Dynamics and genetic regulation of leaf nutrient concentration in barley based on hyperspectral imaging and machine learning. (February 2022)

• Record Type: Journal Article
• Authors: Grieco, Michele; Schmidt, Maria; Warnemünde, Sebastian; Backhaus, Andreas; Klück, Hans-Christian; Garibay, Adriana; Tandrón Moya, Yudelsy Antonia; Jozefowicz, Anna Maria; Mock, Hans-Peter; Seiffert, Udo; Maurer, Andreas; Pillen, Klaus
• Is Part Of: Plant science. Volume 315(2022)
• Publication Date: 2022-02
• Subjects: AI artificial intelligence -- ANOVA analysis of variance -- BLUEs best linear unbiased estimators -- CV cross-validation -- DR detection rate -- GWAS genome-wide association study -- HEB Halle Exotic Barley -- HIS hyperspectral imaging -- HR-ICP-MS high resolution inductively coupled plasma mass spectrometry -- MLP multilayer perceptron -- MRE mean relative error -- MTA marker-trait associations -- NAM nested association mapping -- PLS partial least squares regression -- QTL quantitative trait locus -- r Pearson's correlation coefficient -- R2 coefficient of determination -- RBF radial basis function network -- RI remobilization index -- RP relative performance -- RPD residual prediction deviation -- SNV standard normal variate; Artificial intelligence (AI) -- Barley (Hordeum vulgare) -- Genome-wide association study (GWAS) -- Hyperspectral imaging (HSI) -- Nested association mapping (NAM) -- Partial least squares regression (PLS); Botany -- Periodicals; Botanique -- Périodiques; 580
• DOI: 10.1016/j.plantsci.2021.111123
• Languages: English
• ISSNs: 0168-9452

Abstract

Highlights: Hyperspectral imaging and artificial intelligence (AI) can predict nutrient concentration in leaves. Prolonged barley grain filling correlates with the accumulation of N, P, K and Cu in leaves. Barley QTL regulate co-variation of leaf nutrients over developmental stages. The wild barley allele at QTL-4H-1 exerts positive effects on leaf concentration of N, P, K and Cu. HSI and AI can assist in large-scale field trials to select genes and improved breeding lines. Abstract: Biofortification, the enrichment of nutrients in crop plants, is of increasing importance to improve human health. The wild barley nested association mapping (NAM) population HEB-25 was developed to improve agronomic traits including nutrient concentration. Here, we evaluated the potential of high-throughput hyperspectral imaging in HEB-25 to predict leaf concentration of 15 mineral nutrients, sampled from two field experiments and four developmental stages. Particularly accurate predictions were obtained by partial least squares regression (PLS) modeling of leaf concentrations for N, P and K reaching coefficients of determination of 0.90, 0.75 and 0.89, respectively. We recognized nutrient-specific patterns of variation of leaf nutrient concentration between developmental stages. A number of quantitative trait loci (QTL) associated with the simultaneous expression of leaf nutrients were detected, indicating their potential co-regulation in barley. For example, the wild barley allele of QTL-4H-1 simultaneously increased leaf concentration of N, P, K and Cu. Similar effects of the same QTL were previously reported for nutrient concentrations in grains, supporting a potential parallel regulation of N, P, K and Cu in leaves and grains of HEB-25. Our study provides a new approach for nutrient assessment in large-scale field experiments to ultimately select genes and genotypes supporting plant biofortification.