Exploring the prospects of UAV-Remotely sensed data in estimating productivity of Maize crops in typical smallholder farms of Southern Africa
This study estimated maize grain biomass, and grain biomass as a proportion of the absolute maize plant biomass using UAV-derived multispectral data. Results showed that UAV-derived data could accurately predict yield with R2 ranging from 0.80 – 0.95, RMSE ranging from 0.03 - 0.94 kg/m2 and RRMSE ranging from 2.21% - 39.91% based on the spectral datasets combined. Results of this study further revealed that the VT-R1 (56-63 days after emergence) vegetative growth stage was the most optimal stage for the early prediction of maize grain yield (R2 Combining double low line 0.85, RMSE Combining double low line 0.1, RRMSE Combining double low line 5.08%) and proportional yield (R2 Combining double low line 0.92, RMSE Combining double low line 0.06, RRMSE Combining double low line 17.56%), with the Normalized Difference Vegetation Index (NDVI), Enhanced Normalized Difference Vegetation Index (ENDVI), Soil Adjusted Vegetation Index (SAVI) and the red edge band being the most optimal prediction variables. The grain yield models produced more accurate results in estimating maize yield when compared to the biomass and proportional yield models. The results demonstrate the value of UAV-derived data in predicting maize yield on smallholder farms - a previously challenging task with coarse spatial resolution satellite sensors.
| Main Authors: | , , , , , , , , , |
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| Format: | Conference Paper biblioteca |
| Language: | English |
| Published: |
Copernicus GmbH
2023
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| Subjects: | AGRICULTURAL SCIENCES AND BIOTECHNOLOGY, Smallholder Farms, Random Forest Algorithm, Grain Yield, Proportional Yield, GRAIN, YIELDS, MAIZE, SMALLHOLDERS, UNMANNED AERIAL VEHICLES, REMOTE SENSING, VEGETATION INDEX, Sustainable Agrifood Systems, |
| Online Access: | https://hdl.handle.net/10883/23147 |
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| Summary: | This study estimated maize grain biomass, and grain biomass as a proportion of the absolute maize plant biomass using UAV-derived multispectral data. Results showed that UAV-derived data could accurately predict yield with R2 ranging from 0.80 – 0.95, RMSE ranging from 0.03 - 0.94 kg/m2 and RRMSE ranging from 2.21% - 39.91% based on the spectral datasets combined. Results of this study further revealed that the VT-R1 (56-63 days after emergence) vegetative growth stage was the most optimal stage for the early prediction of maize grain yield (R2 Combining double low line 0.85, RMSE Combining double low line 0.1, RRMSE Combining double low line 5.08%) and proportional yield (R2 Combining double low line 0.92, RMSE Combining double low line 0.06, RRMSE Combining double low line 17.56%), with the Normalized Difference Vegetation Index (NDVI), Enhanced Normalized Difference Vegetation Index (ENDVI), Soil Adjusted Vegetation Index (SAVI) and the red edge band being the most optimal prediction variables. The grain yield models produced more accurate results in estimating maize yield when compared to the biomass and proportional yield models. The results demonstrate the value of UAV-derived data in predicting maize yield on smallholder farms - a previously challenging task with coarse spatial resolution satellite sensors. |
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