Improved potato AGB estimates based on UAV RGB and hyperspectral images

Abstract

Crops' above-ground biomass (AGB) is a crucial indicator that reflects crop health and predicts crop yield. However, using only optical vegetation indices (VIs) can produce inaccurate AGB estimates due to differences in crop varieties, growth stages, and measurement environments. Given the advantages of unmanned aerial vehicle (UAV) RGB and hyperspectral image fusion, this study evaluated the performance of multi-source remote sensing data for estimating potato AGB at multiple growth stages. In 2019, this study conducted potato trials with different varieties, fertilization levels, and planting densities at the Xiaotangshan Experiment Base (Beijing). UAV image and AGB data of potato three main stages were obtained from ground survey work. High-frequency information of the potato canopy was extracted from RGB images using discrete wavelet transform (DWT). VIs and wavelet energy coefficients were extracted from hyperspectral images using continuous wavelet transform (CWT). The linear relationships between potato AGB with VIs, high-frequency information, and wavelet coefficients were analyzed. Potato AGB estimation models were constructed based on single and multiple types of variables using multiple stepwise regression (MSR) and random forest (RF) models, respectively. This work showed the following results: (i) High-frequency information and wavelet coefficients were more sensitive to potato multi-growth stage AGB than VIs, and the latter were the most sensitive. (ii) Using VIs, high-frequency information, or wavelet coefficients separately to estimate the potato multi-growth stage AGB resulted in higher error and lower model accuracy. (iii) Combining VIs with either high-frequency information or wavelet coefficients improved the accuracy of AGB estimation, which was further improved by combining high-frequency information with wavelet coefficients. (iv) Combining VIs with both high-frequency information and wavelet coefficients provided the highest estimation accuracy using the MSR method. This combined AGB estimation model reduced the RMSE by 27%, 21%, and 16%, respectively, relative to VIs, high-frequency information, or wavelet coefficients alone. This result shows that the complementary advantages of multi-source UAV data can solve the challenge of insufficient AGB estimation by optical remote sensing. The work in this study provides remote sensing technology support to achieve potato crop growth monitoring and improve yield predictions.