Proceedings
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| Filter results8 paper(s) found. |
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1. Field-Based Evaluation of Targeted Herbicide Spraying Efficacy: A Comparison of Qualitative and Quantitative ApproachesWeed control remains one of the main challenges for maintaining agricultural productivity. In this context, selective spraying based on optical sensors and embedded vision systems emerges as a promising alternative for localized weed management, aligned with the principles of precision agriculture and sustainability. However, the adoption of these technologies on a commercial scale demands robust methods to evaluate agronomic efficacy and operational performance under real field conditions. This... R. Luiz Panini, A.R. Tamara, G.M. Franco, V.C. De Oliveira, H.R. Lemos, M. Nishikawa, P.R. Forti, M. Biagi, L.F. Dudek, M.C. Hauschild, G.N. Ruscito, M.P. Da Silva, E. Claro, Z.M. De Souza |
2. Democratizing Prescriptive Agronomy: Quality-Preserving Edge AI for Sugar BeetsThe global sugar beet sector faces a critical production paradox where agronomic interventions designed to maximize root yield often compromise sucrose concentration and processing quality. While precision agriculture aims to navigate this delicate balance, current methodologies have reached a methodological impasse. Existing solutions are bifurcated between descriptive data-intensive machine learning (ML), which struggles to generalize across heterogeneous fields, and physiological Process-Based... A. Tabbassi, S. Henkler, A. Zakhary, K. Rother |
3. Digital Livestock Management Solution for Cattle Identification, Traceability, and Real-time MonitoringBrazil is a global player in the beef industry with the world's largest commercial bovine herd, exceeding 230 million head, and leads the international market, accounting for approximately 25% of the global beef trade, reaching over 150 international markets. The combination of edaphoclimatic diversity, high-performance genetics, rigorous sanitary protocols, and the adoption of technological framework for tropical livestock accounts for decoupling of Brazilian ranching from extensive, low-productivity... A. Bernardi, A.R. Garcia, E.S. Guimarães, F. Tonato, S.R. Medeiros, W. Barioni Jr., J.B. Portugal, T.C. Alves, W.P. Cavalcante, M. Serão Filho, C. Gaioli Jr |
4. Monitoring, Automation, and Control System for Small Scale SilosBrazil has an important role in the global grain production scenario. However, the country’s grain storage infrastructure is usually inadequate and insufficient. The drying and storage of grains post-harvest are essential to ensure product quality, even over extended periods. In this context, research has been conducted on sensing, monitoring, and prediction of grain temperature and humidity, as well as the automation and control of the drying process. Therefore, this paper presents a system... C.C. Scharlau, C.R. Krumreich, N. Pagani Neto, H.M. Tamba , B. Culda |
5. Standardisation Challenges in Precision Agriculture: Mapping the Landscape and Advancing Semantic InteroperabilityBackground: Precision agriculture increasingly depends on digital technologies and the exchange of data between equipment, sensors, platforms and decision support tools. A wide range of standards is available, including machine data formats such as ISOXML and semantic resources such as AGROVOC and rmAgro. Despite this variety, the overall standardisation landscape remains fragmented. Even within single countries, differences in code lists, vocabularies and data publishing... J. Tummers, F. Sijbrandij, T. Ten Den, A. Gupta, T. Bresilla, B. Veldhuisen |
6. Enhancing Weed Detection in Corn Crops Through Attention-based Models and Curated DatasetsWeed infestation is one of the leading causes of global agricultural productivity losses, directly impacting production costs, environmental sustainability, and food security. In precision agriculture, automated weed detection from aerial imagery enables site-specific herbicide application, reducing chemical overuse and environmental impact. Deep learning-based computer vision techniques have been widely adopted for this purpose, with Convolutional Neural Networks (CNNs) historically dominating... T.M. Martins, E.C. Tetila, J.G. Barbedo, J.C. Felipe, L. Zhao |
7. Growth-Stage and Hourly Modeling of Non-Stressed Soybean Canopy Temperature Using High-Frequency Proximal Thermal SensingCanopy temperature (Tc) sensing provides a proximal, non-destructive approach for monitoring crop water status. It supports irrigation scheduling through thermal indices such as the Crop Water Stress Index (CWSI) and Degrees Above Non-Stressed (DANS), both of which require accurate estimation of non-stressed canopy temperature (Tcns) (Nakabuye et al., 2022). Maintaining a continuously non-stressed reference treatment to determine Tcns is operationally difficult, motivating development of weather-based... |
8. Fusing Deep Learning and Control Theory for Optimized Sugar Beet Yield PredictionAccurate yield prediction is a vital field of research in precision agriculture, enabling optimal resource allocation and enhanced food security under growing climatic uncertainty. Traditional models struggle to capture complex, non-linear interactions between environmental drivers and crop growth. To address this, we present our approach, a multi-stage method for sugar beet yield prediction and management that integrates deep learning with control-theoretic techniques and mathematical language... A. Tabbassi |