https://www.alice.cnptia.embrapa.br/alice/handle/item/197 Artigo em anais de congresso (CNPTIA) 2026-07-27T13:37:15Z 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. 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. 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 https://www.alice.cnptia.embrapa.br/alice/handle/doc/1185942 Título: Semear MAppleT FW: a dataset for apple detection and tracking in orchards under fruiting wall training system. Autoria: SANTOS, T. T.; FARIA, L. N. de; GEBLER, L. Conteúdo: Computer vision techniques for fruit detection and tracking are crucial for agricultural automation, yet most current datasets lack temporally consistent annotations needed for reliable tracking. Here we present Semear MAppleT FW, a dataset for apple detection and tracking in modern fruiting wall systems. The dataset comprises six video sequences of 100 frames each, captured by two RGB-D stereo cameras mounted on a tractor traversing orchard rows. Unlike previous datasets, Semear MAppleT FW features wide-angle images capturing entire tree lengths, ensuring complete canopy visibility within the field of view. To date, we provide over 53,000 bounding box annotations for 1,267 unique apple instances with temporal consistency across frames, stereo image pairs with known baseline calibration, and 3D reconstruction data. Our annotation method leverages structure-from-motion to estimate fruit positions in 3D space, enabling accurate tracking even when fruits are occluded by branches, leaves, or other fruits. The dataset includes visibility flags for each annotation, distinguishing between visible and occluded fruits. This approach maintains spatial consistency of annotations across frames while significantly reducing manual annotation workload. Semear MAppleT FW provides a valuable resource for developing artificial intelligence systems for automated yield estimation, fruit growth monitoring, and robotic harvesting in commercial orchards. 2025-01-01T00:00:00Z