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This paper proposes an improved YOLOv8 model to address the issues related to the maturity detection of Fuji apples grown in China using image\u2010based methods. The model was optimized in several ways according to the characteristics of apple targets and scenes. First, a lightweight MobileNetV3 is used as the backbone network, replacing the original CSPDarknet\u201053 backbone network, which reduces the model parameters and computational complexity and increases the inference speed. Second, by introducing the efficient multiscale attention (EMA) module and using the bidirectional feature pyramid network (BiFPN) in the neck part, the model enhances the extraction capability of important features and suppresses redundant features, thus improving the model\u2019s generalization ability. Experimental results show that the size of the model is 2.6 megabytes. On the apple dataset, its precision, recall, F1 score, and mean average precision reach 90.2%, 88.5%, 89.3%, and 91.3%, respectively, with improvements of 4.3%, 3.2%, 3.7%, and 2.6% compared to the original model. Based on this model, an Android application has been developed for real\u2010time apple maturity detection. The improved model proposed in this paper achieves real\u2010time apple target recognition and maturity detection, providing quick and accurate target recognition guidance for the mechanical automatic harvesting of apples.<\/jats:p>","DOI":"10.1155\/cplx\/6666447","type":"journal-article","created":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T15:43:02Z","timestamp":1766418182000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Real\u2010Time Apple Maturity Detection Method Combining Lightweight Networks and Multiscale Attention Mechanisms"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-8017-3356","authenticated-orcid":false,"given":"Yonglin","family":"Gao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7073-7928","authenticated-orcid":false,"given":"Zhong","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0956-9670","authenticated-orcid":false,"given":"Dongdong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,12,22]]},"reference":[{"key":"e_1_2_12_1_2","volume-title":"FAOSTAT Food and Agriculture Organization of the United Nations","author":"Faostat","year":"2024"},{"key":"e_1_2_12_2_2","doi-asserted-by":"publisher","DOI":"10.3969\/j.issn.1000-6362.2020.07.003"},{"key":"e_1_2_12_3_2","volume-title":"Understanding the Dynamics of Hand Picking Patterns of Fresh Market Apples","author":"Tong J.","year":"2014"},{"key":"e_1_2_12_4_2","first-page":"1","article-title":"Convolutional Neural Networks (CNN) for Detecting Fruit Information Using Machine Learning Techniques","volume":"22","author":"Risdin F.","year":"2020","journal-title":"IOSR Journal of Computer Engineering"},{"key":"e_1_2_12_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105348"},{"key":"e_1_2_12_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2019.107036"},{"key":"e_1_2_12_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11119-020-09709-3"},{"key":"e_1_2_12_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/s41651-023-00163-z"},{"key":"e_1_2_12_9_2","doi-asserted-by":"publisher","DOI":"10.25165\/j.ijabe.20241702.8574"},{"key":"e_1_2_12_10_2","doi-asserted-by":"publisher","DOI":"10.3390\/s24113610"},{"key":"e_1_2_12_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-024-11536-w"},{"key":"e_1_2_12_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cropro.2024.106758"},{"key":"e_1_2_12_13_2","first-page":"71","article-title":"Apple Skin Colour Changes During Harvest as an Indicator of Maturity","volume":"13","author":"\u0141ysiak G.","year":"2014","journal-title":"Acta Scientiarum Polonorum"},{"key":"e_1_2_12_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11119-012-9269-2"},{"key":"e_1_2_12_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2022.01.135"},{"key":"e_1_2_12_16_2","doi-asserted-by":"publisher","DOI":"10.3390\/machines11070677"},{"key":"e_1_2_12_17_2","doi-asserted-by":"publisher","DOI":"10.1049\/ipr2.13073"},{"key":"e_1_2_12_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2016.91"},{"key":"e_1_2_12_19_2","doi-asserted-by":"publisher","DOI":"10.3390\/agriculture15010075"},{"key":"e_1_2_12_20_2","doi-asserted-by":"publisher","DOI":"10.4236\/jcc.2023.117014"},{"key":"e_1_2_12_21_2","doi-asserted-by":"publisher","DOI":"10.3390\/agriculture14030353"},{"key":"e_1_2_12_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP49357.2023.10096516"},{"key":"e_1_2_12_23_2","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1651\/1\/012114"},{"key":"e_1_2_12_24_2","doi-asserted-by":"crossref","unstructured":"KonaiteM. 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