Deciphering the contributions of spectral and structural data to wheat yield estimation from proximal sensing

Abstract

Accurate, efficient, and timely yield estimation is critical for crop variety breeding and management optimization. However, the contributions of proximal sensing data characteristics (spectral, temporal, and spatial) to yield estimation have not been systematically evaluated. We collected long-term, hyper-temporal, and large-volume light detection and ranging (LiDAR) and multispectral data to (i) identify the best machine learning method and prediction stage for wheat yield estimation, (ii) characterize the contribution of multisource data fusion and the dynamic importance of structural and spectral traits to yield estimation, and (iii) elucidate the contribution of time-series data fusion and 3D spatial information to yield estimation. Wheat yield could be accurately (R2 = 0.891) and timely (approximately-two months before harvest) estimated from fused LiDAR and multispectral data. The artificial neural network model and the flowering stage were always the best method and prediction stage, respectively. Spectral traits (such as CIgreen) dominated yield estimation, especially in the early stage, whereas the contribution of structural traits (such as height) was more stable in the late stage. Fusing spectral and structural traits increased estimation accuracy at all growth stages. Better yield estimation was realized from traits derived from complete 3D points than from canopy surface points and from integrated multi-stage (especially from jointing to heading and flowering stages) data than from single-stage data. We suggest that this study offers a novel perspective on deciphering the contributions of spectral, structural, and time-series information to wheat yield estimation and can guide accurate, efficient, and timely estimation of wheat yield.