Integrating satellite imagery and environmental data to predict field-level cane and sugar yields in Australia using machine learning

An accurate model for predicting sugarcane yield will benefit many aspects of managing growth and harvest of sugarcane crops. In this study, Sentinel-1 and Sentinel-2 satellite imagery were used in combination with climate, soil and elevation data to predict field-level sugarcane yield across the multiple sugar mill areas in the Wet Tropics of Australia at different time steps over four consecutive growing seasons (2016-2019). A total of approximate to 1400 field-level measurements were used to train predictive machine learning models of cane yield (t/ha), commercial cane sugar (CCS, %), sugar yield (t/ha), crop varieties and ratoon numbers. We compared the predictive performance of models based on both satellite imagery only and a fusion of satellite imagery with climate, soil and topographical information. Randomized search on hyperparameters was the method used to optimize and identify the most accurate decision tree-based machine model. Overall, gradient boosting was the most accurate method for predicting sugarcane attributes. The analysis resulted in cane yield, CCS and sugar yield predicted at the field level with R-2 of up to 0.51 (RMSE = 16 t/ha), 0.63 (RMSE = 1 %) and 0.62 (RMSE = 2 t/ha) as soon as four months before the harvest season. It was also found that sugarcane varieties could be mapped with an accuracy of up to 73.4 %, while the differentiation of planted and ratoon crops exhibited the lowest accuracy of 45.4 %. Using a novel SHapley Additive exPlanations (SHAP) approach to explain the output of our machine learning models we found that Sentinel-2 derived spectral indices were the most important in predicting cane yield as well as differentiating sugarcane varieties and ratoon numbers. In contrast, climate and elevation derived predictors were the most important in predicting CCS and sugar yield. At the whole sugar mill area level, spatially averaged field-level results predicted mill area cane yield, CCS and sugar yield with R-2 of 0.75 (RMSE = 4.6 t/ha), 0.80 (RMSE = 0.6 %) and 0.77 (RMSE = 1 t/ha). Early season prediction of sugarcane yields at both fieldand mill-area level could be valuable for informing fertilizer application, harvest scheduling and marketing decisions.

Automated summary

An accurate model for predicting sugarcane yield using satellite imagery and environmental data was developed in this study, with a focus on the Wet Tropics of Australia. The models showed high predictive performance for field-level cane yield, sugar content, and sugar yield, as well as being able to differentiate between sugarcane varieties.