Estimation of Aboveground Rice Biomass by Unmanned Aerial Vehicle Imaging

  Objective   Feasibility of using images generated by unmanned aerial vehicle (UAV) to estimate the aboveground biomass (AGB) on a rice field was evaluated for crop production prediction.

  Methods   On fields of two different varieties of rice fertilized with 4 varied nitrogen applications, AGB of rice plants at tillering, booting, and full heading stages were recorded by using the UAV imaging technology. Data on color and texture measurements were extracted from the images to correlate with corresponding AGB. A mathematic model was constructed, tested, and validated for prediction accuracy.

  Result   On color, the red and blue differentiation (r-b) of the images highly correlated with the AGB; on texture, it was the G-mean. A prediction model was thus obtained for the entire growth period as y=2 544.507+5 054.243x₁−145.543x₂−556.553x₁x₂+27 379.41x₁²+3.927x₂² , which had a correlation coefficient (R²) of 0.920 2 and a test determination coefficient of 0.911 2.

  Conclusion   The prediction model based on r-b and G-mean derived from the UAV images performed satisfactorily in monitoring the AGB for the entire growth period of rice in the field. It was conceivably applicable for the farming operation and crop management.