https://www.alice.cnptia.embrapa.br/alice/handle/item/198 Resumo em anais de congresso (CNPTIA) 2026-06-02T03:15:49Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1187157 Título: Cattle weight estimation from dense point clouds. Autoria: CASTANHEIRO, L.; TETILA, E.; FURUYA, D.; SILVA, J. P. da; BARBEDO, J. G. A.; ROMANI, L. A. S.; BOLFE, E. L. Conteúdo: Abstract: Cattle weight is essential for decision-making in precision livestock farming, directly supporting nutrition management, animal welfare, and production efficiency. Existing methods rely on close-range measurements or manual intervention, limiting scalability. This work proposes an workflow for cattle weight estimation based on point clouds derived from aerial images. RGB images acquired at low altitude were processed using Structure from Motion (SfM) techniques to generate dense point clouds. 2026-01-01T00:00:00Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1186344 Título: Center for science for development of digital agriculture: Semear Digital, Brazil. Autoria: BOLFE, E. L.; ROMANI, L. A. S.; BARBEDO, J. G. A.; MASSRUHÁ, S. M. F. S. Conteúdo: Projections of world population show that there will be a greater demand for food, fiber and energy, requiring an increase in agricultural productivity, cost reduction and sustainable use of natural resources. Thus, it is essential to consider the process of digital transformation in the countryside, with the introduction of equipment and sensors to collect and generate data in amounts that easily surpass the human processing capacity. In this context, “Semear Digital” is a Brazilian research center designed to enhance agricultural productivity and sustainability through digital technologies and connectivity solutions. The center prioritizes research in inclusive digital technologies in six axis, integrating small and medium scale producers, and operates under the coordination of the Brazilian Agricultural Research Corporation (Embrapa), funded by the São Paulo Research Foundation (FAPESP), and with a collaborative consortium of Research, Development, and Innovation (RD&I) institutions. 2026-01-01T00:00:00Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1186341 Título: From satellites to smartphones: opportunities for digital agriculture in apple orchards. Autoria: FURUYA, D. E. G.; BOLFE, E. L.; PARREIRAS, T. C. Conteúdo: Mobile phones images of Fuji and Gala apples are being used to evaluate the performance of neural networks for fruit detection, which can serve as the basis for developing future mobile applications not only for fruit detection but also for the assessment of different characteristics and parameters, such as size, color, or quality. Such applications may support farmers and technicians by offering practical and low-cost tools for monitoring orchard conditions. 2026-01-01T00:00:00Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1186342 Título: Advancing coffee management mapping through multisensor data and multistep ensemble learning. Autoria: PARREIRAS, T. C.; BOLFE, E. L.; FURUYA, D. E. G. Conteúdo: Despite the advances, accurately identifying recently renovated and skeletonized coffee areas remains a challenge, as their altered canopy structure and reduced vigor produce spectral signatures similar to those of fallow or non-coffee areas. To address these limitations, upcoming research will focus on leveraging a space-time hybrid approach with deep learning and surface phenology modeling. Specifically, we plan to implement a workflow combining the spatial detail of Sentinel-2 with the temporal continuity of HLS. 2026-01-01T00:00:00Z