Machine learning models based on remote and proximal sensing as potential methods for in-season biomass yields prediction in commercial sorghum fields.

Abstract

Crop yield monitoring demonstrated the potential to improve agricultural productivity through improved crop breeding, farm management and commodity planning. Remote and proximal sensing offer the possibility to cut crop monitoring costs traditionally associated with surveys and censuses. Fraction of absorbed photosynthetically active radiation (fAPAR), chlorophyll concentration (CI) and normalized difference vegetation (NDVI) indices were used in crop monitoring, but their comparative performances in sorghum monitoring is lacking. This work aimed therefore at closing this gap by evaluating the performance of machine learning modelling of in-season sorghum biomass yields based on Sentinel-2-derived fAPAR and simpler high-throughput optical handheld meters-derived NDVI and CI calculated from sorghum plants reflectance. Bayesian ridge regression showed good cross-validated performance, and high reliability (R2 = 35%) and low bias (mean absolute prediction error, MAPE = 0.4%) during the validation step. Hand-held optical meter-derived CI and Sentinel-2-derived fAPAR showed comparable effects on machine learning performance, but CI outperformed NDVI and was therefore considered as a good alternative to Sentinel-2's fAPAR. The best times to sample the vegetation indices were the months of June (second half) and July. The results obtained in this work will serve several purposes including improvements in plant breeding, farming management and sorghum biomass yield forecasting at extension services and policy making levels.

Plant phenotyping relevance

ソルガムのバイオマス収量という植物形質を、リモート・近接センシングと機械学習で推定し、指標間の性能比較と検証を行っており、フェノタイピング手法が中心である。

Code and data availability

The article describes sorghum biomass yield prediction from Sentinel-2 fAPAR and handheld NDVI/CI reflectance data, but no public phenotype dataset, raw sensor data, or author analysis code is made available. The only supplement (S1 Table) merely describes feature names/descriptive statistics, not the underlying data,;

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