{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T14:17:10Z","timestamp":1774966630049,"version":"3.50.1"},"reference-count":23,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,8,15]],"date-time":"2024-08-15T00:00:00Z","timestamp":1723680000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>In agriculture, the timely and accurate assessment of fruit ripeness is crucial to optimizing harvest planning and reduce waste. In this article, we explore the integration of two cutting-edge deep learning models, YOLOv9 and Swin Transformer, to develop a complex model for detecting strawberry ripeness. Trained and tested on a specially curated dataset, our model achieves a mean precision (mAP) of 87.3% by using the metric intersection over union (IoU) at a threshold of 0.5. This outperforms the model using YOLOv9 alone, which achieves an mAP of 86.1%. Our model also demonstrated improved precision and recall, with precision rising to 85.3% and recall rising to 84.0%, reflecting its ability to accurately and consistently detect different stages of strawberry ripeness.<\/jats:p>","DOI":"10.3390\/bdcc8080092","type":"journal-article","created":{"date-parts":[[2024,8,15]],"date-time":"2024-08-15T09:29:41Z","timestamp":1723714181000},"page":"92","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Strawberry Ripeness Detection Using Deep Learning Models"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-8669-0814","authenticated-orcid":false,"given":"Zhiyuan","family":"Mi","sequence":"first","affiliation":[{"name":"School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7443-3285","authenticated-orcid":false,"given":"Wei Qi","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,15]]},"reference":[{"key":"ref_1","unstructured":"Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., and Ding, G. 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