A low-cost greenhouse-based high-throughput phenotyping platform for genetic studies: a case study in maize under inoculation with plant growth-promoting bacteria

Abstract

Greenhouse-based high-throughput phenotyping (HTP) presents a useful approach for studying novel plant growth-promoting bacteria (PGPB). Despite the potential of this approach to leverage genetic variability for breeding new maize cultivars exhibiting highly stable symbiosis with PGPB, greenhouse-based HTP platforms are not yet widely used because they are highly expensive; hence, it is challenging to perform HTP studies under a limited budget. In this study, we built a low-cost greenhouse-based HTP platform to collect growth-related image-derived phenotypes. We assessed 360 inbred maize lines with or without PGPB inoculation under nitrogen-limited conditions. Plant height, canopy coverage, and canopy volume obtained from photogrammetry were evaluated five times during early maize development. A plant biomass index was constructed as a function of plant height and canopy coverage. Inoculation with PGPB promoted plant growth. Phenotypic correlations between the image-derived phenotypes and manual measurements were at least 0.6. The genomic heritability estimates of the image-derived phenotypes ranged from 0.23 to 0.54. Moderate-to-strong genomic correlations between the plant biomass index and shoot dry mass (0.24-0.47) and between HTP-based plant height and manually measured plant height (0.55-0.68) across the developmental stages showed the utility of our HTP platform. Collectively, our results demonstrate the usefulness of the low-cost HTP platform for large-scale genetic and management studies to capture plant growth.

Classification of plant growth-promoting bacteria inoculation status and prediction of growth-related traits in tropical maize using hyperspectral image and genomic data

“…For genomic prediction, the higher predictive ability for PH was probably due to its higher heritability in comparison to SD and SDM (Yassue et al, 2021a,b). Similar to the phenomic prediction, AutoML yielded relatively good predictions.…”

“…This could limit the applicability hyperspectral imaging in plant breeding or genetics program pipelines because of the laborious data collection time. The use of hyperspectral cameras in a low-cost, high-throughput phenotyping platform, such as that reported by Yassue et al (2021b), may ease the application of phenomic prediction that uses hyperspectral reflectance data.…”

“…The use of hyperspectral cameras in a low-cost, high-throughput phenotyping platform, such as that reported by Yassue et al (2021b), may ease the application of phenomic prediction that uses hyperspectral reflectance data.…”

Genome-wide association analysis of hyperspectral reflectance data to dissect growth-related traits genetic architecture in maize under inoculation with plant growth-promoting bacteria

“…Manually measured phenotypes included plant height (PH), stalk diameter (SD), and shoot dry mass (SDM). Detailed information on the experimental design can be found in Yassue et al (2021Yassue et al ( , 2022b.…”

From pixel to knowledge: how high-throughput phenotyping helps to dissect the genetic architecture and improves predictive ability in maize under inoculation with plant growth-promoting bacteria

“…This could limit the applicability hyperspectral imaging in plant breeding or genetics program pipelines because of the laborious data collection time. The use of hyperspectral cameras in a low-cost, highthroughput phenotyping platform, such as that reported by Yassue et al (2021b), may ease the application of phenomic prediction that uses hyperspectral reflectance data.…”