Objective Phenotyping of Root System Architecture Using Image Augmentation and Machine Learning in Alfalfa (Medicago sativa L.)

Xu, Zhanyou; York, Larry M.; Seethepalli, Anand; Bucciarelli, Bruna; Ho, Cheng; Samac, Deborah A.

doi:10.34133/2022/9879610

Cited by 22 publications

(30 citation statements)

References 77 publications

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“…Phenotypic breeding based on root traits lagged behind that based on aboveground traits of alfalfa as the root system was hidden underground and di cult to measure (Xu et al 2022). Besides, the characteristics of alfalfa such as heterogeneous pollination, polyploid inheritance and selfincompatibility hindered the molecular breeding of alfalfa (Yang et al 2022).…”

Section: Discussionmentioning

confidence: 99%

Root system architecture and anatomical traits variability of alfalfa at the seeding stage

Pan¹,

Wang²,

Wei³

et al. 2023

Preprint

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Background and aims Alfalfa (Medicago sativa. L) growth is largely restricted by abiotic stress such as drought and nutrient deficiency. Identifying root architectural and anatomical characteristics is of great significance for breeding alfalfa genotypes with improved adaptation to adverse environments. Methods Using nutrient solution sand culture method and visual rhizobox cultivation system, we explored the variability in root system architecture (RSA) and anatomy of 53 alfalfa genotypes at the seedling stage. Results Among 44 measured traits, 23 root traits, nitrogen (N) and phosphorus (P) uptake exhibited larger coefficients of variation (CVs ≥ 0.25) across tested genotypes. The variation degrees of local root traits and root anatomical traits were larger than global root traits. Twenty-five traits with CVs ≥ 0.25 constituted 6 principal components (eigenvalues > 1) accounting for 88.9% of the total genotypic variation. Total root length, root length in diameter thin, root tips number, maximal root depth, root length and root tips number in different soil layers were positively correlated with shoot dry mass and root dry mass (P ≤ 0.05). Total stele area (P ≤ 0.05) and xylem vessel area (P ≤ 0.001) were positively correlated with N and P uptake. Conclusion The tested alfalfa genotypes showed larger variation in local root morphological and anatomical traits at the seedling stage. Some important root traits, including root length, root length in diameter thin, root tips number, maximal root depth, total stele area and xylem vessel area have potential function on breeding alfalfa genotypes with improved adaption to abiotic stress.

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Section: Discussionmentioning

confidence: 99%

Root system architecture and anatomical traits variability of alfalfa at the seeding stage

Pan¹,

Wang²,

Wei³

et al. 2023

Preprint

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“…6). We are not aware of other root phenomics workflows that aim to distinguish basal and lateral ARs, although several distinguished root type for non-adventitious roots, such as for alfalfa (Xu et al ., 2022), Arabidopsis, wheat and Brassica (Yasrab et al ., 2019).…”

Section: Discussionmentioning

confidence: 99%

GWAS identifies candidate genes controlling adventitious rooting in Populus trichocarpa

Nagle

Yuan

Kaur

et al. 2022

Preprint

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Adventitious rooting is critical to the propagation, breeding, and genetic engineering or editing of trees. The capacity for plants to undergo these processes is highly heritable; however, the basis of its genetic variation is largely uncharacterized. To identify genetic regulators of these processes, we performed a genome-wide association study (GWAS) using 1,148 genotypes of Populus trichocarpa. GWAS are often limited by the abilities of researchers to collect precise phenotype data on a high-throughput scale; to help overcome this limitation, we developed a computer vision system to measure an array of traits related to adventitious root development in poplar, including temporal measures of lateral and basal root length and area. GWAS was performed using multiple methods and significance thresholds to handle non-normal phenotype statistics, and to gain statistical power. These analyses yielded a total of 277 unique associations, suggesting that genes that control rooting include regulators of hormone signaling, cell division and structure, and reactive oxygen species signaling. Genes related to other processes with known roles in root development, and numerous genes with uncharacterized functions and/or cryptic roles, were also identified. These candidates provide targets for functional analysis, including physiological and epistatic analyses, to better characterize the complex polygenic regulation of adventitious rooting.

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“…Assessment of RSA is as crucial as the evaluation of aboveground parts of the plants since plant performance relies on its root architecture and functionality [65]. In the last decade, combining the ML platform with root phenotyping has allowed scientists to understand root development, its interaction with different environments, and the classification of the RSA phenotypes for genomic breeding [66]. The present study employed MLP, GP, RF, and XGBoost to predict RSA under the application of FPH, and all the models with the R 2 values between 0.75 to 0.95 showed high performance for the parameters, including projected area, surface area, root tips, and root forks.…”

Section: Performance Of Modeling Of Root System Architecturementioning

confidence: 99%

Effects of Protein Hydrolysate Derived from Anchovy By-Product on Plant Growth of Primrose and Root System Architecture Analysis with Machine Learning

Tütüncü

2024

Horticulturae

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Protein hydrolysates (PHs) derived from waste materials are promising for sustainable practices in agricultural production. This study evaluated the effects of PH enzymatically derived from anchovy by-products on the root system architecture (RSA) and aboveground development of potted primrose. The plants were treated with 0.5, 1.0, and 1.5 g/L concentrations of PH by drenching with 100 mL/pot at two-week intervals and irrigated once a week with 100 mL/pot during winter and twice weekly during spring. The results revealed that the 1.5 g/L treatment statistically significantly improved dry weight and leaf area, while the highest leaf chlorophyll content was observed with the 1.0 g/L treatment. The treatments did not influence leaf and flower numbers. Treatment with 1.0 g/L produced the most substantial improvement in root surface area, projected area, volume, length, tips, and forks. Additionally, the study employed machine learning (ML) algorithms, including GP, RF, XGBoost, and an ANN-based MLP. The input variables (root surface area, projected area, volume, length, tips, and forks) were assessed to model and predict the root traits. The ML and ANN algorithms’ R-squared rates were noted in the following order: MLP > GP > RF > XGBoost. These outcomes hold significant implications for enhancing primrose growth.

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Objective Phenotyping of Root System Architecture Using Image Augmentation and Machine Learning in Alfalfa (Medicago sativa L.)

Cited by 22 publications

References 77 publications

Root system architecture and anatomical traits variability of alfalfa at the seeding stage

Root system architecture and anatomical traits variability of alfalfa at the seeding stage

GWAS identifies candidate genes controlling adventitious rooting in Populus trichocarpa

Effects of Protein Hydrolysate Derived from Anchovy By-Product on Plant Growth of Primrose and Root System Architecture Analysis with Machine Learning

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