Faba bean and pea harvest index estimations using aerial-based multimodal data and machine learning algorithms

Le résumé fourni par la source

Early and high-throughput estimations of the crop harvest index (HI) are essential for crop breeding and field management in precision agriculture; however, traditional methods for measuring HI are time-consuming and labor-intensive. The development of unmanned aerial vehicles (UAVs) with onboard sensors offers an alternative strategy for crop HI research. In this study, we explored the potential of using low-cost, UAV-based multimodal data for HI estimation using red-green-blue (RGB), multispectral (MS), and thermal infrared (TIR) sensors at 4 growth stages to estimate faba bean (Vicia faba L.) and pea (Pisum sativum L.) HI values within the framework of ensemble learning. The average estimates of RGB (faba bean: coefficient of determination [R2] = 0.49, normalized root-mean-square error [NRMSE] = 15.78%; pea: R2 = 0.46, NRMSE = 20.08%) and MS (faba bean: R2 = 0.50, NRMSE = 15.16%; pea: R2 = 0.46, NRMSE = 19.43%) were superior to those of TIR (faba bean: R2 = 0.37, NRMSE = 16.47%; pea: R2 = 0.38, NRMSE = 19.71%), and the fusion of multisensor data exhibited a higher estimation accuracy than those obtained using each sensor individually. Ensemble Bayesian model averaging provided the most accurate estimations (faba bean: R2 = 0.64, NRMSE = 13.76%; pea: R2 = 0.74, NRMSE = 15.20%) for whole growth stage, and the estimation accuracy improved with advancing growth stage. These results indicate that the combination of low-cost, UAV-based multimodal data and machine learning algorithms can be used to estimate crop HI reliably, therefore highlighting a promising strategy and providing valuable insights for high spatial precision in agriculture, which can help breeders make early and efficient decisions.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.