Proceedings
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| Filter results4 paper(s) found. |
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1. AI Tools in Agri DSS Pipeline - the Case of Irrigated SugarbeetA general pipeline that can be associated to a DSS includes several steps. Data Collectionn includes Acquisition, extraction, and aggregation of data from previously identified and selected sources. Data Cleaning and preparation make data available for exploratory analysis that make them usable. Data Analysis is then applied to extract meaningful information e.g. by statistical and/or simulation models. Data are successively synthesized and visualized to make them clear to the decision-maker to... G.-. Vitali, C. Ferraz |
2. Large-Scale Sugarcane Yield Prediction Across Regions by Integrating Multi-Source Remote Sensing and Machine LearningSugarcane (Saccharum officinarum L.) is one of the most important agro-industrial crops worldwide, playing a key role in sugar, bioethanol, and renewable energy production. Early and accurate yield estimation during the growing season is essential to support agricultural planning, resource management, and decision-making in the sugar-energy industry under increasing climate variability. However, most yield models are calibrated to single locations and struggle to transfer across regions. The primary... R. Fortes Gallego, F. Serra Burriel, M. Cabrera Dengra, C. Ferraz, A. Do Vale Dondo |
3. Development of a Predictive Machine Learning Model for Pasture Biomass Using Satellite Vegetation Indices and Climate Data in Spanish Dehesa SystemsExtensive livestock systems are fundamental to the ecological, economic, and cultural sustainability of Mediterranean agroecosystems such as the Spanish dehesa. These silvopastoral landscapes support biodiversity, prevent land abandonment, and sustain rural livelihoods, but their productivity is highly dependent on pasture availability. Efficient management therefore requires accurate and timely information on pasture biomass, which is strongly influenced by climatic variability, soil properties,... C. Ferraz, A. Tamayo López, A. Do Vale Dondo |
4. Operational Satellite Weed Detection Across 14,000 Sugarcane Fields: Lessons in Temporal Feature DesignWeed infestations in sugarcane (Saccharum officinarum L.) can reduce yields by 20–60% depending on species composition and management timing, yet operational weed management at scale remains an unsolved challenge. Existing studies typically cover tens of fields; scaling to thousands introduces challenges in processing throughput, ground truth scarcity, and feature design. This work describes the development and operational deployment of a satellite-based weed detection system covering... C. Ferraz, R. Fortes, M. Cabrera Dengra, J. Poli, E. Bernardes Júnior, A. Do Vale Dondo |