AI4Root: 人工智能驱动的植物根系研究进展

doi:10.11983/CBB25192

植物学报 ›› 2026, Vol. 61 ›› Issue (4): 549-714.DOI: 10.11983/CBB25192 cstr: 32102.14.CBB25192

• 特邀综述 • 下一篇

AI4Root: 人工智能驱动的植物根系研究进展

方博², 高树琴¹, 段世名³, 马慧敏⁴, 赵洪龙¹, 蒋浩¹, 杨艳敏⁵, 龙隆¹, 贺祖光¹, 张玉成¹, 郑聪聪¹^,^*()

¹ 中国科学院计算技术研究所 , 北京 100190
² 德国于利希研究中心植物科学研究所(IBG—2), 于利希 52428
³ 中国农业大学水利与土木工程学院 , 北京 100193
⁴ 吉林农业大学农学院 , 长春 130118
⁵ 中国科学院遗传与发育生物学研究所农业资源研究中心 , 石家庄 050021

收稿日期:2025-10-27 接受日期:2026-01-09 出版日期:2026-07-10 发布日期:2026-01-22
通讯作者: 郑聪聪
基金资助:
中国科学院科研项目

Al4Root: a Framework to Advance Al-Driven Plant Root Research

Bo Fang², Shuqin Gao¹, Shiming Duan³, Huimin Ma ⁴, Honglong Zhao¹, Hao Jiang ¹, Yanmin Yang⁵, Long Long¹, Zuguang He¹, Yucheng Zhang ¹, Congcong Zheng^1*

1 Institute of Computing Technology, Chinese Academy of Sciences,Beijling 100190, China;

2 Institute of Plant Sciences(IBG-2),Forschungszentrum Jilich , Jilich 52428,Germany;

3 college of Water Conservancy and Civil Engineering, ChinaAgricultural University (CAU), Beijing 100193,China;

4 College of Agriculture,Jilin Agricultural University, Changchun 130118, China;

5 Center for Agricultural Resources Research, Iinstitute of Genetics and Developmental Biology, Chinese Academy of Sciences, Shijiazhuang 050021,China

Received:2025-10-27 Accepted:2026-01-09 Online:2026-07-10 Published:2026-01-22

摘要/Abstract

摘要： 随着科学智能(AI for science, AI4Science) (即用人工智能技术来驱动科学研究)作为科学研究新范式的兴起, 人工智能(AI)正深刻推动植物生命系统的研究向数据驱动与智能化转型。根系作为植物获取水分和养分、感知环境胁迫并参与地上-地下互作的关键器官, 对作物产量和生态系统的功能起着决定性作用。然而, 由于根系生长在土壤中, 其复杂表型难以直接观测, 导致对根系结构和功能的认识长期滞后于地上部分。人工智能的发展为破解这一“地下黑箱”提供了新工具和新路径。如何以人工智能为核心驱动力, 整合多源的根系与环境数据、前沿算法及根系科学知识体系, 进而揭示根系结构与功能的关键关系, 成为当前根系研究的前沿方向。该文从数据获取、结构建模、机制推理和管理决策等方面系统综述了AI在根系研究中的主要应用与发展趋势, 基于AI的图像分析可实现根系结构的自动识别与量化; 建模与数据融合可揭示根系-土壤多尺度、多过程的互作。虽然AI在根系表型测定、构型模拟、分泌物及微生物互作解析等方面显示出巨大潜力, 为深入理解根系功能提供了前所未有的手段。然而, AI应用于根系研究仍面临田间高质量数据获取困难、不同实验尺度之间的数据融合不足, 以及根系-土壤-环境耦合过程的模拟与刻画不充分等挑战。该文对根系研究如何依托多源数据深度整合、跨学科算法优化及数字农业平台协同应用进行探讨, 以推动相关关键核心技术突破, 促进根系科学向智能化、精准化方向发展, 为新一代根系育种和资源高效利用提供理论依据与技术支撑。

关键词: 根系, 人工智能, 深度学习, 智慧农业, AI4Root

Abstract: With Al for Science (A4Science) emerging as a new paradigm for scientific research, artificial intelligence (AI) is driving a transition toward data-driven and intelligent research in plant science. Roots, as the key organs responsible forwater and nutrient acquisition, environmental sensing, and above-below ground interactions, are decisive for crop productivity and ecosystem functioning. However, due to their hidden growth in soil, structural complexity, and limited observability, our understanding of root structure and function has long lagged behind that of above ground parts. Recent advances in Al have provided new tools and pathways for decoding the "underground black box". Using Al as a key driver to integrate multi-source root and environmental data, advanced algorithms, and the broader knowledge base of root science has emergedas a frontier direction in below ground research. In this context, we systematically review the major applications and emerging trends of Al in root studies, encompassing data acquisition, structural modeling, mechanistic inference, and management decision support. Al-based image analysis enables automated recognition and quantification of root architectures, while modeling and data-fusion approaches reveal the multi-scale and multi-process interactions between roots and soil. Al applications in root phenotyping, architectural simulation, and deciphering root exudate-microbiome interactions are also showing great potential, offering unprecedented tools to advance our understanding of root function. However, the application of Al in root research stil faces several challenges, including the difficulty of acquiring high-quality field data, insufficient integration across experimental scales, and limited capacity to model the complexity of root-soil-environment interactions. Looking ahead, the deep integration of multi-source data, cross-disciplinary algorithmic advancements, and the coordinated development of digital agriculture platforms are expected to propel root science into a new era of intelligent research, offering innovative pathways for crop improvement and the advancement of smart agriculture.

Key words: roots, artificial intelligence, deep learning, smart agriculture, Al4Root

方博, 高树琴, 段世名, 马慧敏, 赵洪龙, 蒋浩, 杨艳敏, 龙隆, 贺祖光, 张玉成, 郑聪聪. AI4Root: 人工智能驱动的植物根系研究进展. 植物学报, 2026, 61(4): 549-714.

Bo Fang, Shuqin Gao, Shiming Duan, Huimin Ma, Honglong Zhao, Hao Jiang, Yanmin Yang, Long Long, Zuguang He, Yucheng Zhang , Congcong Zheng. Al4Root: a Framework to Advance Al-Driven Plant Root Research. Chinese Bulletin of Botany, 2026, 61(4): 549-714.

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链接本文: https://www.chinbullbotany.com/CN/10.11983/CBB25192

https://www.chinbullbotany.com/CN/Y2026/V61/I4/549

参考文献 0

	Alahmad S, Smith D, Katsikis C, Aldiss Z, Brunner SM, Meer SV, Meijer L, Heidariask B, Chenu K, Chapman S, Potgieter AB, Wasson A, Baraibar S, Godoy J, Moody D, Robinson H, Hickey LT (2025). Phenotyping the hidden half: combining UAV phenotyping and machine learning to predict barley root traits in the field. J Exp Bot 76, 5161-5178.
	Amtmann A, Bennett MJ, Henry A (2022). Root phenotypes for the future. Plant Cell Environ 45, 595-601.
	Arsenault JL, Poulcur S, Messier C, Guay R (1995). WinRHlZO^TM, a root—measuring system with a unique overlap correction method . HortScience 30, 906.
	Atkinson JA, Pound MP, Bennett MJ, Wells DM (2019). Uncovering the hidden half of plants using new advances in root phenotyping. Curr Opin Biotechnol 55, 1-8.
	Baker DN, Selzner T, Göbbert JH, Scharr H, Riedel M, Hvannberg E Þ, Schnepf A, Zielasko D (2025). VRoot: a VR—based application for manual root system architecture reconstruction. Plant Phenomics 7, 100013.
	Bates GH (1937). A device for the observation of root growth in the soil. Nature 139, 966-967.
	Calderwood A, Siles L, Eastmond PJ, Kurup S, Morris RJ (2023). A causal inference and Bayesian optimisation framework for modelling multi—trait relationships: proof—of—concept using Brassica napus seed yield under controlled conditions . PLoS One 18, e0290429.
	Cannon WA (1911). The Root Habits of Desert Plants. Washington: Carnegie Institution of Washington. pp. 10-11.
	Cao YR, Tian DM, Tang ZX, Liu XN, Hu WJ, Zhang Z, Song SH (2024). OPIA: an open archive of plant images and related phenotypic traits. Nucleic Acids Res 52, D1530-D1537.
	Changdar S, Popovic O, Wacker TS, Markussen B, Dam EB, Thorup—Kristensen K (2023). Non—invasive phenotyping for water and nitrogen uptake by deep roots explored using machine learning. Plant Soil 493, 603-616.
	Chen HY, Xue L, Liu L, Zou GF, Han JX, Dong YB, Cong MZ, Liu YT, Hosseini—Nasab SM (2025). Physics—informed graph neural network for predicting fluid flow in porous media. Pet Sci 22, 4240-4253.
	Chen WL, Koide RT, Adams TS, DeForest JL, Cheng L, Eissenstat DM (2016). Root morphology and mycorrhizal symbioses together shape nutrient foraging strategies of temperate trees. Proc Natl Acad Sci USA 113, 8741-8746.
	Cranmer K, Brehmer J, Louppe G (2020). The frontier of simulation—based inference. Proc Natl Acad Sci USA 117, 30055-30062.
	de S. Nóia—Júnior R, Ruane AC, Athanasiadis IN, Ewert F, Harrison MT, Jägermeyr J, Martre P, Müller C, Palosuo T, Salmerón M, Webber H, Maccarthy DS, Asseng S (2025). Crop models for future food systems. One Earth 8, 101487.
	Dhal SB, Mahanta S, Moore JM, Kalafatis S (2024). Machine learning—based analysis of nutrient and water uptake in hydroponically grown soybeans. Sci Rep 14, 24337.
	Evangelisti E, Turner C, McDowell A, Shenhav L, Yunusov T, Gavrin A, Servante EK, Quan C, Schornack S (2021). Deep learning—based quantification of arbuscular mycorrhizal fungi in plant roots. New Phytol 232, 2207-2219.
	Farooq MA, Gao S, Hassan MA, Huang ZP, Rasheed A, Hearne S, Prasanna B, Li XH, Li HH (2024). Artificial intelligence in plant breeding. Trends Genet 40, 891-908.
	Freschet GT, Roumet C, Comas LH, Weemstra M, Bengough AG, Rewald B, Bardgett RD, De Deyn GB, Johnson D, Klimešová J, Lukac M, McCormack ML, Meier IC, Pagès L, Poorter H, Prieto I, Wurzburger N, Zadworny M, Bagniewska—Zadworna A, Blancaflor EB, Brunner I, Gessler A, Hobbie SE, Iversen CM, Mommer L, Picon—Cochard C, Postma JA, Rose L, Ryser P, Scherer—Lorenzen M, Soudzilovskaia NA, Sun T, Valverde—Barrantes OJ, Weigelt A, York LM, Stokes A (2021). Root traits as drivers of plant and ecosystem functioning: current understanding, pitfalls and future research needs. New Phytol 232, 1123-1158.
	Gaggion N, Ariel F, Daric V, Lambert É, Legendre S, Roulé T, Camoirano A, Milone DH, Crespi M, Blein T, Ferrante E (2021). ChronoRoot: high—throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture. Gigascience 10, giab052.
	Gaitan NC, Batinas BI, Ursu C, Crainiciuc FN (2025). Integrating artificial intelligence into an automated irrigation system. Sensors 25, 1199.
	Ge JK, Zhang RN, He YJ, Sun ZZ, Li Q, Jin SC, Cai J, Zhou Q, Huang M, Wang X, Jiang D (2025). faCRSA: an automated pipeline for high—throughput analysis of crop root system architecture. Crop J 13, 1919-1927.
	Gu HJ, Wang YZ, Peruzzo L, Li BR, Lu Y, Liu XW (2025). Linking winter wheat (Triticum aestivum L.) root traits and root water uptake with electrical resistivity tomography . Agric Water Manag 307, 109247.
	Guerrero—Ramírez NR, Mommer L, Freschet GT, Iversen CM, McCormack ML, Kattge J, Poorter H, van der Plas F, Bergmann J, Kuyper TW, York LM, Bruelheide H, Laughlin DC, Meier IC, Roumet C, Semchenko M, Sweeney CJ, van Ruijven J, Valverde—Barrantes OJ, Aubin I, Catford JA, Manning P, Martin A, Milla R, Minden V, Pausas JG, Smith SW, Soudzilovskaia NA, Ammer C, Butterfield B, Craine J, Cornelissen JHC, de Vries FT, Isaac ME, Kramer K, König C, Lamb EG, Onipchenko VG, Peñuelas J, Reich PB, Rillig MC, Sack L, Shipley B, Tedersoo L, Valladares F, van Bodegom P, Weigelt P, Wright JP, Weigelt A (2021). Global root traits (GRooT) database. Glob Ecol Biogeogr 30, 25-37.
	Harfouche AL, Jacobson DA, Kainer D, Romero JC, Harfouche AH, Scarascia Mugnozza G, Moshelion M, Tuskan GA, Keurentjes JJB, Altman A (2019). Accelerating climate resilient plant breeding by applying next—generation artificial intelligence. Trends Biotechnol 37, 1217-1235.
	Herrero—Huerta M, Raumonen P, Gonzalez—Aguilera D (2022). 4DRoot: root phenotyping software for temporal 3D scans by X—ray computed tomography. Front Plant Sci 13, 986856.
	Hodge A, Berta G, Doussan C, Merchan F, Crespi M (2009). Plant root growth, architecture and function. Plant Soil 321, 153-187.
	Hruska J, Čermák J, Šustek S (1999). Mapping tree root systems with ground—penetrating radar. Tree Physiol 19, 125-130.
	Jafar A, Bibi N, Naqvi RA, Sadeghi—Niaraki A, Jeong D (2024). Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations. Front Plant Sci 15, 1356260.
	James BR, Bartlett RJ, Amadon JF (1985). A root observation and sampling chamber (rhizotron) for pot studies. Plant Soil 85, 291-293.
	Kamil H, Soulaïmani A, Beljadid A (2025). Physics—informed neural operators for efficient modeling of infiltration in porous media. J Comput Phys 538, 114156.
	Khaliq A, Khan A, Jan S, Umair M, Gulshair A, Ali A, Ali Shah U (2025). AI—driven smart agriculture: an integrated approach for soil analysis, irrigation, and crop—fertilizer recommendations. IEEE Access 13, 141124-141138.
	Khan MA, Gemenet DC, Villordon A (2016). Root system architecture and abiotic stress tolerance: current knowledge in root and tuber crops. Front Plant Sci 7, 1584.
	Klepper B, Kaspar TC (1994). Rhizotrons: their development and use in agricultural research. Agron J 86, 745-753.
	Köckenberger W, Pope JM, Xia Y, Jeffrey KR, Komor E, Callaghan PT (1997). A non—invasive measurement of phloem and xylem water flow in castor bean seedlings by nuclear magnetic resonance microimaging. Planta 201, 53-63.
	Li AC (2023). Image Segmentation and Phenotypic Analysis of In Situ Root Systems in Crops . PhD dissertation. Baoding: Hebei Agricultural University. pp. 1-103. (in Chinese with English abstract)
	李安昌 (2023). 作物原位根系图像分割及表型分析技术研究. 博士论文. 保定: 河北农业大学. pp. 1-103.
	Li GJ (2024). AI4R: the fifth scientific research paradigm. Bull Chin Acad Sci 39, 1-9. (in Chinese with English abstract)
	李国杰 (2024). 智能化科研(AI4R): 第五科研范式. 中国科学院院刊 39, 1-9.
	Li X, Yu HC (2024). A new paradigm of life science research driven by artificial intelligence. Bull Chin Acad Sci 39, 50-58. (in Chinese with English abstract)
	李鑫, 于汉超 (2024). 人工智能驱动的生命科学研究新范式. 中国科学院院刊 39, 50-58.
	Liu YQ, Wang AW, Li B, Šimůnek J, Liao RK (2024). Combining mathematical models and machine learning algorithms to predict the future regional—scale actual transpiration by maize. Agric Water Manag 303, 109056.
	Lopez G, Ahmadi SH, Amelung W, Athmann M, Ewert F, Gaiser T, Gocke MI, Kautz T, Postma J, Rachmilevitch S, Schaaf G, Schnepf A, Stoschus A, Watt M, Yu P, Seidel SJ (2023). Nutrient deficiency effects on root architecture and root—to—shoot ratio in arable crops. Front Plant Sci 13, 1067498.
	Lynch J (1995). Root architecture and plant productivity. Plant Physiol 109, 7-13.
	Lynch JP (2013). Steep, cheap and deep: an ideotype to optimize water and N acquisition by maize root systems. Ann Bot 112, 347-357.
	Lynch JP (2019). Root phenotypes for improved nutrient capture: an underexploited opportunity for global agriculture. New Phytol 223, 548-564.
	Lynch JP (2022). Harnessing root architecture to address global challenges. Plant J 109, 415-431.
	Macha P (2023). Root2Graph: a Graph—based Semantic Segmentation Architecture for Plant Roots. Master’s thesis. Saskatoon: University of Saskatchewan. pp. 1-77.
	McCormack ML, Guo DL, Iversen CM, Chen WL, Eissenstat DM, Fernandez CW, Li L, Ma CG, Ma ZQ, Poorter H, Reich PB, Zadworny M, Zanne A (2017). Building a better foundation: improving root—trait measurements to understand and model plant and ecosystem processes. New Phytol 215, 27-37.
	McDougall WB (1916). The growth of forest tree roots. Am J Bot 3, 384-392.
	Micikevicius P, Narang S, Alben J, Diamos G, Elsen E, Garcia D, Ginsburg B, Houston M, Kuchaiev O, Venkatesh G, Wu H (2018). Mixed precision training. arXiv.
	Morandage S, Laloy E, Schnepf A, Vereecken H, Vanderborght J (2021). Bayesian inference of root architectural model parameters from synthetic field data. Plant Soil 467, 67-89.
	Mungoli N (2023). Scalable, distributed AI frameworks: leveraging cloud computing for enhanced deep learning performance and efficiency. arXiv.
	Murmu S, Sinha D, Chaurasia H, Sharma S, Das R, Jha GK, Archak S (2024). A review of artificial intelligence—assisted omics techniques in plant defense: current trends and future directions. Front Plant Sci 15, 1292054.
	Nabwire S, Suh HK, Kim MS, Baek I, Cho BK (2021). Review: application of artificial intelligence in phenomics. Sensors 21, 4363.
	Nigar N, Muhammad Faisal H, Umer M, Oki O, Manappattukunnel Lukose J (2024). Improving plant disease classification with deep—learning—based prediction model using explainable artificial intelligence. IEEE Access 12, 100005-100014.
	Persson H (1978). Root dynamics in a young Scots pine stand in central Sweden. Oikos 30, 508-519.
	Petrovic AM, Siebert JE, Rieke PE (1982). Soil bulk density analysis in three dimensions by computed tomographic scanning. Soil Sci Soc Am J 46, 445-450.
	Pierz LD, Heslinga DR, Buell CR, Haus MJ (2023). An image—based technique for automated root disease severity assessment using PlantCV. Appl Plant Sci 11, e11507.
	Postma JA, Lynch JP (2012). Complementarity in root architecture for nutrient uptake in ancient maize/bean and maize/bean/squash polycultures. Ann Bot 110, 521-534.
	Pound MP, Atkinson JA, Townsend AJ, Wilson MH, Griffiths M, Jackson AS, Bulat A, Tzimiropoulos G, Wells DM, Murchie EH, Pridmore TP, French AP (2017). Deep machine learning provides state—of—the—art performance in image—based plant phenotyping. Gigascience 6, gix083.
	Rahman MA, Chakraborty NR, Sufiun A, Banshal SK, Tajnin FR (2024). An AIoT—based hydroponic system for crop recommendation and nutrient parameter monitorization. Smart Agric Technol 8, 100472.
	Raza S, Pandey BK, Hawkesford MJ, Griffiths S, Bennett MJ, Mooney SJ (2025). Future crop breeding needs to consider future soils. Nat Plants 11, 939-941.
	Rellán—Álvarez R, Lobet G, Lindner H, Pradier PL, Sebastian J, Yee MC, Geng Y, Trontin C, LaRue T, Schrager—Lavelle A, Haney CH, Nieu R, Maloof J, Vogel JP, Dinneny JR (2015). GLO—Roots: an imaging platform enabling multidimensional characterization of soil—grown root systems. eLife 4, e07597.
	Shahhosseini M, Hu G, Huber I, Archontoulis SV (2021). Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt. Sci Rep 11, 1606.
	Sheikh M, Iqra F, Ambreen H, Pravin KA, Ikra M, Chung YS (2024). Integrating artificial intelligence and high—throughput phenotyping for crop improvement. J Integr Agric 23, 1787-1802.
	Shi JW, Xie SY, Li WK, Wang X, Wang JL, Chen YY, Chang YY, Lou QJ, Yang WN (2025). RPT: an integrated root phenotyping toolbox for segmenting and quantifying root system architecture. Plant Biotechnol J 23, 2095-2109.
	Shi WS, Cao J, Zhang Q, Li YHZ, Xu LY (2016). Edge computing: vision and challenges. IEEE Internet Things J 3, 637-646.
	Smith AG, Petersen J, Selvan R, Rasmussen CR (2020). Segmentation of roots in soil with U—Net. Plant Methods 16, 13.
	Soltaninejad M, Sturrock CJ, Griffiths M, Pridmore TP, Pound MP (2020). Three dimensional root CT segmentation using multi—resolution encoder—decoder networks. IEEE Trans Image Process 29, 6667-6679.
	Song H, Panjvani K, Liu ZG, Amar H, Kochian L, Ye SJ, Yang X, Feurtado JA, Chavda K, Chimbo Huatatoca KA, Eramian M (2023). Plant root occlusion inpainting with generative adversarial network. 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). Paris: IEEE. pp. 531-539.
	Sordo Z, Andeer P, Sethian J, Northen T, Ushizima D (2024). RhizoNet segments plant roots to assess biomass and growth for enabling self—driving labs. Sci Rep 14, 12907.
	Sun BR, Gao YZ, Wu X, Ma HM, Zheng CC, Wang XY, Zhang HL, Li ZJ, Yang HJ (2020). The relative contributions of pH, organic anions, and phosphatase to rhizosphere soil phosphorus mobilization and crop phosphorus uptake in maize/alfalfa polyculture. Plant Soil 447, 117-133.
	Taccari ML, Nuttall J, Chen XH, Wang H, Minnema B, Jimack PK (2022). Attention U—Net as a surrogate model for groundwater prediction. Adv Water Resour 163, 104-169.
	Tennant D (1975). A test of a modified line intersect method of estimating root length. J Ecol 63, 995-1001.
	Teramoto S, Uga Y (2024). Convolutional neural networks combined with conventional filtering to semantically segment plant roots in rapidly scanned X—ray computed tomography volumes with high noise levels. Plant Methods 20, 73.
	Thesma V, Velni JM (2022). Plant root phenotyping using deep conditional GANs and binary semantic segmentation. Sensors 23, 309.
	Trachsel S, Kaeppler SM, Brown KM, Lynch JP (2011). Shovelomics: high throughput phenotyping of maize (Zea mays L.) root architecture in the field . Plant Soil 341, 75-87.
	Weihs BJ, Heuschele DJ, Tang Z, York LM, Zhang ZW, Xu ZY (2024a). The state of the art in root system architecture image analysis using artificial intelligence: a review. Plant Phenomics 6, 0178.
	Weihs BJ, Tang Z, Tian ZZ, Heuschele DJ, Siddique A, Terrill TH, Zhang Z, York LM, Zhang ZW, Xu ZY (2024b). Phenotyping alfalfa (Medicago sativa L.) root structure architecture via integrating confident machine learning with ResNet—18 . Plant Phenomics 6, 0251.
	Wójcik—Gront E, Zieniuk B, Pawełkowicz M (2024). Harnessing AI—powered genomic research for sustainable crop improvement. Agriculture 14, 2299.
	Xing YY, Wang XK (2024). Precise application of water and fertilizer to crops: challenges and opportunities. Front Plant Sci 15, 1444560.
	Yang XK, Xu YY, Chen L, Wang WB, Gao Y, Tian JD, Yu K, Jin YH, Mei H (2024). AI for Science: AI enabled scientific facility transforms fundamental research. Bull Chin Acad Sci 39, 59-69. (in Chinese with English abstract)
	杨小康, 许岩岩, 陈露, 王韫博, 高岳, 田济东, 俞凯, 金耀辉, 梅宏 (2024). AI for Science: 智能化科学设施变革基础研究. 中国科学院院刊 39, 59-69.
	Yu P, He XM, Baer M, Beirinckx S, Tian T, Moya YAT, Zhang XC, Deichmann M, Frey FP, Bresgen V, Li CJ, Razavi BS, Schaaf G, von Wirén N, Su Z, Bucher M, Tsuda K, Goormachtig S, Chen XP, Hochholdinger F (2021). Plant flavones enrich rhizosphere Oxalobacteraceae to improve maize performance under nitrogen deprivation. Nat Plants 7, 481-499.

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