https://www.alice.cnptia.embrapa.br/alice/handle/item/19 Embrapa Agricultura Digital (CNPTIA) 2026-04-05T02:25:00Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1186025 Título: Identification of Leucaena leucocephala in urban landscapes using Street-level images and deep learning. Autoria: FURUYA, D. E. G.; MARRAFON, G.; LEMOS, E. L. de; FURUYA, M. T. G.; GONÇALVES, R. D. S.; GONÇALVES, W. N.; MARCATO JUNIOR, J.; BOLFE, E. L.; LIESENBERG, V.; OSCO, L. P.; RAMOS, A. P. M. Conteúdo: Mapping urban tree species supports green infrastructure planning. An essential issue refers to the monitoring of exotic species that may become invasive. Street-level imagery provides a complementary perspective to aerial images for species identification that are difficult to distinguish from above. In this context, our study aimed to evaluate deep learning-based object detection and image segmentation approaches to identify a potentially invasive tree species known as Leucaena leucocephala in an urban environment in Brazil, using 422 street-level images acquired from Google Street View (SV) and mobile phones (MPs). Object detection models (YOLOv8 and DETR) and a foundation segmentation model (SAM, zero-shot) were applied to assess how deep learning paradigms perform under heterogeneous urban imaging conditions. YOLOv8 achieved detection performance with mAP50 above 0.83 and recall up to 0.76. DETR showed domain sensitivity, with mAP50 of 0.45 in SV images and 0.84 in MP imagery. For segmentation, SAM zero-shot achieved 0.92 accuracy and 0.93 F1-score in SV images, decreasing to 0.63 accuracy and 0.66 F1-score in MP images. Overall, this study demonstrates that combining detection and segmentation approaches provides complementary information for urban vegetation monitoring, supporting decision-making related to invasive species management and sustainable urban landscape planning. 2026-01-01T00:00:00Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1186019 Título: Produtividade em cana-de-açucar em variedades convencionais e tipo cana energia sob diferentes doses de potassio. Autoria: SILVA, F. C. da; RAIZER, A. J.; CARVALHO, M. L.; DIAS, V. G.; CRISTOFOLETI, D.; CASTRO, A. de Conteúdo: A cana-de-açúcar é cultura de destaque no Brasil, pela importância socioeconômica e como fonte bioenergética, ocupando área de colheita de 8,5 milhões de hectares e produção de 655 milhões de toneladas, em 2021. O potássio se destaca dentre os nutrientes utilizados na cultura, sendo o nutriente exportado em maior quantidade, além de influenciar na sua qualidade. O objetivo do estudo foi avaliar, em vários ciclos, doses crescentes de potássio aplicadas na cana-de-açúcar, bem como o comportamento de variedades convencionais e de cana energia. 2023-01-01T00:00:00Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1185961 Título: Geo-referenced factor-graph SLAM for orchard-scale 3D apple reconstruction and yield estimation. Autoria: BHARTI, D.; FARIA, L. N. de; KOENIGKAN, L. V.; GEBLER, L.; RUFATO, A. de R.; SANTOS, T. T. Conteúdo: Accurate and spatially resolved yield estimation is a critical requirement for precision agriculture and orchard management. This paper presents a geometrically consistent, orchard-scale apple yield estimation framework that integrates GNSS–visual-inertial odometry (VIO) fusion, deep learning-based object detection, multi-frame tracking, three-dimensional triangulation, and incremental factor-graph optimization. Camera poses are obtained using ZED GNSS–VIO fusion and subsequently refined using an iSAM2-based nonlinear smoothing approach that incorporates strong relative-motion constraints and soft global ENU (East-North-Up) translation priors. Apples are detected using a YOLO-based model and associated across frames via CoTracker3, enabling robust multi-view landmark reconstruction. Reprojection factors and landmark priors are incorporated into a unified nonlinear factor graph to jointly optimize camera trajectories and 3D apple positions. The reconstructed apples are spatially aggregated into a grid-based mass map, where individual fruit volumes are estimated assuming spherical geometry and converted to mass using density models. The resulting ENU-referenced yield plot provides a structured representation of orchard production variability. Experimental results demonstrate significant reductions in reprojection error after optimization and improved global consistency of the trajectory, leading to stable and spatially coherent 3D reconstructions. The proposed pipeline bridges perception, geometry, and optimization, providing a scalable solution for orchard-scale yield mapping and decision support in precision agriculture. 2026-01-01T00:00:00Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1185938 Título: UAV-based automation: a case study of coffee crop input application in Caconde, Brazil. Autoria: SILVA, T. L. da; ROMANI, L. A. S.; MARTHA JUNIOR, G. B.; GREGO, C. R.; LUCHIARI JUNIOR, A.; MASSRUHÁ, S. M. F. S. Conteúdo: Precision agriculture optimizes agricultural practices by applying inputs at the right time and place, enhancing productivity and sustainability. This study compared the costs, time requirements, and water efficiency of using an unmanned aerial vehicle (UAV) versus the traditional manual backpack sprayer method on a 5-hectare coffee farm in Caconde, São Paulo, Brazil. Results showed that UAV application reduced operational costs by 21.8% (R$750 vs. R$960), water consumption by 96% (50 liters vs. 1,250 liters), and application time by 75% (two hours vs. eight hours). Sensitivity analysis confirmed the cost-effectiveness of UAV method under varying labor costs. This study underscores the economic and environmental advantages of UAV spraying and highlights its potential to address labor shortages and promote sustainable precision farming. 2025-01-01T00:00:00Z