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Year-independent prediction of rice grain protein content using machine learning with agronomy-aligned multi-year field data.

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Title: Year-independent prediction of rice grain protein content using machine learning with agronomy-aligned multi-year field data.
Authors: Jung HJ; Winter Crop Research Division, National Institute of Crop and Food Science, Rural Development Administration, Wanju, Republic of Korea., Lee YH; Crop Production and Physiology Division, National Institute of Crop and Food Science, Rural Development Administration, Wanju, Republic of Korea., Lee C; Central-Northern Region Crop Research Center, National Institute of Crop and Food Science, Rural Development Administration, Suwon, Republic of Korea., Cho SW; Department of Smart Agro-Industry, Gyeongsang National University, Jinju, Republic of Korea., Hwang TY; Department of Crop Science, Chungbuk National University, Cheongju, Republic of Korea.
Source: Frontiers in plant science [Front Plant Sci] 2026 Jun 01; Vol. 17, pp. 1818096. Date of Electronic Publication: 2026 Jun 01 (Print Publication: 2026).
Publication Type: Journal Article
Journal Info: Publisher: Frontiers Research Foundation Country of Publication: Switzerland NLM ID: 101568200 Publication Model: eCollection Cited Medium: Print ISSN: 1664-462X (Print) Linking ISSN: 1664462X NLM ISO Abbreviation: Front Plant Sci Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
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DbLabel: MEDLINE Ultimate
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PubType: Academic Journal
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  Data: <searchLink fieldCode="AU" term="%22Jung+HJ%22">Jung HJ</searchLink>; Winter Crop Research Division, National Institute of Crop and Food Science, Rural Development Administration, Wanju, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Lee+YH%22">Lee YH</searchLink>; Crop Production and Physiology Division, National Institute of Crop and Food Science, Rural Development Administration, Wanju, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Lee+C%22">Lee C</searchLink>; Central-Northern Region Crop Research Center, National Institute of Crop and Food Science, Rural Development Administration, Suwon, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Cho+SW%22">Cho SW</searchLink>; Department of Smart Agro-Industry, Gyeongsang National University, Jinju, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Hwang+TY%22">Hwang TY</searchLink>; Department of Crop Science, Chungbuk National University, Cheongju, Republic of Korea.
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  Data: <searchLink fieldCode="JN" term="%22101568200%22">Frontiers in plant science</searchLink> [Front Plant Sci] 2026 Jun 01; Vol. 17, pp. 1818096. <i>Date of Electronic Publication: </i>2026 Jun 01 (<i>Print Publication: </i>2026).
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Frontiers+Research+Foundation%22">Frontiers Research Foundation </searchLink><i>Country of Publication: </i>Switzerland <i>NLM ID: </i>101568200 <i>Publication Model: </i>eCollection <i>Cited Medium: </i>Print <i>ISSN: </i>1664-462X (Print) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%221664462X%22">1664462X </searchLink><i>NLM ISO Abbreviation: </i>Front Plant Sci <i>Subsets: </i>PubMed not MEDLINE
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        Value: 10.3389/fpls.2026.1818096
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      – Code: eng
        Text: English
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              Text: 2026 Jun 01
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