Phenomics assisted trait dissection and genotype selection for improved nitrogen use efficiency in rice

Phenomics assisted trait dissection and genotype selection for improved nitrogen use efficiency in rice

Modern crop phenomics and current advances in machine learning (ML) models are promising tools to speed up genetic improvement of nitrogen use efficiency (NUE) in rice. However, high-throughput phenotyping of rice genotypes and NUE trait dissection for rice improvement is scant. A large-scale mesoco...

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Main Authors: Nguyen Trung Duc, Dhandapani Raju, Sudhir Kumar, Renu Pandey, Ranjith Kumar Ellur, Gopala Krishnan S, Chandan Vishwakarma, Elangovan Allimuthu, Biswabiplab Singh, Ambika Rajendran, Rabi Narayan Sahoo, Viswanathan Chinnusamy
Format: Article
Language:English
Published: Elsevier 2025-09-01
Series:Plant Stress
Subjects:
HTTP
Phenomics
Nitrogen use efficiency (NUE)
Rice
NUEb
Online Access:http://www.sciencedirect.com/science/article/pii/S2667064X25001770
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Summary:Modern crop phenomics and current advances in machine learning (ML) models are promising tools to speed up genetic improvement of nitrogen use efficiency (NUE) in rice. However, high-throughput phenotyping of rice genotypes and NUE trait dissection for rice improvement is scant. A large-scale mesocosm experiment was conducted for precision phenotyping of 300 diverse rice genotypes under nitrogen sufficient and deficit-condition. Multispectral imaging sensors (RGB, IR &amp; NIR) were used to study the phenome-wide dynamic response of the rice genotypes at multi-temporal time-points (68, 75 &amp; 83 DAT). The analysis of manual (30) and image (68) traits suggested uncovering large &amp; dynamic phenotypic responses of diverse rice genotypes irrespective of treatment conditions. A novel trait dissection strategy was adopted to successfully identify two promising i-traits of interest (NUpE &amp; NUEb) for improving NUE in rice. ML prediction models were developed for non-destructive prediction of NUEb with a high confidence level (R2=0.98 % at p < 0.001). Finally, phenomics-assisted genotype selection was established, and three superior rice donors (IC463705, Suweon &amp; Cauvery) were identified. In summary, this experiment demonstrated the potential use of phenomics and ML techniques for breeding rice crops with enhanced NUE traits.
ISSN:2667-064X

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