Sugarcane yield prediction and genotype selection using unmanned aerial vehicle‐based hyperspectral imaging and machine learning

Abstract Sugarcane ( Saccharum spp. interspecific hybrids), a high biomass perennial crop, in which manual data collection for early yield prediction, through its growth cycle (∼12 mo long), is labor intensive and time consuming. Alternately, aerial imagery can be explored to predict yield‐related components and high‐throughput phenotyping for genetic selection. In this study, aerial imagery and ground data were collected in Stage IV (final stage of genotype selection) of the Florida sugarcane cultivar development program to evaluate the use of unmanned aerial vehicles in yield prediction (tons of cane per hectare [TCH], sucrose concentration, and tons of sugar per hectare [TSH]) in multiple new genotypes (13 in plant cane crop, nine in first ratoon crop). Aerial imagery data were collected using hyperspectral sensor, and yield data were collected through manual sampling of sugarcane stalks at harvest. The gradient‐boosting regression tree model was selected based on low mean absolute percentage error on multiple dates (April, July, and September) to determine the best timing of yield predictions. Results showed that yield was predicted with greater accuracy in July in plant crop and April in the first ratoon crop. Also, sucrose percentage was predicted with greater accuracy (94% in plant crop and 93% in first ratoon crop) than TCH and TSH. Although only two out of the top five genotypes were common in both selection methods (measured vs. predicted yields) in Stage IV, high accuracy in TCH and sucrose percentage shows that aerial imagery may be useful in making genotype selection in early stages when actual yield estimation is not feasible.