https://www.alice.cnptia.embrapa.br/alice/handle/item/197 Artigo em anais de congresso (CNPTIA) Fri, 10 Jul 2026 20:30:50 GMT 2026-07-10T20:30:50Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1187715 Título: Environmental challenges of pastoral farming systems in tropical areas. Autoria: CARVALHO, P. C. F.; BARIONI, L. G.; FREUA, M. C.; BOVAL, M. Conteúdo: The need to increase food production has become urgent. Pastoral farming systems based on grasslands in the tropics are essential players in this scenario, considering the surface area and stakeholders they represent. Improving productivity from existing grasslands can be a way forward to produce food, because most of them still produce less than the potential primary and secondary production they could achieve if constraints to pasture and animal growth were surpassed using existing technologies. This potential production could be reached without increasing the surface area used. However, the technologies available to support this intensification process are generally based on an input approach, and are associated with increased use of natural resources and pollution. This classical anthropogenic effect has already been experienced in the temperate grasslands of developed countries, and has raised environmental concerns there. Pastoral farming systems in the tropics seemed to be following the same trend, but are currently being called upon to increase production without such side effects. Dealing with these new environmental drivers and unraveling the production vs. conservation dilemma requires pastoral farming to take a new process-oriented approach. Grassland science is responding to this environmental constraint, and is being asked to build innovative systems devoted to sustainable intensification, at a time when urgency contrasts with a seeming lack of creativity and innovation. Here we explore these issues, focusing on Brazilian pastoral farming trends. This case study is of worldwide interest because of its major place in the global market, and its impact on food security and natural resource conservation in Brazil and elsewhere. Tue, 01 Jan 2013 00:00:00 GMT https://www.alice.cnptia.embrapa.br/alice/handle/doc/1187715 2013-01-01T00:00:00Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1187153 Título: Cattle weight estimation from dense point clouds. Autoria: CASTANHEIRO, L. F.; TETILA, E. C.; FURUYA, D. E. G.; SILVA, J. P. da; BARBEDO, J. G. A.; ROMANI, L. A. S.; BOLFE, E. L. Conteúdo: 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. Individual animals were automatically segmented from the reconstructed 3D scene, and voxel-based volumetric features were extracted for each animal. Body weight was then estimated through linear regression models calibrated with ground truth measurements obtained from individual weighing. The proposed approach was evaluated on Nellore cattle in a feedlot environment and achieved a root mean square error (RMSE) of 8.35 kg, corresponding to an average relative error of approximately 2.29%. The results highlight the potential of UAV-based photogrammetry as a cost-effective decision support tool for digital and sustainable livestock management. Thu, 01 Jan 2026 00:00:00 GMT https://www.alice.cnptia.embrapa.br/alice/handle/doc/1187153 2026-01-01T00:00:00Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1185950 Título: SEEmear: a system for large scale geo-referenced stereo imaging of orchards. Autoria: SANTOS, T. T.; KOENIGKAN, L. V.; GEBLER, L. Conteúdo: Precision agriculture applications at the plant level require high-resolution, detailed imaging that cannot be adequately provided by satellite-based remote sensing due to insufficient resolution and suboptimal viewing angles. While Unmanned Aerial Vehicles (UAVs) offer improved capabilities, their downward-facing perspective and navigation challenges in orchards limit their effectiveness for fruit monitoring and anomalies detection. To address these limitations, we present SEEmear, a novel ground-based proximal sensing system for large-scale geo-referenced stereo imaging of orchard environments. The system integrates high-performance embedded computing, wide-angle global shutter RGB-D cameras, and precision RTK GNSS positioning, enabling simultaneous imaging of both sides of orchard rows from close proximity. SEEmear's 110° field-of-view cameras capture entire tree structures even at distances of 1 meter, while global shutter sensors eliminate motion artifacts essential for moving platforms. We tested the system in apple orchards, collecting comprehensive geo-referenced RGB-D imagery across 1.50 ha in approximately 40 minutes per session. The resulting dataset supports advanced applications including depth estimation, 3D reconstruction, background filtering, and object segmentation. The adaptable platform integrates with various ground vehicles and provides substantial data storage and processing capabilities needed for hardware-accelerated AI algorithms. SEEmear addresses the critical need for high-quality proximal sensing data in precision agriculture research, supporting applications in fruit detection, tracking, yield mapping, autonomous navigation, and field robotics. Wed, 01 Jan 2025 00:00:00 GMT https://www.alice.cnptia.embrapa.br/alice/handle/doc/1185950 2025-01-01T00:00:00Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1185946 Título: Multistep pasture vigor classification at the local scale: a comparative analysis in Guia Lopes da Laguna, MS. Autoria: CASTRO, V. H. M. e de C.; KLINKE NETO, G.; PARREIRAS, T. C.; BOLFE, E. L.; BERGIER, I. Conteúdo: Accurate pasture mapping is essential for sustainable livestock management in Brazil, where degradation affects extensive areas. Tools like the Atlas das Pastagens provide large-scale data on pasture quality, but local-scale accuracy remains underexplored. This study assessed the accuracy of pasture vigor classification in the 2023 Atlas using 60 groundtruth points collected in Guia Lopes da Laguna, Mato Grosso do Sul, in June 2024. Field data were classified into High, Medium, or Low vigor and then grouped into a binary scheme: “non-degraded” (high vigor) and “degraded” (medium + low Vigor). A confusion matrix comparing reference (field) and estimated (Atlas) data revealed an overall accuracy of 73.3%. The method in Atlas performed better at detecting degraded areas (recall = 80.0%, precision = 70.6%, F1-score = 75.0%) than non-degraded areas (recall = 34.8%, precision = 47.1%, F1-score = 40.0%). While the binary approach improves overall accuracy, the Atlas still shows significant limitations in correctly identifying non-degraded (High Vigor) pastures at the municipal level, often underestimating their presence. Therefore, the Atlas is a useful tool for detecting degradation signs but should be used with caution in applications that require high local precision in distinguishing productive pasture areas. Wed, 01 Jan 2025 00:00:00 GMT https://www.alice.cnptia.embrapa.br/alice/handle/doc/1185946 2025-01-01T00:00:00Z