Monitoring of crops during their growth period is essential to increase the profitability of production and reduce costs. Unmanned aerial vehicles (UAVs) have been introduced as an appropriate tool in precision farming, due to their affordability and high-spatial resolution data. The potential of plant height and vegetation indices derived from UAV-based RGB imagery at 12-leaf growth stage (V12) is investigated for prediction of corn forage yield. Using the structure from motion techniques, the crop height model (CHM) was created by subtracting crop surface model from digital elevation model. Next, by evaluating the CHM using ground measurements of plant height, it was shown that UAV imagery provides high capability in modeling the plant height (R2 = 0.85). Pearson’s correlation coefficient (R) between variables showed that the plant height had the highest correlation with forage yield (R = 0.84). Furthermore, among the vegetation indices, visible atmospherically resistant index with R = 0.77, normalized difference index with R = 0.75, and excess red with R = 0.74 were better than other indices. Finally, the partial least squares regression (PLSR) model was constructed using plant height and vegetation indices to predict forage yield. The beta regression coefficients of the PLSR model were used to identify the most relevant predictors, and the final PLSR model was rebuilt with relevant predictors. The PLSR analysis confirmed that the plant height was the most important predictor, although integrating the vegetation indices in the PLSR model has slightly improved the prediction model (R2 = 0.74, RMSE = 0.316 kg / m2, and relative RMSE = 12.39 % ), compared to the model that used only plant height (R2 = 0.702, RMSE = 0.339 kg / m2, and relative RMSE = 13.29 % ). Overall, these results indicate that the UAV-based images are efficient and cost-effective data to predict corn forage yield as well as plant height modeling.
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