Advances in field-based high-throughput photosynthetic phenotyping

Abstract

Gas exchange techniques revolutionized plant research and advanced understanding, including associated fluxes and efficiencies, of photosynthesis, photorespiration, and respiration of plants from cellular to ecosystem scales. These techniques remain the gold standard for inferring photosynthetic rates and underlying physiology/biochemistry, although their utility for high-throughput phenotyping (HTP) of photosynthesis is limited both by the number of gas exchange systems available and the number of personnel available to operate the equipment. Remote sensing techniques have long been used to assess ecosystem productivity at coarse spatial and temporal resolutions, and advances in sensor technology coupled with advanced statistical techniques are expanding remote sensing tools to finer spatial scales and increasing the number and complexity of phenotypes that can be extracted. In this review, we outline the photosynthetic phenotypes of interest to the plant science community and describe the advances in high-throughput techniques to characterize photosynthesis at spatial scales useful to infer treatment or genotypic variation in field-based experiments or breeding trials. We will accomplish this objective by presenting six lessons learned thus far through the development and application of proximal/remote sensing-based measurements and the accompanying statistical analyses. We will conclude by outlining what we perceive as the current limitations, bottlenecks, and opportunities facing HTP of photosynthesis.

A ‘wiring diagram’ for sink strength traits impacting wheat yield potential

“…It has been previously shown that more natural variation is seen between genotypes during non-steady-state conditions than steady-state conditions, suggesting that our previous understanding of natural variation for photosynthetic traits may be underestimated (Acevedo-Siaca et al, 2020;McAusland et al, 2020;Cowling et al, 2022). It remains difficult to measure dynamic properties at a high throughput required for field screening, for example by using gas exchange, solar-induced fluorescence, or spectral reflectance; however, this is an active research area (reviewed in Murchie et al, 2018;Fu et al, 2022). Recent advances in very high-throughput laboratory-based methodologies, for example using chlorophyll fluorescence (Ferguson et al, 2020;McAusland et al, 2020), have shown promise if these can be scaled to the field.…”

A ‘wiring diagram’ for source strength traits impacting wheat yield potential

“…It has been previously shown that more natural variation is seen between genotypes during non-steady-state conditions than steady-state conditions, suggesting that our previous understanding of natural variation for photosynthetic traits may be underestimated ( Acevedo-Siaca et al , 2020 ; McAusland et al , 2020 ; Cowling et al ., 2022 ). It remains difficult to measure dynamic properties at a high throughput required for field screening, for example by using gas exchange, solar-induced fluorescence, or spectral reflectance; however, this is an active research area (reviewed in Murchie et al , 2018 ; Fu et al , 2022 ). Recent advances in very high-throughput laboratory-based methodologies, for example using chlorophyll fluorescence ( Ferguson et al , 2020 ; McAusland et al , 2020 ), have shown promise if these can be scaled to the field.…”

Retrieving rice (Oryza sativa L.) net photosynthetic rate from UAV multispectral images based on machine learning methods

“…Machine learning methods have been widely used in the regression and classification issues and have been proven to be fast, accurate, and good at generalization. WA (Bruce et al, 2002), partial least square regression (PLSR) (Fu et al, 2022), and least absolute shrinkage and selection operator (LASSO) (Yang and Bao, 2017) are usually used in HSI studies to reduce the high-dimension hyperspectral data to a few important components that are sensitive to the target parameters.…”