Illuminating duckweed: hyperspectral reflectance highlights a predictive ceiling for accurate determination of biomass

Abstract

Duckweed is a fast growing aquatic plant with strong potential as a sustainable, high protein crop that can be produced without expanding agricultural land. However, substantial trait variation necessitates rapid selection proxies to identify genotypes with high growth, biomass and pigment content while simultaneously suited to low input, controlled environments. Although hyperspectral reflectance phenotyping is widely applied in conventional crops, its utility for duckweed remains unexplored. We assessed 133 UK accessions from six species grown under ambient light, quantifying growth, pigment composition, photosynthetic performance and spectral reflectance. A second panel with contrasting light tolerance strategies, including growth and photosynthetic acclimation, was used to test whether hyperspectral reflectance could detect photoprotective mechanisms associated with high light resilience. Duckweed accessions displayed extensive variation across all traits. However, vegetation indices (VIs) commonly used in arable crops demonstrated only limited predictive accuracy for total chlorophyll (R2 =0.34, p<0.005) and maximum photosystem II (PSII) quantum yield (Fv/Fm) (R2 =0.25, p<0.0001), with poor performance for other traits. Under high light, VIs moderately differentiated tolerant accessions, including those maintaining higher Fv/Fm, {Phi}PSII operating efficiency and carotenoid:chlorophyll ratios. Partial least squares regression (PLSR) modelling improved predictive performance, providing reliable estimates of chlorophyll content (R2 =0.5, RMSE =2.05 mg/g freeze dry weight) maximum non-photochemical quenching (NPQ) (R2 =0.5, RMSE =0.57) and NPQ efficiency of PSII (R2 =0.53, RMSE =0.04). Although predictions of biomass and growth rate remained weak, green and red edge spectral regions were strongly associated with pigment contents and photosynthesis and indirectly linked to biomass. This study provides the first comprehensive evaluation of spectral phenotyping for duckweed crop development. Our findings indicate that broad spectrum modelling approaches, integrated with other high throughput phenotyping tools, may support selection of high vigour varieties and monitoring of commercial duckweed production systems.