Predicting sugarcane yield through temporal analysis of satellite imagery during the growth phase.
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This research investigates how to estimate sugarcane (Saccharum officinarum L.) yield at harvest by using an average satellite image time-series collected during the growth phase. This study aims to evaluate the effectiveness of various modeling approaches, including a heteroskedastic gamma regression model, Random Forest, and Artificial Neural Networks, in predicting sugarcane yield based on satellite-derived vegetation indices and environmental variables. Key covariates analyzed include sugarcane varieties, production cycles, accumulated precipitation during the growth phase, and the mean GNDVI vegetation index. The analysis was conducted in two locations over two consecutive growing seasons. The research emphasizes the integration of satellite data with advanced statistical and machine learning techniques to enhance yield prediction in agricultural systems, specifically focusing on sugarcane cultivation.
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Agricultura digital, Rendimento de cultura, Modelo estatístico, Aprendizado de máquina, Digital agriculture, Machine learning, Cana de Açúcar, Agricultura de Precisão, Saccharum Officinarum, Sugarcane, Crop yield, Statistical models