人工智能驱动离子胁迫耐受性种质创新: 进展与挑战

doi:10.11983/CBB25177

植物学报 ›› 2026, Vol. 61 ›› Issue (4): 588-597.DOI: 10.11983/CBB25177 cstr: 32102.14.CBB25177

人工智能驱动离子胁迫耐受性种质创新: 进展与挑战

闫慧莉¹^,²^,^†, 秦雅婷³^,^†, 周运转¹^,², 张怀⁴, 李斌峰⁴, 公保南加⁵, 张生燕⁶, 许文秀¹^,², 宋晓彦³^,^*(), 何振艳¹^,²^,^*()

¹ 中国科学院植物研究所, 饲草种质高效设计与利用全国重点实验室 , 北京 100093
² 国家植物园 , 北京 100093
³ 山西农业大学农学院 , 太原 030801
⁴ 青海牛必乐农牧科技有限公司 , 西宁 810003
⁵ 尖扎县农牧业综合服务中心 , 黄南藏族自治州 811299
⁶ 海东市平安区农业农村和科技局 , 海东 810699

收稿日期:2025-09-30 接受日期:2026-01-09 出版日期:2026-07-10 发布日期:2026-01-12
通讯作者: 宋晓彦, 何振艳
作者简介:
^† 共同第一作者
基金资助:
青海省重点研发与转化计划(2025-NK-126); 国家重点研发计划(2023YFD1200700); 国家重点研发计划(2023YFF1001304); 科技创新2030-重大项目(2023ZD04072); 国家自然科学基金(U23A20183)

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

Huili Yan¹^,², Yating Qin³, Yunzhuan Zhou¹^,², Huai Zhang⁴, Binfeng Li⁴, Nanja Gongbao⁵, Shengyan Zhang⁶, Wenxiu Xu¹^,², Xiaoyan Song³^,^*(), Zhenyan He¹^,²^,^*()

¹ State 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:2025-09-30 Accepted:2026-01-09 Online:2026-07-10 Published:2026-01-12
Contact: Xiaoyan Song, Zhenyan He

摘要/Abstract

摘要： 随着全球气候变化与人类活动加剧, 盐碱化与重金属污染等离子逆境导致的耕地退化日益严重, 威胁粮食安全。人工智能技术的快速发展为离子逆境适生种质创新提供了新范式。该文系统综述了人工智能在离子逆境适生种质创新中的主要进展与应用。在表型组学方面, 多维度表型组采集系统整合红绿蓝成像(RGB)、光谱传感、荧光成像、热红外成像与X射线荧光光谱(XRF)等技术, 结合机器学习、深度学习与多源数据融合, 可实现对离子逆境胁迫表型的早期、精准与无损识别。在智慧育种方面, 人工智能应用于基因挖掘、遗传多样性解析、多基因调控网络预测与全基因组选择等方面, 显著提升了育种效率。然而, 当前仍面临数据标准化与共享机制不完善、基因型-环境互作(G×E)模型预测精度有待提升以及技术推广成本高等挑战, 制约了人工智能技术的规模化应用。展望未来, 随着数据整合能力不断增强、算法持续优化以及技术普及程度的不断提高, 人工智能有望在退化耕地治理与农业可持续发展中发挥更大作用。

关键词: 人工智能技术, 离子逆境, 适应性育种, 高通量植物表型技术, 机器学习

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

闫慧莉, 秦雅婷, 周运转, 张怀, 李斌峰, 公保南加, 张生燕, 许文秀, 宋晓彦, 何振艳. 人工智能驱动离子胁迫耐受性种质创新: 进展与挑战. 植物学报, 2026, 61(4): 588-597.

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. Chinese Bulletin of Botany, 2026, 61(4): 588-597.

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链接本文: https://www.chinbullbotany.com/CN/10.11983/CBB25177

https://www.chinbullbotany.com/CN/Y2026/V61/I4/588

参考文献 0

	Atieno J, Li YL, Langridge P, Dowling K, Brien C, Berger B, Varshney RK, Sutton T (2017). Exploring genetic variation for salinity tolerance in chickpea using image—based phenotyping. Sci Rep 7, 1300.
	Awlia M, Alshareef N, Saber N, Korte A, Oakey H, Panzarová K, Trtílek M, Negrão S, Tester M, Julkowska MM (2021). Genetic mapping of the early responses to salt stress in Arabidopsis thaliana. Plant J 107, 544-563.
	Chaerle L, Van Der Straeten D (2000). Imaging techniques and the early detection of plant stress. Trends Plant Sci 5, 495-501.
	Chen SK, Zhang H, Gao SQ, He KH, Yu TX, Gao S, Wang JK, Li HH (2025). Unveiling salt tolerance mechanisms in plants: integrating the KANMB machine learning model with metabolomic and transcriptomic analysis. Adv Sci 12, 2417560.
	Deng X, Pang HY, Fu Y, Zhang AW, Zhang JY, Chong K (2025). Targeted integrating hyperspectral and metabolomic data with spectral indices and metabolite content models for efficient salt—tolerant phenotype discrimination in Medicago truncatula. Plant Phenom 7, 100020.
	Deshmukh R, Sonah H, Patil G, Chen W, Prince S, Mutava R, Vuong T, Valliyodan B, Nguyen HT (2014). Integrating omic approaches for abiotic stress tolerance in soybean. Front Plant Sci 5, 244.
	El—Hendawy SE, Hassan WM, Refay Y, Schmidhalter U (2017). On the use of spectral reflectance indices to assess agro—morphological traits of wheat plants grown under simulated saline field conditions. J Agron Crop Sci 203, 406-428.
	Fu XQ, Han B, Liu SY, Zhou JY, Zhang HW, Wang HB, Zhang H, Ouyang ZQ (2022). WSVAS: a YOLOv4—based phenotyping platform for automatically detecting the salt tolerance of wheat based on seed germination vigour. Front Plant Sci 13, 1074360.
	Garriga M, Retamales JB, Romero—Bravo S, Caligari PDS, Lobos GA (2014). Chlorophyll, anthocyanin, and gas exchange changes assessed by spectroradiometry in Fragaria chiloensis under salt stress. J Integr Plant Biol 56, 505-515.
	Ge HY, Ji XD, Lu XJ, Lv M, Jiang YY, Jia ZY, Zhang Y (2023). Identification of heavy metal pollutants in wheat by THz spectroscopy and deep support vector machine. Spectrochim Acta A Mol Biomol Spectrosc 303, 123206.
	Gebremedhin A, Li YJ, Shunmugam ASK, Sudheesh S, Valipour—Kahrood H, Hayden MJ, Rosewarne GM, Kaur S (2024). Genomic selection for target traits in the Australian lentil breeding program. Front Plant Sci 14, 1284781.
	Hou DY, Jia XY, Wang LW, Mcgrath SP, Zhu YG, Hu Q, Zhao FJ, Bank MS, O’connor D, Nriagu J (2025). Global soil pollution by toxic metals threatens agriculture and human health. Science 388, 316-321.
	Kurunc A, Tezcan NY, Khan A (2025). Determination of salt stress by plant leaf temperature and thermal imaging in black cumin grown under different salt sources. Commun Soil Sci Plan 56, 517-527.
	Li JM, Yang ZH, Zhao YR, Yu K (2024). SERS combined with the SAE—CNN model for estimating apple rootstocks under heavy metal copper stress. Measurement 224, 113911.
	Li JY, Zeng HY, Huang CX, Wu LB, Ma J, Zhou BB, Ye DP, Weng HY (2023). Noninvasive detection of salt stress in cotton seedlings by combining multicolor fluorescence—multispectral reflectance imaging with EfficientNet—OB2. Plant Phenom 5, 0125.
	Li XQ, Liu XN, Liu ML, Wang CC, Xia XP (2015). A hyperspectral index sensitive to subtle changes in the canopy chlorophyll content under arsenic stress. Int J Appl Earth Obs Geoinform 36, 41-53.
	Lin FY, Yin XJ, Liang YN, Huang JC (2020). Analysis of in situ distribution of inorganic elements in plants by Micro—XRF. Chin Bull Bot 55, 733-739. (in Chinese with English abstract)
	林梵宇, 尹希杰, 梁毓娜, 黄杰超 (2020). 微区XRF技术分析无机元素在植物中的原位分布. 植物学报 55, 733-739.
	Liu JN, Yan LP, Chai ZJ, Liang Q, Dong YH, Wang CX, Li XC, Li CY, Mu YT, Gong AD, Yang JF, Li JX, Yang KQ, Wu DJ, Fang HC (2025a). Pan—genome analyses of 11 Fraxinus species provide insights into salt adaptation in ash trees. Plant Commun 6, 101137.
	Liu ZH, Jiang QY, Ji YS, Liu R, Liu HQ, Ya X, Liu ZX, Wang ZR, Jin XL, Yang T (2025b). Assessment of salt tolerance in peas using machine learning and multi—sensor data. Plant Stress 17, 100902.
	Lu Y, Nie LJ, Guo XY, Pan TT, Chen RQ, Liu XY, Li XL, Li TQ, Liu F (2024). Rapid assessment of heavy metal accumulation capability of Sedum alfredii using hyperspectral imaging and deep learning. Ecotox Environ Safe 282, 116704.
	Ma C, Xin MM, Feldmann KA, Wang XF (2014). Machine learning—based differential network analysis: a study of stress—responsive transcriptomes in Arabidopsis. Plant Cell 26, 520-537.
	Ma YX, Tashpolat N (2023). Current status and development trend of soil salinity monitoring research in China. Sustainability 15, 5874.
	Moghimi A, Yang C, Marchetto PM (2018). Ensemble feature selection for plant phenotyping: a journey from hyperspectral to multispectral imaging. IEEE Access 6, 56870-56884.
	Peng YF, Sun J, Cai ZT, Shi L, Wu XH, Dai CX, Xie YB (2025). Copper stress levels classification in oilseed rape using deep residual networks and hyperspectral false—color images. Horticulturae 11, 840.
	Pii Y, Orzes G, Mazzetto F, Sambo P, Cesco S (2024). Advances in viticulture via smart phenotyping: current progress and future directions in tackling soil copper accumulation. Front Plant Sci 15, 1459670.
	Ren ZW, Yang M, McKenna BA, Lian XM, Zhao FJ, Kopittke PM, Lombi E, Wang P (2023). Fast X—ray fluorescence microscopy provides high—throughput phenotyping of element distribution in seeds. Plant Physiol 191, 1520-1534.
	Sighicelli M, Guarneri M (2014). Assessing the poplar photochemical response to high zinc concentrations by image processing and statistical approach. Photosynth Res 122, 315-322.
	Sirault XRR, James RA, Furbank RT (2009). A new screening method for osmotic component of salinity tolerance in cereals using infrared thermography. Funct Plant Biol 36, 970-977.
	Uygun S, Seddon AE, Azodi CB, Shiu SH (2017). Predictive models of spatial transcriptional response to high salinity. Plant Physiol 174, 450-464.
	Wei H, Wang XM, Zhang ZP, Yang LB, Zhang QQ, Li YL, He HY, Chen DD, Zhang B, Zheng CK, Leng Y, Cao XL, Cui Y, Shi CL, Liu YF, Lv Y, Ma J, He WC, Liu XP, Xu Q, Yuan QL, Yu XM, Wang TY, Qian HG, Li XX, Zhang BT, Zhang H, Chen W, Guo ML, Dai XF, Wang YX, Zheng XM, Guo LB, Xie XZ, Qian Q, Shang LG (2024). Uncovering key salt—tolerant regulators through a combined eQTL and GWAS analysis using the super pan—genome in rice. Natl Sci Rev 11, nwae043.
	Xu CQ, Sun L, Zhang LD, Guo ZJ, Wang JC, Zhuang LH, Ma DN, Song LY, Li J, Ding QS, Tang HC, Zheng HL (2025). Machine learning based prediction by PlantCdMiner and experimental validation of cadmium—responsive genes in plants. J Hazard Mater 494, 138582.
	Yan HL, Guo HY, Xu WX, Dai CH, Kimani W, Xie JY, Zhang HZF, Li T, Wang F, Yu YJ, Ma M, Hao ZF, He ZY (2023). GWAS—assisted genomic prediction of cadmium accumulation in maize kernel with machine learning and linear statistical methods. J Hazard Mater 441, 129929.
	Yang MG, Chen SK, Huang ZP, Gao S, Yu TX, Du TT, Zhang H, Li X, Liu CM, Chen SH, Li HH (2023). Deep learning—enabled discovery and characterization of HKT genes in Spartina alterniflora. Plant J 116, 690-705.
	Yang ZM, Li AL, Chen JQ, Dai ZG, Su JG, Deng CH, Ye GA, Cheng CH, Tang Q, Zhang XY, Xu Y, Chen XJ, Wu BB, Zhang ZH, Zheng XY, Yang L, Xiao L (2024). Machine learning phenotyping and GWAS reveal genetic basis of Cd tolerance and absorption in jute. Environ Pollut 362, 124918.
	Yao Z, Yao MT, Wang C, Li K, Guo JH, Xiao YJ, Yan JB, Liu JX (2025). GEFormer: a genotype—environment interaction—based genomic prediction method that integrates the gating multilayer perceptron and linear attention mechanisms. Mol Plant 18, 527-549.
	Zhang JH, Zhang SY, Yang YB, Yan WL, Lin XM, Wilson LT, Liu B, Liu LL, Xiao LJ, Zhu Y, Cao WX, Tang L (2025). Integrating crop models, single nucleotide polymorphism, and climatic indices to develop genotype—environment interaction model: a case study on rice flowering time. Plant Phenom 7, 100007.

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人工智能驱动离子胁迫耐受性种质创新: 进展与挑战

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

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