybean planting quality from point-based
imagery acquired by a remotely piloted aircraft, integrating computer vision, spatial analysis,
and geostatistics. The study was conducted at Mandaguari Farm (Indianópolis, Minas Gerais,
Brazil), center pivot 3-1 (Field 1), covering 14.13 ha. Data acquisition, performed on October
30, 2025, followed two stages: (i) a flight at approximately 120 m AGL to generate an
orthomosaic and digital elevation and terrain models; and (ii) a low-altitude point-based flight at
approximately 4 m AGL, with one image per sampling point at the VC growth stage. Point
images were processed with a YOLOv8m instance-segmentation model trained on 640 × 640-
pixel tiles using a point-level dataset split. Validation reached mAP50(box)=0.963,
mAP50(mask)=0.960, mAP50–95(box)=0.782, mAP50–95(mask)=0.715,
precision(box)=0.918, and recall(box)=0.938. Segmented masks were converted to centroids in
QGIS, and a Python script estimated the dominant planting direction, grouped crop rows, and
calculated within-row plant spacing, classifying skips and doubles relative to the expected
spacing within each image. As a quality-control criterion for centroid linking, points with
drop_pct ≥ 25% were treated as severe linking failures and removed, yielding n=39 for
descriptive and spatial analyses. After filtering, median values were %CV=65.20%,
%doubles=8.93%, and %skips=18.68%. Ordinary kriging was used to generate thematic maps
and variograms; however, Moran's I did not indicate significant spatial autocorrelation at the
5% level, and cross-validation yielded low R² values, indicating weak spatial structure and
limited predictive performance under the evaluated conditions. The workflow was reproducible
and suitable for quantifying point-level planting-quality indicators, whereas stronger spatial
products will require higher sampling density and/or additional covariates.2026-03-07T00:00:00ZProdutividade e qualidade de tomate cocktail em função da maturação dos cachoshttps://repositorio.ufu.br/handle/123456789/495052026-08-14T06:19:11Z2026-05-12T00:00:00ZTitle: Produtividade e qualidade de tomate cocktail em função da maturação dos cachos2026-05-12T00:00:00ZBiofertilizante líquido na produtividade de alface tipo americanahttps://repositorio.ufu.br/handle/123456789/492982026-08-06T06:19:51Z2026-03-05T00:00:00ZTitle: Biofertilizante líquido na produtividade de alface tipo americana2026-03-05T00:00:00ZEfeito temporal do cultivo de café (Coffea arabica L.) no estoque de carbono e atividade biológica do solohttps://repositorio.ufu.br/handle/123456789/492912026-08-05T06:21:01Z2026-03-23T00:00:00ZTitle: Efeito temporal do cultivo de café (Coffea arabica L.) no estoque de carbono e atividade biológica do solo2026-03-23T00:00:00Z