Proceedings
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| Filter results2 paper(s) found. |
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1. 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 |
2. Assessment of Machine Learning Models for Leaf Chlorophyll Estimation Using Visible-Range ReflectanceChlorophyll content plays a central role in the photosynthetic process directly influencing plant growth, development and yield. However, plant pigment dynamics arise from complex metabolic interactions that are not adequately captured by conventional statistical approaches or traditional laboratory analyses, which are time-consuming and impractical for large-scale field applications. In this context, remote sensing offers a non-destructive alternative for assessing foliar pigments in agricultural... T. Costa Barboza, W. Batista Da Silva, S. Guimarães Moreira, S. Godinho Silva, L. Lacerda, A. Felipe Dos Santos |