Chinese Bulletin of Botany >

2026 , Vol. 61 >Issue 4: 588 - 597

DOI: https://doi.org/10.11983/CBB25177

AI-driven Innovation of Ion Stress-tolerant Germplasm: Progress and Challenges

  • Huili Yan 1, 2 ,
  • Yating Qin 3 ,
  • Yunzhuan Zhou 1, 2 ,
  • Huai Zhang 4 ,
  • Binfeng Li 4 ,
  • Nanja Gongbao 5 ,
  • Shengyan Zhang 6 ,
  • Wenxiu Xu 1, 2 ,
  • Xiaoyan Song , 3, * ,
  • Zhenyan He , 1, 2, *
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  • 1State Key Laboratory of Forage Breeding—by—Design and Utilization, Chinese Academy of Sciences , Beijing 100093, China
  • 2 National Botanical Garden , Beijing 100093, China
  • 3 College of Agriculture, Shanxi Agricultural University , Taiyuan 030801, China
  • 4 Qinghai Cattle Pierer Husbandry Technology Co. Ltd. , Xining 810003, China
  • 5 Jianzha County Agriculture & Animal Husbandry Integrated Service Center , Huangnan Zang Autonomous Prefecture 811299, China
  • 6 Bureau of Agriculture, Rural Areas and Science & Technology, Ping’an District of Haidong City , Haidong 810699, China

Received date: 2025-09-30

  Accepted date: 2026-01-09

  Online published: 2026-01-12

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Abstract

Global climate change and intensive human activities have led to increasing degradation of arable land due to ionic stress, such as salinization and heavy metal contamination, posing a huge threat to world food security. The rapid development of artificial intelligence (AI) technology offers a new approach for germplasm innovation for stress adaptation. This review systematically summarizes key advances and applications of AI in germplasm innovation for ionic stress tolerance. In the field of phenomics, multi-dimensional phenotyping systems integrate technologies including red-green-blue imaging, spectral sensing, fluorescence imaging, thermal infrared imaging, and X-ray fluorescence spectrometer. These are coupled with machine learning, deep learning, and multi-source data fusion to enable early, accurate, and non-invasive identification of ionic stress responses. In the field of smart breeding, machine learning accelerates gene discovery, along with pangenomics to elucidate genetic diversity. AI-driven prediction of multi-gene regulatory networks and genomic selection have significantly improved breeding efficiency. However, challenges such as insufficient data standardization and sharing, limited prediction accuracy of genotype-environment interaction (G×E) models, and high technology promotion costs are still constraining the large-scale application of the AI-enabled technologies. We expect that future efforts with focus on data integration, algorithm optimization, and technology popularization will allow AI to play a greater role in the remediation of degraded farmland and in achieving sustainable agricultural development.

Key words： artificial intelligence technology; ionic stress; adaptive breeding; high-throughput plant phenotyping technology; machine learning

Cite this article

Huili Yan , Yating Qin , Yunzhuan Zhou , Huai Zhang , Binfeng Li , Nanja Gongbao , Shengyan Zhang , Wenxiu Xu , Xiaoyan Song , Zhenyan He . AI-driven Innovation of Ion Stress-tolerant Germplasm: Progress and Challenges[J]. Chinese Bulletin of Botany, 2026 , 61(4) : 588 -597 . DOI: 10.11983/CBB25177

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