Sugarcane yields prediction at the row level using a novel cross-validation approach to multi-year multispectral images

Early prediction of sugarcane crop yield would benefit sugarcane growers and policymakers by allowing for timely decisions. The primary objective of this study was to reduce reliance on satellite images and improve early prediction of sugarcane yield at row level by using high-resolution multispectral Unmanned Aerial Vehicle (UAV) imagery. To our knowledge, no previous study has evaluated the performance of multispectral UAV-derived vegetation indices in sugarcane crops at the crop row level. In this study, we used UAV mapping on 48 rows of sugarcane at three main growth stages (early, middle, and mature) over three growing seasons. A secondary objective was to predict future sugarcane yields at the earliest possible stage of growth. The results showed that the optimal growth stage for all 23 VIs varied, but the middle stage, from mid-March to early May, was the most prevalent. Further detailed analysis in the middle stage revealed that March was the best month for predicting future sugarcane yields when compared to April and May. This result is approximately a month earlier than previous studies in the same region. Following two stages of feature selection, such as Pearson correlation analysis and stepwise feature selection, a novel cross-validation methodology based on a generalized linear model trained and tested the yield prediction models on various combinations of the VIs. This novel methodology improves model accuracy by avoiding overfitting and over complexity caused by interdependent VIs, and then validates the model generality using previously unseen data. The best performance was achieved by combining the Normalized Difference RedEdge (NDRE) and the Green–Red Normalized Difference Vegetation Index (GRNDVI) at March. These results help growers and decision-makers benefit from early row-level yield forecast, six months before harvest, if UAV mapping is available.