https://www.alice.cnptia.embrapa.br/alice/handle/item/197 Artigo em anais de congresso (CNPTIA) 2026-04-05T09:19:37Z 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 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. 2025-01-01T00:00:00Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1185941 Título: Prospective analysis of the adoption of digital technologies in agriculture. Autoria: DIBBERN, T.; ROMANI, L. A. S.; EVANGELISTA, S. R. M.; MARTINS, V. A.; MASSRUHÁ, S. M. F. S. Conteúdo: Open innovation emerges when knowledge, experience, and capabilities are distributed across various organizations, enabling innovative activities inside and outside research institutions within a collaborative system. In the agricultural sector, this phenomenon is manifested through innovation ecosystems that involve multiple stakeholders, including universities, governments, research institutes, companies, cooperatives, and financial markets. However, one of the main challenges is measuring an innovation's success, especially in the early stages of developing technology-based products. Given this context, this paper proposes a methodological model for developing indicators that measure the level of success in adopting digital technology in the field, from the initial stages of technological product development. Five stages and the results obtained in the benchmarking phase are presented. 2025-01-01T00:00:00Z