A Comparison of High-Throughput Imaging Methods for Quantifying Plant Growth Traits and Estimating Above-Ground Biomass Accumulation

Abstract

Image-based estimation of above-ground biomass accumulation is recognized as the predominant asset of breeding programs for accelerating gains in crop adaptation and productivity. High-throughput phenotyping (HTP) has the potential to greatly facilitate genetic improvements by dissecting morphological traits which can serve as accurate predictors of optically sensed plant biomass. Thus, various high-throughput data acquisition methods have been recently developed to quantify desirable phenotypes from images. Novel insights are essential to provide helpful guidelines to breeders for the optimal selection of phenotyping approaches aimed at estimating plant biomass. In this study, three representative HTP data acquisition methods based on two-dimensional (2D) image analysis, Multi View Stereo (MVS)-Structure from Motion (SfM) three-dimensional (3D)-reconstruction and Structured Light (SL) 3D-scanning were compared for estimating fresh (FAGB) and dry above-ground biomass (DAGB) weight of potted plants at early growth stages. Two crop species with contrasting shape and canopy architecture, namely maize ( Zea mais L.) and tomato ( Lycopersicum esculentum L.), were used as model plants. First, the performances of each sensing approach were tested in the accurate reproduction of the major phenotypic traits and, secondly, in the reliable fresh/dry AGB estimation from the relevant allometric equations calibrated according multi- (six sampling dates, once a week) and mono-temporal (one sampling date at harvest time) datasets. The overall results demonstrated the effectiveness of the tested methods in reproducing the salient features of canopies with increasing architectural complexity, including plants’ height (R2 = 0.98, rRMSE = 7.73% and AIC = 475.07), shoot area (R2 = 0.91, rRMSE = 29.53% and AIC = 1369.77) and convex hull volume (R2 = 0.88, rRMSE = 27.32% and AIC = 818.19). In this context, the shoot area associated with the age of the plant was found to be the most indicative phenotypic determinant for an accurate estimation of DAGB and FAGB. Accordingly, the greater ability of the 2D image analysis in quantifying canopies of elongated plants characterized by thin organs ensured the best estimates of fresh/dry biomass accumulation in maize (0.99 ≤ R2 ≤ 0.98 and 8.98% ≤ rRMSE ≤ 16.03%, considering multi- and mono-temporal calibrated models). Contrariwise, the MVS-SfM 3D-reconstruction of more complex canopies with compact habit was advantageous for the accurate prediction of above-ground DAGB and FAGB dynamics in tomato (R2 = 0.99 and 6.70% ≤ rRMSE ≤ 15.82%, considering multi- and mono-temporal calibrated models). These findings provide references to carefully select the best suited HTP data acquisition approach for the accurate estimation of biomass accumulation across plants of different canopy complexity, thereby paving the way to break through current phenotyping bottlenecks in breeding applications for current and future food security.

Keywords: Digital biomass, high-throughput phenotyping platforms, Multi View Stereo, plant modelling