Sorghum yield prediction using UAV multispectral imaging and stacking ensemble learning in arid regions

Frequent droughts and climate fluctuations pose significant challenges to stabilizing and increasing the yields of drought-tolerant crops like sorghum. Accurate, detailed, and spatially explicit yield predictions are essential for precision irrigation, variable fertilization, and food security assessment.

This study was conducted in the Lifang dryland experimental area in Jinzhong, Shanxi Province, using a sorghum planting experiment. Multispectral imagery and meteorological data were collected simultaneously using a DJI Mavic 3M UAV during key growth stages (seedling emergence, jointing, flowering, and maturity). A "spectral-meteorological-spatial" three-dimensional prediction framework was developed using eight machine learning algorithms. SHAP values and Partial Dependency Plots were used to assess variable importance.

Ensemble learning algorithms performed best, with the Gradient Boosting model achieving an R² of 0.9491 and Random Forest reaching 0.9070. SHAP analysis revealed that DVI and NDGI were the most important predictors. The jointing stage contributed most to prediction accuracy (R² = 0.9454), followed by maturity (R² = 0.9215) and flowering (R² = 0.9075). Yield spatial distribution ranged from 4,291 to 4,965 kg haR^-1, with a global Moran's I index of 0.5552 indicating moderate positive spatial autocorrelation.

Integrating UAV multispectral data with machine learning methods enables efficient sorghum yield prediction, with the jointing stage identified as the optimal monitoring period. This study provides crucial technical support for precision planting and efficient sorghum management in arid regions.