Plant Gene and Trait 2026, Vol.17, No.3, 182-196 http://genbreedpublisher.com/index.php/pgt 196 Luo L.F., Liu W.T., Lu Q.H., Wang J.H., Wen W.C., Yan D., and Tang Y.C., 2021, Grape berry detection and size measurement based on edge image processing and geometric morphology, Machines, 9(10): 233. https://doi.org/10.3390/machines9100233 Meneses M., Muñoz-Espinoza C., Reyes-Impellizzeri S., Salazar E., Meneses C., Herzog K., and Hinrichsen P., 2025, Characterization of bunch compactness in a diverse collection of Vitis vinifera L. genotypes enriched in table grape cultivars reveals new candidate genes associated with berry number, Plants, 14(9): 1308. https://doi.org/10.3390/plants14091308 Milišić K., Banjanin T., Vasić Z., Matijašević S., and Jančić R., 2025, Yield and cluster characteristics of grapevine varieties grown in the gene bank of the experimental vineyard Radmilovac, Serbia, AgroReS, 14: 41-47. https://doi.org/10.63356/agrores.2025.005 Mirbod O., Yoder L., and Nuske S., 2016, Automated measurement of berry size in images, IFAC-PapersOnLine, 49(16): 79-84. https://doi.org/10.1016/j.ifacol.2016.10.015 Mucalo A., Matić D., Morić-Španić A., and Čagalj M., 2024, Satellite solutions for precision viticulture: enhancing sustainability and efficiency in vineyard management, Agronomy, 14(8): 1862. https://doi.org/10.3390/agronomy14081862 Roscher R., Herzog K., Kunkel A., Kicherer A., Töpfer R., and Förstner W., 2014, Automated image analysis framework for the high-throughput determination of grapevine berry sizes using conditional random fields, Computers and Electronics in Agriculture, 100: 148-158. https://doi.org/10.1016/j.compag.2013.11.008 Sabir A., Kilinc S., and Sabir F., 2020, Qualitative and quantitative responses of early ripening table grape cultivars (Vitis vinifera L.) to pollination treatments under controlled growing condition, Erwerbs-Obstbau, 62: 75-80. https://doi.org/10.1007/s10341-020-00499-6 Sharma S., Jr Munoz J.R., Torres-Lomas E., Lin J., Banayad H., Lupo Y., Nunez V., Gaspar A., CantùD., and Diaz-Garcia L., 2025, Leveraging foundation models to dissect the genetic basis of cluster compactness and yield in grapevine, Scientific Reports, 16: 1434. https://doi.org/10.1038/s41598-025-31531-y Somogyi E., Kun Á., Lazar J., Bodor-Pesti P., and Sárdy D.A.N., 2021, Quantitative analysis of the berry size in grapevine cultivar ‘Italia’: digital image analysis of the grapevine, Progress in Agricultural Engineering Sciences, 17(S1): 53-60. https://doi.org/10.1556/446.2021.30007 Thorat K.D., Upadhyay A., Samarth R.R., Machchhindra S.R., Jagtap M.A., Kushwaha K., Kesharwani P.K., Gaikwad P.S., Gawande D.N., and Somkuwar R.G., 2024, Genome-wide association analysis to identify genomic regions and predict candidate genes for bunch traits in grapes (Vitis vinifera L.), Scientia Horticulturae, 328: 112882. https://doi.org/10.1016/j.scienta.2024.112882 Torres-Lomas E., Lado-Jimena J., Garcia-Zamora G., and Diaz-Garcia L., 2024, Segment anything for comprehensive analysis of grapevine cluster architecture and berry properties, Plant Phenomics, 6: 0202. https://doi.org/10.34133/plantphenomics.0202 Trivedi M., Zhou Y., Moon J., Meyers J., Jiang Y., Lu G., and Vanden Heuvel J.E., 2023, A preliminary method for tracking in-season grapevine cluster closure using image segmentation and image thresholding, Australian Journal of Grape and Wine Research, 2023(2): 1-12. https://doi.org/10.1155/2023/3923839 Upadhyaya P., Karkee M., Kshetri S., and Paudel A., 2023, Automated lag-phase detection in wine grapes using a mobile vision system, Smart Agricultural Technology, 6: 100381. https://doi.org/10.1016/j.atech.2023.100381 Zhang Y.Y., Wang Y.J., Henke M., Carbonell-Bejerano P., Wang Z.M., Bert P.F., Wang Y., Li H.Y., Kong J.H., Fan P.G., Dai Z.W., and Liang Z.C., 2025, Integrating dense genotyping with high-throughput phenotyping empowers the genetic dissection of berry quality and resilience traits in grapevine, Advanced Science, 12(20): e2412587. https://doi.org/10.1002/advs.202412587
RkJQdWJsaXNoZXIy MjQ4ODYzNA==