Investigation of Accuracy for Rice Crop Parameters Predicted Using UAV Multispectral Imagery

Remote measurement of rice crop parameters; Leaf- Chlorophyll, Above-ground biomass, Plant Height, Leaf Moisture and Rice Yield of rice plants before the actual harvest are vital for the early management of rice crops. This study was conducted in controlled rice fields and extended to farmer rice fields in the Maha season in Sri Lanka. The multispectral aerial images of the field were acquired by an Unmanned Ariel Vehicle (UAV) and processed. Vegetation indices (VIs) were then derived and selected the best combination of VIs explaining the respective groundmeasured parameters. The combination of selected VIs showed significant association (R²=0.84, RMSE=0.28; R²=0.81, RMSE=0.06 kg/m²; R²=0.63, RMSE=0.11 cm; R²=0.57, RMSE= 0.16% and R²=0.98, RMSE=128.5 kg/ha, Leaf-Chlorophyll, Above-ground biomass, Plant height, Leaf moisture, and Rice yield respectively) with the ground data in the controlled rice field experiment conducted at the Rice Research and Development Institute (RRDI), Bathalagoda, Sri Lanka. When these selected vegetation indices were transferred to a farmer rice field they showed a relationship as R2=0.57, RMSE=1.35; R2=0.60, RMSE=1.23 kg/m2; R2=0.41, RMSE= 9.95 cm; R²=0.36, RMSE= 34.4% and R²=0.53, RMSE=563.04 kg/ha respectively., The vegetation indices derived from UAV-multispectral images were able to associate and estimate the rice crop parameters like Leaf- Chlorophyll, Above-ground biomass and Rice Yield at booting stage and 25 m flying height. Therefore, VIs developed using UAV multispectral imagery to measure Leaf-Chl, AGB, PHt, LM and RY behave differently across rice fields in different soils, rice varieties, weedy conditions and agronomic practices.