Smart Breeding: Integration and Application from High-throughput Phenotyping to Genome Selection

doi:10.11983/CBB25155

Abstract

Abstract: With global warming and population growth accelerating, crop breeding must advance towards greater precision, efficiency and sustainability. Riding the wave of rapid AI development, AI-driven smart breeding has emerged as the cutting edge of plant breeding. Leveraging technologies such as high-throughput phenotyping through machine learning and deep learning, genome-wide association studies for genomic selection, and multiomics big data analysis, it integrates and edits genetic information while linking phenotypes with genomes. This approach promises revolutionary changes in agricultural breeding, enhancing crop productivity, improving quality, and refining traits. Looking ahead, precision agriculture and personalised breeding represent key developmental trajectories. Strategies focusing on environmental adaptability and climate change mitigation will aid in addressing ecological challenges. The cutting-edge technologies of smart breeding, significantly enhancing breeding efficiency and precision, will shape the future direction and progress of plant breeding.

Key words: smart breeding, high-throughput phenotyping, machine learning, genome-wide selection, big data analysis

Chuangxin Wang, Jianqi Li, Ying Zhang, Dong Han, Jing Fang, Enguang Zhao, Hua Huang, Lingling Da, Ji Zhang. Smart Breeding: Integration and Application from High-throughput Phenotyping to Genome Selection[J]. Chinese Bulletin of Botany, 2026, 61(4): 549-714.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.chinbullbotany.com/EN/10.11983/CBB25155

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

References 0

	Abdollahi—Arpanahi R, Gianola D, Peñagaricano F (2020). Deep learning versus parametric and ensemble methods for genomic prediction of complex phenotypes. Genet Sel Evol 52, 12.
	Ahn E, Prom LK, Magill C (2023). Multi—trait genome—wide association studies of Sorghum bicolor regarding resistance to anthracnose, downy mildew, grain mold and head smut. Pathogens 12, 779.
	Alvarez J, Martinez E, Diezma B (2021). Application of hyperspectral imaging in the assessment of drought and salt stress in magneto—primed triticale seeds. Plants 10, 835.
	Alzubaidi L, Zhang JL, Humaidi AJ, Al—Dujaili A, Duan Y, Al—Shamma O, Santamaría J, Fadhel MA, Al—Amidie M, Farhan L (2021). Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 8, 53.
	Araus JL, Kefauver SC, Zaman—Allah M, Olsen MS, Cairns JE (2018). Translating high—throughput phenotyping into genetic gain. Trends Plant Sci 23, 451-466.
	Bali N, Singla A (2022). Emerging trends in machine learning to predict crop yield and study its influential factors: a survey. Arch Comput Methods Eng 29, 95-112.
	Bellot P, De Los Campos G, Pérez—Enciso M (2018). Can deep learning improve genomic prediction of complex human traits? Genetics 210, 809-819.
	Bhat JA, Adeboye KA, Ganie SA, Barmukh R, Hu DZ, Varshney RK, Yu DY (2022). Genome—wide association study, haplotype analysis, and genomic prediction reveal the genetic basis of yield—related traits in soybean (Glycine max L.). Front Genet 13, 953833.
	Bhat JA, Deshmukh R, Zhao TJ, Patil G, Deokar A, Shinde S, Chaudhary J (2020). Harnessing high—throughput phenotyping and genotyping for enhanced drought tolerance in crop plants. J Biotechnol 324, 248-260.
	Bhat JA, Yu DY (2021). High—throughput NGS—based genotyping and phenotyping: role in genomics—assisted breeding for soybean improvement. Legume Sci 3, e81.
	Budhlakoti N, Kushwaha AK, Rai A, Chaturvedi KK, Kumar A, Pradhan AK, Kumar U, Kumar RR, Juliana P, Mishra DC, Kumar S (2022). Genomic selection: a tool for accelerating the efficiency of molecular breeding for development of climate—resilient crops. Front Genet 13, 832153.
	Bundó M, Martín—Cardoso H, Pesenti M, Gómez—Ariza J, Castillo L, Frouin J, Serrat X, Nogués S, Courtois B, Grenier C, Sacchi GA, San Segundo B (2022). Integrative approach for precise genotyping and transcriptomics of salt tolerant introgression rice lines. Front Plant Sci 12, 797141.
	Busemeyer L, Mentrup D, Möller K, Wunder E, Alheit K, Hahn V, Maurer HP, Reif JC, Würschum T, Müller J, Rahe F, Ruckelshausen A (2013). BreedVision—a multi—sensor platform for non—destructive field—based phenotyping in plant breeding. Sensors 13, 2830-2847.
	Chang WS, Wen WL, Zheng CX, Lu XJ, Chen B, Li RQ, Guo XY (2023). Geometric wheat modeling and quantitative plant architecture analysis using three—dimensional phytomers. Plants 12, 445.
	Chang—Brahim I, Koppensteiner LJ, Beltrame L, Bodner G, Saranti A, Salzinger J, Fanta—Jende P, Sulzbachner C, Bruckmüller F, Trognitz F, Samad—Zamini M, Zechner E, Holzinger A, Molin EM (2024). Reviewing the essential roles of remote phenotyping, GWAS and explainable AI in practical marker—assisted selection for drought—tolerant winter wheat breeding. Front Plant Sci 15, 1319938.
	Che DS, Liu Q, Rasheed K, Tao XP (2011). Decision tree and ensemble learning algorithms with their applications in bioinformatics. Adv Exp Med Biol 696, 191-199.
	Chen JX, Tan C, Zhu M, Zhang CY, Wang ZH, Ni XM, Liu YL, Wei T, Wei XF, Fang XD, Xu Y, Huang XH, Qiu J, Liu H (2024). CropGS—Hub: a comprehensive database of genotype and phenotype resources for genomic prediction in major crops. Nucleic Acids Res 52, D1519-D1529.
	Chen X, Ishwaran H (2012). Random forests for genomic data analysis. Genomics 99, 323-329.
	Cheng CY, Li Y, Varala K, Bubert J, Huang J, Kim GJ, Halim J, Arp J, Shih HJS, Levinson G, Park SH, Cho HY, Moose SP, Coruzzi GM (2021). Evolutionarily informed machine learning enhances the power of predictive gene—to—phenotype relationships. Nat Commun 12, 5627.
	Cheng Q, Wang XF (2024). Machine learning for AI breeding in plants. Genom Proteom Bioinform 22, qzae051.
	Confalonieri R, Paleari L, Foi M, Movedi E, Vesely FM, Thoelke W, Agape C, Borlini G, Ferri I, Massara F, Motta R, Ravasi RA, Tartarini S, Zoppolato C, Baia LM, Brumana A, Colombo D, Curatolo A, Fauda V, Gaia D, Gerosa A, Ghilardi A, Grassi E, Magarini A, Novelli F, Perez Garcia FB, Rota Graziosi A, Salvan M, Tadiello T, Rossini L (2017). PocketPlant3D: analysing canopy structure using a smartphone. Biosys Eng 164, 1-12.
	Correia PMP, Cairo Westergaard J, Bernardes Da Silva A, Roitsch T, Carmo—Silva E, Marques Da Silva J (2022). High—throughput phenotyping of physiological traits for wheat resilience to high temperature and drought stress. J Exp Bot 73, 5235-5251.
	Crossa J, De Los Campos G, Maccaferri M, Tuberosa R, Burgueño J, Pérez—Rodríguez P (2016). Extending the marker × environment interaction model for genomic—enabled prediction and genome—wide association analysis in durum wheat. Crop Sci 56, 2193-2209.
	Crossa J, Martini JWR, Vitale P, Pérez—Rodríguez P, Costa—Neto G, Fritsche—Neto R, Runcie D, Cuevas J, Toledo F, Li H, De Vita P, Gerard G, Dreisigacker S, Crespo—Herrera L, Saint Pierre C, Bentley A, Lillemo M, Ortiz R, Montesinos—López OA, Montesinos—López A (2025a). Expanding genomic prediction in plant breeding: harnessing big data, machine learning, and advanced software. Trends Plant Sci 30, 756-774.
	Crossa J, Montesinos—Lopez OA, Costa—Neto G, Vitale P, Martini JWR, Runcie D, Fritsche—Neto R, Montesinos—Lopez A, Pérez—Rodríguez P, Gerard G, Dreisigacker S, Crespo—Herrera L, Pierre CS, Lillemo M, Cuevas J, Bentley A, Ortiz R (2025b). Machine learning algorithms translate big data into predictive breeding accuracy. Trends Plant Sci 30, 167-184.
	Cunicelli M, Olukolu BA, Sams C, Schneider L, West D, Pantalone V (2022). Mapping and identification of QTL in 5601T × U99—310255 RIL population using SNP genotyping: soybean seed quality traits. Mol Biol Rep 49, 6623-6632.
	Dan ZW, Chen YP, Li H, Zeng YF, Xu WW, Zhao WB, He RF, Huang WC (2021). The metabolomic landscape of rice heterosis highlights pathway biomarkers for predicting complex phenotypes. Plant Physiol 187, 1011-1025.
	De Los Campos G, Gianola D, Rosa GJM, Weigel KA, Crossa J (2010). Semi—parametric genomic—enabled prediction of genetic values using reproducing kernel Hilbert spaces methods. Genet Res 92, 295-308.
	DeChant C, Wiesner—Hanks T, Chen SY, Stewart EL, Yosinski J, Gore MA, Nelson RJ, Lipson H (2017). Automated identification of northern leaf blight—infected maize plants from field imagery using deep learning. Phytopathology 107, 1426-1432.
	Duncan KE, Czymmek KJ, Jiang N, Thies AC, Topp CN (2022). X—ray microscopy enables multiscale high—resolution 3D imaging of plant cells, tissues, and organs. Plant Physiol 188, 831-845.
	Endelman JB (2011). Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome 4, 250-255.
	Fahlgren N, Gehan MA, Baxter I (2015). Lights, camera, action: high—throughput plant phenotyping is ready for a close—up. Curr Opin Plant Biol 24, 93-99.
	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.
	Fichman Y, Miller G, Mittler R (2019). Whole—plant live imaging of reactive oxygen species. Mol Plant 12, 1203-1210.
	Fu JJ, Hao YF, Li HH, Reif JC, Chen SJ, Huang CL, Wang GY, Li XH, Xu YB, Li L (2022). Integration of genomic selection with doubled—haploid evaluation in hybrid breeding: from GS 1.0 to GS 4.0 and beyond. Mol Plant 15, 577-580.
	Gao PF, Zhao HN, Luo Z, Lin YF, Feng WJ, Li YL, Kong FJ, Li X, Fang C, Wang XT (2023). SoyDNGP: a web—accessible deep learning framework for genomic prediction in soybean breeding. Brief Bioinform 24, bbad349.
	Gehan MA, Fahlgren N, Abbasi A, Berry JC, Callen ST, Chavez L, Doust AN, Feldman MJ, Gilbert KB, Hodge JG, Hoyer JS, Lin A, Liu SX, Lizárraga C, Lorence A, Miller M, Platon E, Tessman M, Sax T (2017). PlantCV v2: image analysis software for high—throughput plant phenotyping. PeerJ 5, e4088.
	Gill T, Gill SK, Saini DK, Chopra Y, De Koff JP, Sandhu KS (2022). A comprehensive review of high throughput phenotyping and machine learning for plant stress phenotyping. Phenomics 2, 156-183.
	Goluguri NVRR, Suganya Devi K, Vadaparthi N (2021). Image classifiers and image deep learning classifiers evolved in detection of Oryza sativa diseases: survey. Artif Intell Rev 54, 359-396.
	Gou CM, Zafar S, Fatima, Hasnain Z, Aslam N, Iqbal N, Abbas S, Li H, Li J, Chen B, Ragauskas AJ, Abbas M (2024). Machine and deep learning: artificial intelligence application in biotic and abiotic stress management in plants. Front Biosci (Landmark Ed) 29, 20.
	Gui ST, Yang LF, Li JB, Luo JY, Xu XK, Yuan JY, Chen L, Li WQ, Yang X, Wu SS, Li SY, Wang YB, Zhu YB, Gao Q, Yang N, Yan JB (2020). ZEAMAP, a comprehensive database adapted to the maize multi—omics era. iScience 23, 101241.
	Guo QH, Wu FF, Pang SX, Zhao XQ, Chen LH, Liu J, Xue BL, Xu GC, Li L, Jing HC, Chu CC (2018). Crop 3D—a LiDAR based platform for 3D high—throughput crop phenotyping. Sci China Life Sci 61, 328-339.
	Hartmann A, Czauderna T, Hoffmann R, Stein N, Schreiber F (2011). HTPheno: an image analysis pipeline for high—throughput plant phenotyping. BMC Bioinform 12, 148.
	Holliday JA, Wang TL, Aitken S (2012). Predicting adaptive phenotypes from multilocus genotypes in Sitka spruce (Picea sitchensis) using random forest. G3—Genes Genom Genet 2, 1085-1093.
	Hu TQ, Shen P, Zhang YS, Zhang JF, Li X, Xia CZ, Liu P, Lu H, Wu TT, Han ZG (2025). OpenPheno: an open—access, user—friendly, and smartphone—based software platform for instant plant phenotyping. Plant Methods 21, 76.
	Jamil IN, Remali J, Azizan KA, Nor Muhammad NA, Arita M, Goh HH, Aizat WM (2020). Systematic multi—omics integration (MOI) approach in plant systems biology. Front Plant Sci 11, 944.
	Jiang N, Zhu XG (2024). Modern phenomics to empower holistic crop science, agronomy, and breeding research. J Genet Genomics 51, 790-800.
	Jiang Y, Li CY (2020). Convolutional neural networks for image—based high—throughput plant phenotyping: a review. Plant Phenomics 2020, 4152816.
	Jiang Z, Tu HF, Bai BW, Yang CH, Zhao BQ, Guo ZY, Liu Q, Zhao H, Yang WN, Xiong LZ, Zhang J (2021). Combining UAV—RGB high—throughput field phenotyping and genome—wide association study to reveal genetic variation of rice germplasms in dynamic response to drought stress. New Phytol 232, 440-455.
	Khaki S, Wang LZ, Archontoulis SV (2019). A CNN—RNN framework for crop yield prediction. Front Plant Sci 10, 1750.
	Knecht AC, Campbell MT, Caprez A, Swanson DR, Walia H (2016). Image Harvest: an open—source platform for high—throughput plant image processing and analysis. J Exp Bot 67, 3587-3599.
	Kumar R, Das SP, Choudhury BU, Kumar A, Prakash NR, Verma R, Chakraborti M, Devi AG, Bhattacharjee B, Das R, Das B, Devi HL, Das B, Rawat S, Mishra VK (2024). Advances in genomic tools for plant breeding: harnessing DNA molecular markers, genomic selection, and genome editing. Biol Res 57, 80.
	Kuzbakova M, Khassanova G, Oshergina I, Ten E, Jatayev S, Yerzhebayeva R, Bulatova K, Khalbayeva S, Schramm C, Anderson P, Sweetman C, Jenkins CLD, Soole KL, Shavrukov Y (2022). Height to first pod: A review of genetic and breeding approaches to improve combine harvesting in legume crops. Front Plant Sci 13, 948099.
	Li DL, Quan CQ, Song ZY, Li X, Yu GH, Li C, Muhammad A (2021). High—throughput plant phenotyping platform (HT3P) as a novel tool for estimating agronomic traits from the lab to the field. Front Bioeng Biotechnol 8, 623705.
	Li GT, An LN, Yang WN, Yang L, Wei T, Shi JW, Wang JL, Doonan JH, Xie KB, Fernie AR, Lagudah ES, Wing RA, Gao CX (2025a). Integrated biotechnological and AI innovations for crop improvement. Nature 643, 925-937.
	Li HH, Li X, Zhang P, Feng YW, Mi JR, Gao S, Sheng LL, Ali M, Yang ZK, Li L, Fang W, Wang WS, Qian Q, Gu F, Zhou WB (2024). Smart breeding platform: a web—based tool for high—throughput population genetics, phenomics, and genomic selection. Mol Plant 17, 677-681.
	Li L, Hassan MA, Wang DX, Wan GL, Beegum S, Rasheed A, Xia XC, He Y, Zhang Y, He ZH, Liu JD, Xiao YG (2025b). RGB imaging and computer vision—based approaches for identifying spike number loci for wheat. Plant Phenomics 7, 100051.
	Li L, Zhang Q, Huang DF (2014). A review of imaging techniques for plant phenotyping. Sensors 14, 20078-20111.
	Liu SY, Xu F, Xu YT, Wang Q, Yan J, Wang JY, Wang XB, Wang XF (2022). MODAS: exploring maize germplasm with multi—omics data association studies. Sci Bull 67, 903-906.
	Long NY, Gianola D, Rosa GJM, Weigel KA (2011). Application of support vector regression to genome—assisted prediction of quantitative traits. Theor Appl Genet 123, 1065-1074.
	Lozada DN, Sandhu KS, Bhatta M (2023). Ridge regression and deep learning models for genomewide selection of complex traits in New Mexican Chile peppers. BMC Genom Data 24, 80.
	Ma LL, Wang C, Hu Y, Dai W, Liang ZJ, Zou CY, Pan GT, Lübberstedt T, Shen YO (2022). GWAS and transcriptome analysis reveal MADS26 involved in seed germination ability in maize. Theor Appl Genet 135, 1717-1730.
	Ma SW, Wang M, Wu JH, Guo WL, Chen YM, Li GW, Wang YP, Shi WM, Xia GM, Fu DL, Kang ZS, Ni F (2021). WheatOmics: a platform combining multiple omics data to accelerate functional genomics studies in wheat. Mol Plant 14, 1965-1968.
	Ma WL, Qiu ZX, Song J, Li JJ, Cheng Q, Zhai JJ, Ma C (2018). A deep convolutional neural network approach for predicting phenotypes from genotypes. Planta 248, 1307-1318.
	Maina F, Harou A, Hamidou F, Morris GP (2022). Genome—wide association studies identify putative pleiotropic locus mediating drought tolerance in sorghum. Plant Direct 6, e413.
	Merrick LF, Herr AW, Sandhu KS, Lozada DN, Carter AH (2022). Optimizing plant breeding programs for genomic selection. Agronomy 12, 714.
	Mochida K, Koda S, Inoue K, Hirayama T, Tanaka S, Nishii R, Melgani F (2019). Computer vision—based phenotyping for improvement of plant productivity: a machine learning perspective. Gigascience 8, giy153.
	Moeinizade S, Pham H, Han Y, Dobbels A, Hu GP (2022). An applied deep learning approach for estimating soybean relative maturity from UAV imagery to aid plant breeding decisions. Mach Learn Appl 7, 100233.
	Montesinos—López OA, Herr AW, Crossa J, Montesinos—López A, Carter AH (2024). Enhancing winter wheat prediction with genomics, phenomics and environmental data. BMC Genomics 25, 544.
	Montesinos—López OA, Martín—Vallejo J, Crossa J, Gianola D, Hernández—Suárez CM, Montesinos—López A, Juliana P, Singh R (2019). A benchmarking between deep learning, support vector machine and Bayesian threshold best linear unbiased prediction for predicting ordinal traits in plant breeding. G3—Genes Genom Genet 9, 601-618.
	Montesinos—López OA, Montesinos—López A, Crossa J, Toledo FH, Montesinos—López JC, Singh P, Juliana P, Salinas—Ruiz J (2017). A Bayesian Poisson—lognormal model for count data for multiple—trait multiple—environment genomic—enabled prediction. G3—Genes Genom Genet 7, 1595-1606.
	Montesinos—López OA, Montesinos—López A, Hernandez—Suarez CM, Barrón—López JA, Crossa J (2021a). Deep—learning power and perspectives for genomic selection. Plant Genome 14, e20122.
	Montesinos—López OA, Montesinos—López A, Pérez—Rodríguez P, Barrón—López JA, Martini JWR, Fajardo—Flores SB, Gaytan—Lugo LS, Santana—Mancilla PC, Crossa J (2021b). A review of deep learning applications for genomic selection. BMC Genomics 22, 19.
	Montesinos—López O A, Montesinos—López JC, Salazar E, Barron JA, Montesinos—Lopez A, Buenrostro—Mariscal R, Crossa J (2021c). Application of a Poisson deep neural network model for the prediction of count data in genome—based prediction. Plant Genome 14, e20118.
	Nabwire S, Suh HK, Kim MS, Baek I, Cho BK (2021). Review: application of artificial intelligence in phenomics. Sensors 21, 4363.
	Nakhle F, Harfouche AL (2021). Ready, Steady, Go AI: a practical tutorial on fundamentals of artificial intelligence and its applications in phenomics image analysis. Patterns 2, 100323.
	Paulus S, Mahlein AK (2020). Technical workflows for hyperspectral plant image assessment and processing on the greenhouse and laboratory scale. Gigascience 9, giaa090.
	Peng H, Wang K, Chen Z, Cao YH, Gao Q, Li Y, Li XX, Lu HW, Du HL, Lu M, Yang X, Liang CZ (2019). MBKbase for rice: an integrated omics knowledgebase for molecular breeding in rice. Nucleic Acids Res 48, D1085-D1092.
	Pérez P, De Los Campos G (2014). Genome—wide regression and prediction with the BGLR statistical package. Genetics 198, 483-495.
	Pérez—Rodríguez P, Gianola D, González—Camacho JM, Crossa J, Manès Y, Dreisigacker S (2012). Comparison between linear and non—parametric regression models for genome—enabled prediction in wheat. G3—Genes Genom Genet 2, 1595-1605.
	Pinto F, Damm A, Schickling A, Panigada C, Cogliati S, Müller—Linow M, Ballvora A, Rascher U (2016). Sun—induced chlorophyll fluorescence from high—resolution imaging spectroscopy data to quantify spatio—temporal patterns of photosynthetic function in crop canopies. Plant Cell Environ 39, 1500-1512.
	Raza A, Zaman QU, Shabala S, Tester M, Munns R, Hu ZL, Varshney RK (2025). Genomics—assisted breeding for designing salinity—smart future crops. Plant Biotechnol J 23, 3119-3151.
	Sahito JH, Zhang H, Gishkori ZGN, Ma CH, Wang ZH, Ding D, Zhang XH, Tang JH (2024). Advancements and prospects of genome—wide association studies (GWAS) in maize. Int J Mol Sci 25, 1918.
	Sangjan W, Carter AH, Pumphrey MO, Hagemeyer K, Jitkov V, Sankaran S (2024). Effect of high—resolution satellite and UAV imagery plot pixel resolution in wheat crop yield prediction. Int J Remote Sens 45, 1678-1698.
	Shahi D, Guo J, Pradhan S, Khan J, Avci M, Khan N, McBreen J, Bai GH, Reynolds M, Foulkes J, Babar MA (2022). Multi—trait genomic prediction using in—season physiological parameters increases prediction accuracy of complex traits in US wheat. BMC Genomics 23, 298.
	Shakoor N, Northrup D, Murray S, Mockler TC (2019). Big data driven agriculture: big data analytics in plant breeding, genomics, and the use of remote sensing technologies to advance crop productivity. Plant Phenome J 2, 1-8.
	Shen ZJ, Shen EH, Yang K, Fan ZQ, Zhu QH, Fan LJ, Ye CY (2024). BreedingAIDB: a database integrating crop genome—to—phenotype paired data with machine learning tools applicable to breeding. Plant Commun 5, 100894.
	Singh A, Ganapathysubramanian B, Singh AK, Sarkar S (2016). Machine learning for high—throughput stress phenotyping in plants. Trends Plant Sci 21, 110-124.
	Thomson MJ, Zhao KY, Wright M, McNally KL, Rey J, Tung CW, Reynolds A, Scheffler B, Eizenga G, McClung A, Kim H, Ismail AM, De Ocampo M, Mojica C, Reveche MY, Dilla—Ermita CJ, Mauleon R, Leung H, Bustamante C, McCouch SR (2012). High—throughput single nucleotide polymorphism genotyping for breeding applications in rice using the BeadXpress platform. Mol Breed 29, 875-886.
	Tong H, Nikoloski Z (2021). Machine learning approaches for crop improvement: leveraging phenotypic and genotypic big data. J Plant Physiol 257, 153354.
	Ubbens JR, Stavness I (2017). Deep plant phenomics: a deep learning platform for complex plant phenotyping tasks. Front Plant Sci 8, 1190.
	Van Dijk ADJ, Kootstra G, Kruijer W, De Ridder D (2021). Machine learning in plant science and plant breeding. iScience 24, 101890.
	Vignal A, Milan D, SanCristobal M, Eggen A (2002). a review on SNP and other types of molecular markers and their use in animal genetics. Genet Sel Evol 34, 275-305.
	Virlet N, Sabermanesh K, Sadeghi—Tehran P, Hawkesford MJ (2016). Field Scanalyzer: an automated robotic field phenotyping platform for detailed crop monitoring. Funct Plant Biol 44, 143-153.
	Wang KL, Abid MA, Rasheed A, Crossa J, Hearne S, Li HH (2023a). DNNGP, a deep neural network—based method for genomic prediction using multi—omics data in plants. Mol Plant 16, 279-293.
	Wang MY, Li ZJ, Zhang YE, Zhang YY, Xie YL, Ye LH, Zhuang YL, Lin KD, Zhao F, Guo JY, Teng W, Zhang WL, Tong YP, Xue YB, Zhang YJ (2021). An atlas of wheat epigenetic regulatory elements reveals subgenome divergence in the regulation of development and stress responses. Plant Cell 33, 865-881.
	Wang PY, Yao SL, Kosami KI, Guo T, Li J, Zhang YY, Fukao Y, Kaneko—Kawano T, Zhang H, She YM, Wang PC, Xing WM, Hanada K, Liu RY, Kawano Y (2020). Identification of endogenous small peptides involved in rice immunity through transcriptomics— and proteomics—based screening. Plant Biotechnol J 18, 415-428.
	Wang Q, Jiang S, Li T, Qiu ZX, Yan J, Fu R, Ma C, Wang XF, Jiang SQ, Cheng Q (2023b). G2P provides an integrative environment for multi—model genomic selection analysis to improve genotype—to—phenotype prediction. Front Plant Sci 14, 1207139.
	Wang X, Yang ZF, Xu CW (2015). A comparison of genomic selection methods for breeding value prediction. Sci Bull 60, 925-935.
	Wang XF, Yang S, Yan BQ (2025). Research status and development path of key technologies in digital breeding. Vegetables (1—2), 1-12. (in Chinese)
	王晓锋, 杨硕, 闫宝歧 (2025). 数字育种关键技术研究现状与发展路径. 蔬菜 (1—2), 1-12.
	Wang XY, Yang WN, Lv QC, Huang CL, Liang XY, Chen GX, Xiong LZ, Duan LF (2022). Field rice panicle detection and counting based on deep learning. Front Plant Sci 13, 966495.
	Westhues CC, Mahone GS, Da Silva S, Thorwarth P, Schmidt M, Richter JC, Simianer H, Beissinger TM (2021). Prediction of maize phenotypic traits with genomic and environmental predictors using gradient boosting frameworks. Front Plant Sci 12, 699589.
	Wu CX, Luo JY, Xiao YJ (2024). Multi—omics assists genomic prediction of maize yield with machine learning approaches. Mol Breeding 44, 14.
	Wu D, Guo ZL, Ye JL, Feng H, Liu JX, Chen GX, Zheng JS, Yan DM, Yang XQ, Xiong X, Liu Q, Niu ZY, Gay AP, Doonan JH, Xiong LZ, Yang WN (2019). Combining high—throughput micro—CT—RGB phenotyping and genome—wide association study to dissect the genetic architecture of tiller growth in rice. J Exp Bot 70, 545-561.
	Wu H, Han R, Zhao L, Liu MY, Chen H, Li WF, Li L (2025). AutoGP: an intelligent breeding platform for enhancing maize genomic selection. Plant Commun 6, 101240.
	Wu X, Feng H, Wu D, Yan SJ, Zhang P, Wang WB, Zhang J, Ye JL, Dai GX, Fan Y, Li WK, Song BX, Geng ZD, Yang WL, Chen GX, Qin F, Terzaghi W, Stitzer M, Li L, Xiong LZ, Yan JB, Buckler E, Yang WN, Dai MQ (2021). Using high—throughput multiple optical phenotyping to decipher the genetic architecture of maize drought tolerance. Genome Biol 22, 185.
	Xiao QL, Bai XL, Zhang C, He Y (2022). Advanced high—throughput plant phenotyping techniques for genome—wide association studies: a review. J Adv Res 35, 215-230.
	Xiao YJ, Jiang SQ, Cheng Q, Wang XQ, Yan J, Zhang RY, Qiao F, Ma C, Luo JY, Li WQ, Liu HJ, Yang WY, Song WH, Meng YJ, Warburton ML, Zhao JR, Wang XF, Yan JB (2021). The genetic mechanism of heterosis utilization in maize improvement. Genome Biol 22, 148.
	Xing YC, Lv PX, He H, Leng JT, Yu H, Feng XZ (2022). Traits expansion and storage of soybean phenotypic data in computer vision—based test. Front Plant Sci 13, 832592.
	Xiong X, Yu LJ, Yang WN, Liu M, Jiang N, Wu D, Chen GX, Xiong LZ, Liu KD, Liu Q (2017). A high—throughput stereo—imaging system for quantifying rape leaf traits during the seedling stage. Plant Methods 13, 7.
	Xu Y, Wang X, Ding XW, Zheng XF, Yang ZF, Xu CW, Hu ZL (2018). Genomic selection of agronomic traits in hybrid rice using an NCII population. Rice 11, 32.
	Yan J, Xu YT, Cheng Q, Jiang SQ, Wang Q, Xiao YJ, Ma C, Yan JB, Wang XF (2021). LightGBM: accelerated genomically designed crop breeding through ensemble learning. Genome Biol 22, 271.
	Yang WN, Feng H, Zhang XH, Zhang J, Doonan JH, Batchelor WD, Xiong LZ, Yan JB (2020). Crop phenomics and high—throughput phenotyping: past decades, current challenges, and future perspectives. Mol Plant 13, 187-214.
	Yang WY, Guo TT, Luo JY, Zhang RY, Zhao JR, Warburton ML, Xiao YJ, Yan JB (2022). Target—oriented prioritization: targeted selection strategy by integrating organismal and molecular traits through predictive analytics in breeding. Genome Biol 23, 80.
	Yang YQ, Lv HJ, Chen N (2023). A survey on ensemble learning under the era of deep learning. Artif Intell Rev 56, 5545-5589.
	Yoosefzadeh—Najafabadi M, Earl HJ, Tulpan D, Sulik J, Eskandari M (2021). Application of machine learning algorithms in plant breeding: predicting yield from hyperspectral reflectance in soybean. Front Plant Sci 11, 624-273.
	Yoosefzadeh—Najafabadi M, Eskandari M, Torabi S, Torkamaneh D, Tulpan D, Rajcan I (2022). Machine—learning—based genome—wide association studies for uncovering QTL underlying soybean yield and its components. Int J Mol Sci 23, 5538.
	Yoosefzadeh Najafabadi M, Hesami M, Eskandari M (2023). Machine learning—assisted approaches in modernized plant breeding programs. Genes 14, 777.
	Yu H, Bai SW, Li JY (2024). Towards breeding 5.0: smart variety by intelligent breeding. Chin Sci Bull 69, 4687-4690. (in Chinese)
	余泓, 白世伟, 李家洋 (2024). 迈向育种5.0, 智能品种的智能培育. 科学通报 69, 4687-4690.
	Zarco—Tejada PJ, Guillén—Climent ML, Hernández—Clemente R, Catalina A, González MR, Martín P (2013). Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV). Agric Forest Meteorol 171—172, 281-294.
	Zeng P, Zhou X (2017). Non—parametric genetic prediction of complex traits with latent Dirichlet process regression models. Nat Commun 8, 456.
	Zhang CP, Cui YR, Yuan C, Zhang F, Zhu Q, Li SJ, Zheng SY, Zhou SR, Sheng TT, Qiu XJ, Liang QQ, Yu YY, Wang WS, Li M (2025a). Rice3KGS: a powerful web platform and database for large—scale genome selection. Plant Commun 6, 101369.
	Zhang F, Wu JF, Sade N, Wu S, Egbaria A, Fernie AR, Yan JB, Qin F, Chen W, Brotman Y, Dai MQ (2021). Genomic basis underlying the metabolome—mediated drought adaptation of maize. Genome Biol 22, 260.
	Zhang R, Zhang CP, Yu CY, Dong JG, Hu JH (2022). Integration of multi—omics technologies for crop improvement: status and prospects. Front Bioinform 2, 1027457.
	Zhang Y, Huang GM, Zhao YX, Lu XJ, Wang YR, Wang CY, Guo XY, Zhao CJ (2025b). Revolutionizing crop breeding: next—generation artificial intelligence and big data—driven intelligent design. Engineering 44, 245-255.
	Zhang YT (2024). Development and preliminary exploration of intelligent breeding technology. Seed Sci Technol 42(21), 61-63. (in Chinese)
	张雅婷 (2024). 智能育种技术的发展和初探. 种子科技 42(21), 61-63.
	Zhao CJ, Zhang Y, Du JJ, Guo XY, Wen WL, Gu SH, Wang JL, Fan JC (2019). Crop phenomics: current status and perspectives. Front Plant Sci 10, 714.
	Zhao L, Xie L, Zhang Q, Ouyang WZ, Deng L, Guan PP, Ma M, Li Y, Zhang Y, Xiao Q, Zhang JW, Li HMJ, Wang SY, Man JW, Cao ZL, Zhang QH, Zhang QF, Li GL, Li XW (2020). Integrative analysis of reference epigenomes in 20 rice varieties. Nat Commun 11, 2658.

[1]	Xinwei Guo, Guanze Wang, Rui Sun, Wenxian X, WU Zhi-Xiang. Seasonal variation in tem sap flow and its driving mechanisms across different leaf phenophases in Hevea brasiliensis [J]. Chin J Plant Ecol, 2026, 50(预发表): 0-.
[2]	Rigui Yi, Delin Qi, Shihai Zhu, Tingting Yang, Dan Liu, Qiang Gao, Mingzhe Xia, Wei Wang, Miaomiao Nie, Junmei Jia, Cunfang Zhang. Molecular Identification of Triplophysa Species Using Cyt b Gene-Based Machine Learning Methods [J]. Biodiv Sci, 2026, 34(7): 0-.
[3]	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.
[4]	Meili Wang, Fangbo Lu, Wanchuang Luo, Yao Chen, Yong’an Yin, Jing Zhao, Ni Jiang, Hao Jiang, Shuqin Gao. Artificial Intelligence in Crop Phenotyping: Advances and Challenges [J]. Chinese Bulletin of Botany, 2026, 61(4): 549-714.
[5]	Yafei Shi, Furong Niu, Xiaomin Huang, Xing Hong, Xiangwen Gong, Yanli Wang, Dong Lin, Xiaoni Liu. Interpretable machine learning and its applications in ecology [J]. Biodiv Sci, 2026, 34(1): 25210-.
[6]	FU Zhao-Qi, HU Xu, TIAN Qin-Rui, GE Yan-Ling, ZHOU Hong-Juan, WU Xiao-Yun, CHEN Li-Xin. Nocturnal sap flow characteristics of two typical forest tree species and responses to environmental factors in the loess region of West Shanxi, China [J]. Chin J Plant Ecol, 2024, 48(9): 1128-1142.
[7]	WANG Xiu-Ying, CHEN Qi, DU Hua-Li, ZHANG Rui, MA Hong-Lu. Evapotranspiration interpolation in alpine marshes wetland on the Qingzang Plateau based on machine learning [J]. Chin J Plant Ecol, 2023, 47(7): 912-921.
[8]	Yufei Huang, Chunyan Lu, Mingming Jia, Zili Wang, Yue Su, Yanlin Su. Plant species classification of coastal wetlands based on UAV images and object- oriented deep learning [J]. Biodiv Sci, 2023, 31(3): 22411-.
[9]	Xiaohu Shen, Xiangyu Zhu, Hongfei Shi, Chuanzhi Wang. Research progress of birdsong recognition algorithms based on machine learning [J]. Biodiv Sci, 2023, 31(11): 23272-.
[10]	Li Guo, Xuehan Wang, Feng Tian. Multi-omics Integrative Network Map, a Key to Accurately Deco-ding the Maize Functional Genomics [J]. Chinese Bulletin of Botany, 2023, 58(1): 1-5.
[11]	JIANG Yu-Feng, LI Jing, XIN Rui-Rui, LI Yi. Spatial-temporal dynamics of coastal aquaculture ponds and its impacts on mangrove ecosystems [J]. Chin J Plant Ecol, 2022, 46(10): 1268-1279.
[12]	Xiang Gao, Zheng Wang, Jianya Ding, Qian Yang. Plant Recognition Based on Compound Leaf Features [J]. Chinese Bulletin of Botany, 2014, 49(4): 450-461.