Combining 2D image and point cloud deep learning to predict wheat above ground biomass

Abstract

Purpose

The use of Unmanned aerial vehicle (UAV) data for predicting crop above-ground biomass (AGB) is becoming a more feasible alternative to destructive methods. However, canopy height, vegetation index (VI), and other traditional features can become saturated during the mid to late stages of crop growth, significantly impacting the accuracy of AGB prediction.

Methods

In 2022 and 2023, UAV multispectral, RGB, and light detection and ranging point cloud data of wheat populations were collected at seven growth stages across two experimental fields. The point cloud depth features were extracted using the improved PointNet++ network, and AGB was predicted by fusion with VI, color index (CI), and texture index (TI) raster image features.

Results

The findings indicate that when the point cloud depth features were fused, the R² values predicted from VI, CI, TI, and canopy height model images increased by 0.05, 0.08, 0.06, and 0.07, respectively. For the combination of VI, CI, and TI, R² increased from 0.86 to a maximum of 0.9, while the root-mean-square error (RMSE) and mean absolute error were 1.80 t ha⁻¹ and 1.36 t ha⁻¹, respectively. Additionally, our findings revealed that the hybrid fusion exhibits the highest accuracy, it demonstrates robust adaptability in predicting AGB across various years, growth stages, crop varieties, nitrogen fertilizer applications, and densities.

Conclusion

This study effectively addresses the saturation in spectral and chemical information, provides valuable insights for high-precision phenotyping and advanced crop field management, and serves as a reference for studying other crops and phenotypic parameters.