[1]

Wang H, Fu T, Du Y, Gao W, Huang K, et al. 2023. Scientific discovery in the age of artificial intelligence. Nature 620:47−60

doi: 10.1038/s41586-023-06221-2
[2]

Li H, Yang X, Shang Y, Zhang Z, Huang S, et al. 2023. Vegetable biology and breeding in the genomics era. Science China Life Sciences 66:226−50

doi: 10.1007/s11427-022-2248-6
[3]

Williams K, Subramani M, Lofton LW, Penney M, Todd A, et al. 2024. Tools and techniques to accelerate crop breeding. Plants 13(11):1520

doi: 10.3390/plants13111520
[4]

Wang C, Liu B, Liu L, Zhu Y, Hou J, et al. 2021. A review of deep learning used in the hyperspectral image analysis for agriculture. Artificial Intelligence Review 54(7):5205−53

doi: 10.1007/s10462-021-10018-y
[5]

Wang K, Abid MA, Rasheed A, Crossa J, Hearne S, et al. 2023. DNNGP, a deep neural network-based method for genomic prediction using multi-omics data in plants. Molecular Plant 16:279−93

doi: 10.1016/j.molp.2022.11.004
[6]

Tiamiyu QO, Adebayo SE, Ibrahim N. 2023. Recent advances on postharvest technologies of bell pepper: a review. Heliyon 9(4):e15302

doi: 10.1016/j.heliyon.2023.e15302
[7]

Bhat JA, Feng X, Mir ZA, Raina A, Siddique KHM. 2023. Recent advances in artificial intelligence, mechanistic models, and speed breeding offer exciting opportunities for precise and accelerated genomics-assisted breeding. Physiologia Plantarum 175:e13969

doi: 10.1111/ppl.13969
[8]

Khan MHU, Wang S, Wang J, Ahmar S, Saeed S, et al. 2022. Applications of artificial intelligence in climate-resilient smart-crop breeding. International Journal of Molecular Sciences 23(19):11156

doi: 10.3390/ijms231911156
[9]

Zhu W, Li W, Zhang H, Li L. 2025. Big data and artificial intelligence-aided crop breeding: progress and prospects. Journal of Integrative Plant Biology 67:722−39

doi: 10.1111/jipb.13791
[10]

Li Z, Guo R, Li M, Chen Y, Li G. 2020. A review of computer vision technologies for plant phenotyping. Computers and Electronics in Agriculture 176:105672

doi: 10.1016/j.compag.2020.105672
[11]

Russakovsky O, Deng J, Su H, Krause J, Satheesh S, et al. 2015. ImageNet large scale visual recognition challenge. International Journal of Computer Vision 115(3):211−52

doi: 10.1007/s11263-015-0816-y
[12]

LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436−44

doi: 10.1038/nature14539
[13]

Weihs BJ, Heuschele DJ, Tang Z, York LM, Zhang Z, et al. 2024. The state of the art in root system architecture image analysis using artificial intelligence: a review. Plant Phenomics 6:0178

doi: 10.34133/plantphenomics.0178
[14]

Yan J, Wang X. 2023. Machine learning bridges omics sciences and plant breeding. Trends in Plant Science 28:199−210

doi: 10.1016/j.tplants.2022.08.018
[15]

Zheng B, Sun G, Meng Z, Nan R. 2022. Vegetable size measurement based on stereo camera and keypoints detection. Sensors 22(4):1617

doi: 10.3390/s22041617
[16]

Lawal OM. 2024. Real-time cucurbit fruit detection in greenhouse using improved YOLO series algorithm. Precision Agriculture 25:347−59

doi: 10.1007/s11119-023-10074-0
[17]

Kang R, Huang J, Zhou X, Ren N, Sun S. 2024. Toward real scenery: a lightweight tomato growth inspection algorithm for leaf disease detection and fruit counting. Plant Phenomics 6:0174

doi: 10.34133/plantphenomics.0174
[18]

Hou L, Zhu Y, Wei N, Liu Z, You J, et al. 2024. Study on utilizing mask R-CNN for phenotypic estimation of lettuce's growth status and optimal harvest timing. Agronomy 14:1271

doi: 10.3390/agronomy14061271
[19]

Zarnaq MH, Omid M, Firouz MS, Jafarian M, Bazyar P. 2022. Freshness and quality assessment of parsley using image processing and artificial intelligence techniques. Agricultural Engineering International: CIGR Journal 24(2):282−90

[20]

Lestari AD, Nur Afan syarifudin, Nurriski YJ. 2022. Application of pest detection on vegetable crops using the cnn algorithm as a smart farm innovation to realize food security in the 4.0 era. Journal of Soft Computing Exploration 3:111−16

doi: 10.52465/joscex.v3i2.72
[21]

Ullah Z, Alsubaie N, Jamjoom M, Alajmani SH, Saleem F. 2023. EffiMob-Net: a deep learning-based hybrid model for detection and identification of tomato diseases using leaf images. Agriculture 13:737

doi: 10.3390/agriculture13030737
[22]

Wu H, Han R, Zhao L, Liu M, Chen H, et al. 2025. AutoGP: an intelligent breeding platform for enhancing maize genomic selection. Plant Communications 6:101240

doi: 10.1016/j.xplc.2025.101240
[23]

Jiang R, Sun T, Shi Z, Moshelion M, Xu P. 2024. Leveraging 'golden-hour' WUE for developing superior vegetable varieties with optimal water-saving and growth traits. Vegetable Research 4:e002

doi: 10.48130/vegres-0024-0001
[24]

Sun T, Cheng R, Jiang R, Liu Y, Sun Y, et al. 2023. Combining functional physiological phenotyping and simulation model to estimate dynamic water use efficiency and infer transpiration sensitivity traits. European Journal of Agronomy 150:126955

doi: 10.1016/j.eja.2023.126955
[25]

Aguilar-Ariza A, Sotta N, Fujiwara T, Guo W, Kamiya T. 2024. A multi-target regression method to predict element concentrations in tomato leaves using hyperspectral imaging. Plant Phenomics 6:0146

doi: 10.34133/plantphenomics.0146
[26]

Malounas I, Vierbergen W, Kutluk S, Zude-Sasse M, Yang K, et al. 2024. SpectroFood dataset: a comprehensive fruit and vegetable hyperspectral meta-dataset for dry matter estimation. Data in Brief 52:110040

doi: 10.1016/j.dib.2024.110040
[27]

Xue W, Ding H, Jin T, Meng J, Wang S, et al. 2024. CucumberAI: cucumber fruit morphology identification system based on artificial intelligence. Plant Phenomics 6:0193

doi: 10.34133/plantphenomics.0193
[28]

Purugganan MD, Jackson SA. 2021. Advancing crop genomics from lab to field. Nature Genetics 53:595−601

doi: 10.1038/s41588-021-00866-3
[29]

Bhattarai G, Olaoye D, Mou B, Correll JC, Shi A. 2022. Mapping and selection of downy mildew resistance in spinach cv. whale by low coverage whole genome sequencing. Frontiers in Plant Science 13:1012923

doi: 10.3389/fpls.2022.1012923
[30]

Tong H, Nankar AN, Liu J, Todorova V, Ganeva D, et al. 2022. Genomic prediction of morphometric and colorimetric traits in Solanaceous fruits. Horticulture Research 9:uhac072

doi: 10.1093/hr/uhac072
[31]

Patat AS, Sen F, Erdogdu BS, Uncu AT, Uncu AO. 2022. Construction and characterization of a de novo draft genome of garden cress (Lepidium sativum L.). Functional & Integrative Genomics 22:879−89

doi: 10.1007/s10142-022-00866-4
[32]

Li T, Xu H, Teng S, Suo M, Bahitwa R, et al. 2024. Modeling 0.6 million genes for the rational design of functional cis-regulatory variants and de novo design of cis-regulatory sequences. Proceedings of the National Academy of Sciences of the United States of America 121:e2319811121

doi: 10.1073/pnas.2319811121
[33]

Sun Z, Zhao W, Li Y, Si C, Sun X, et al. 2022. An exploration of pepino (Solanum muricatum) flavor compounds using machine learning combined with metabolomics and sensory evaluation. Foods 11:3248

doi: 10.3390/foods11203248
[34]

Bouranis JA, Ren Y, Beaver LM, Choi J, Wong CP, et al. 2024. Identification of biological signatures of cruciferous vegetable consumption utilizing machine learning-based global untargeted stable isotope traced metabolomics. Frontiers in Nutrition 11:1390223

doi: 10.3389/fnut.2024.1390223
[35]

Luo X, Cao L, Yu L, Gao M, Ai J, et al. 2024. Deep learning-based characterization and redesign of major potato tuber storage protein. Food Chemistry 443:138556

doi: 10.1016/j.foodchem.2024.138556
[36]

Zhang P, He Y, Huang S. 2024. Unlocking epigenetic breeding potential in tomato and potato. aBIOTECH 5:507−18

doi: 10.1007/s42994-024-00184-2
[37]

He L, Wang J, Ciais P, Ballantyne A, Yu K, et al. 2023. Non-symmetric responses of leaf onset date to natural warming and cooling in northern ecosystems. PNAS Nexus 2:pgad308

doi: 10.1093/pnasnexus/pgad308
[38]

Deng Y, Xi H, Zhou G, Chen A, Wang Y, et al. 2023. An effective image-based tomato leaf disease segmentation method using MC-UNet. Plant Phenomics 5:0049

doi: 10.34133/plantphenomics.0049
[39]

Reji J, Nidamanuri RR. 2024. Deep learning-based prediction of plant height and crown area of vegetable crops using LiDAR point cloud. Scientific Reports 14:14903

doi: 10.1038/s41598-024-65322-8
[40]

Rincón MG, Mendez D, Colorado JD. 2022. Four-dimensional plant phenotyping model integrating low-density LiDAR data and multispectral images. Remote Sensing 14:356

doi: 10.3390/rs14020356
[41]

Wang X, Zeng H, Yang X, Shu J, Wu Q, et al. 2025. Remote sensing revolutionizing agriculture: toward a new frontier. Future Generation Computer Systems 166:107691

doi: 10.1016/j.future.2024.107691
[42]

Chen X, Wen S, Zhang L, Lan Y, Ge Y, et al. 2025. A calculation method for cotton phenotypic traits based on unmanned aerial vehicle LiDAR combined with a three-dimensional deep neural network. Computers and Electronics in Agriculture 230:109857

doi: 10.1016/j.compag.2024.109857
[43]

Segarra J, Buchaillot ML, Araus JL, Kefauver SC. 2020. Remote sensing for precision agriculture: sentinel-2 improved features and applications. Agronomy 10:641

doi: 10.3390/agronomy10050641
[44]

Grocholsky B, Keller J, Kumar V, Pappas G. 2006. Cooperative air and ground surveillance. IEEE Robotics & Automation Magazine 13:16−25

doi: 10.1109/MRA.2006.1678135
[45]

Yan G, Feng M, Lin W, Huang Y, Tong R, et al. 2022. Review and prospect for vegetable grafting robot and relevant key technologies. Agriculture 12:1578

doi: 10.3390/agriculture12101578
[46]

Paradkar V, Raheman H, Rahul K. 2021. Development of a metering mechanism with serial robotic arm for handling paper pot seedlings in a vegetable transplanter. Artificial Intelligence in Agriculture 5:52−63

doi: 10.1016/j.aiia.2021.02.001
[47]

Birrell S, Hughes J, Cai JY, Iida F. 2020. A field-tested robotic harvesting system for iceberg lettuce. Journal of Field Robotics 37:225−45

doi: 10.1002/rob.21888
[48]

Zhang W, Miao Z, Li N, He C, Sun T. 2022. Review of current robotic approaches for precision weed management. Current Rheumatology Reports 3:139−51

doi: 10.1007/s43154-022-00086-5
[49]

Monteiro A, Santos S, Gonçalves P. 2021. Precision agriculture for crop and livestock farming—brief review. Animals 11(8):2345

doi: 10.3390/ani11082345
[50]

Ahmed S, Marwat SNK, Brahim GB, Khan WU, Khan S, et al. 2024. IoT based intelligent pest management system for precision agriculture. Scientific Reports 14:31917

doi: 10.1038/s41598-024-83012-3
[51]

Tata JS, Kalidindi NKV, Katherapaka H, Julakal SK, Banothu M. 2022. Real-time quality assurance of fruits and vegetables with artificial intelligence. Journal of Physics: Conference Series 2325:012055

doi: 10.1088/1742-6596/2325/1/012055
[52]

Kim GW, Hong JP, Lee HY, Kwon JK, Kim DA, et al. 2022. Genomic selection with fixed-effect markers improves the prediction accuracy for Capsaicinoid contents in Capsicum annuum. Horticulture Research 9:uhac204

doi: 10.1093/hr/uhac204
[53]

Lara LA, Santos MF, Jank L, Chiari L, de M Vilela M, et al. 2019. Genomic selection with allele dosage in Panicum maximum Jacq. G3 Genes| Genomes| Genetics 9:2463−75

doi: 10.1534/g3.118.200986
[54]

Guo T, Yu X, Li X, Zhang H, Zhu C, et al. 2019. Optimal designs for genomic selection in hybrid crops. Molecular Plant 12:390−401

doi: 10.1016/j.molp.2018.12.022