Predicting Sugarcane Yield Through Temporal Analysis of Satellite Imagery During the Growth Phase

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. The results indicate that the heteroskedastic gamma regression model outperformed the other methods in explaining yield variability, particularly in commercial sugarcane fields, achieving a Coefficient Determination (R2) of 0.89. These findings highlight the potential of these models to support informed decision-making and optimize agricultural practices, providing valuable insights for precision farming. Overall, the results of this study represent an initial step toward developing more robust models for predicting sugarcane yield. Future work will involve incorporating additional variables to better assess the impacts of environmental stresses, such as high temperatures and water deficits, on the crop’s agronomic performance.