https://repositorio.ufu.br/handle/123456789/18053 Mon, 24 Aug 2026 06:31:45 GMT 2026-08-24T06:31:45Z https://repositorio.ufu.br/handle/123456789/49592 Title: 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. Sat, 07 Mar 2026 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/49592 2026-03-07T00:00:00Z https://repositorio.ufu.br/handle/123456789/49505 Title: Produtividade e qualidade de tomate cocktail em função da maturação dos cachos Tue, 12 May 2026 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/49505 2026-05-12T00:00:00Z https://repositorio.ufu.br/handle/123456789/49298 Title: Biofertilizante líquido na produtividade de alface tipo americana Thu, 05 Mar 2026 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/49298 2026-03-05T00:00:00Z https://repositorio.ufu.br/handle/123456789/49291 Title: Efeito temporal do cultivo de café (Coffea arabica L.) no estoque de carbono e atividade biológica do solo Mon, 23 Mar 2026 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/49291 2026-03-23T00:00:00Z