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.
Highlights
Under the climate change scenarios, the rapid increase of World population and industrial development are expected to give rise to increased carbon dioxide concentration in the Earth’s biosphere, while environments are predicted to be warmer and dryer, all of which will favor the cultivation of crops with a C4 photosynthetic pathway over C3 crops [1,2,3]
Across cropping seasons and experimental sites, Fraction of absorbed photosynthetically active radiation (fAPAR) data sampled on single days fortnightly spaced showed the highest values (0.76, 0.84, 0.80, 0.78, 0.91) of the first and third quartiles, median, mean and maximum on the day of year (DOY) 210 i.e., in the second half of
Chlorophyll concentration index (CI) data showed highest values (534.30, 546.90, 558.20) of first quartile, median, and mean on DOY 184–199, while highest values (621.70, 875.10, respectively) of the third quartile and the maximum were recorded on DOY 202–215 and 225–239, respectively
Summary
Under the climate change scenarios, the rapid increase of World population and industrial development are expected to give rise to increased carbon dioxide concentration in the Earth’s biosphere, while environments are predicted to be warmer and dryer, all of which will favor the cultivation of crops with a C4 photosynthetic pathway over C3 crops [1,2,3]. Humans will rely heavily on C4 crops like sorghum (Sorghum bicolor (L.) Moench). Machine learning models based on remote and proximal sensing for in-season yields prediction in sorghum
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