Research on the estimation of wheat AGB at the entire growth stage based on improved convolutional features

The wheat above-ground biomass (AGB) is an important index that shows the life activity of vegetation, which is of great significance for wheat growth monitoring and yield prediction. Traditional biomass estimation methods specifically include sample surveys and harvesting statistics. Although these methods have high estimation accuracy, they are time-consuming, destructive, and difficult to implement to monitor the biomass at a large scale. The main objective of this study is to optimize the traditional remote sensing methods to estimate the wheat AGB based on improved convolutional features (CFs). Low-cost unmanned aerial vehicles (UAV) were used as the main data acquisition equipment. This study acquired image data acquired by RGB camera (RGB) and multi-spectral (MS) image data of the wheat population canopy for two wheat varieties and five key growth stages. Then, field measurements were conducted to obtain the actual wheat biomass data for validation. Based on the remote sensing indices (RSIs), structural features (SFs), and CFs, this study proposed a new feature named AUR-50 (multi-source combination based on convolutional feature optimization) to estimate the wheat AGB. The results show that AUR-50 could estimate the wheat AGB more accurately than RSIs and SFs, and the average R² exceeded 0.77. In the overwintering period, AUR-50_MS (multi-source combination with convolutional feature optimization using multispectral imagery) had the highest estimation accuracy (R² of 0.88). In addition, AUR-50 reduced the effect of the vegetation index saturation on the biomass estimation accuracy by adding CFs, where the highest R² was 0.69 at the flowering stage. The results of this study provide an effective method to evaluate the AGB in wheat with high throughput and a research reference for the phenotypic parameters of other crops.