{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T18:03:19Z","timestamp":1779818599828,"version":"3.53.1"},"reference-count":27,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T00:00:00Z","timestamp":1767830400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T00:00:00Z","timestamp":1767830400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014206","name":"National Key Laboratory Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100014206","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["scijournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Sci Food Agric"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>BACKGROUND<\/jats:title>\n                    <jats:p>Accurate and real\u2010time detection of tea leaf buds is a fundamental requirement for intelligent tea harvesting and smart agriculture. However, achieving high detection accuracy for small targets under complex tea plantation environments remains challenging, particularly for deployment on resource\u2010constrained devices. Existing object detection models often suffer from excessive computational complexity or insufficient performance when applied to such scenarios. Therefore, it is necessary to develop a lightweight detection framework that balances detection accuracy and computational efficiency.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>To address these challenges, this study proposes a lightweight object detection model named TeaBudLiteNet. The model introduces a novel C2f_PConv module, which integrates the computational efficiency of PConv with the non\u2010linear feature representation capability of the C2f module, achieving an effective trade\u2010off between accuracy and efficiency. In addition, the SimAM_Slice attention mechanism is incorporated to enhance feature weighting across different scales, thereby improving small target detection. The Focaler\u2010IoU_Inner regression loss function is further employed to dynamically optimize sample importance and accelerate model convergence, enhancing generalization and adaptability. Experimental results demonstrate that TeaBudLiteNet outperforms mainstream detection models in terms of accuracy, model size and inference speed. Specifically, the model achieves a precision of 90.47%, representing an improvement of 2.08% over the baseline. The parameter count is reduced to 1\u2009912\u2009947, achieving more than a 90% reduction in model size compared to conventional approaches, at the same time as maintaining a high inference speed of 227 frames per second.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSION<\/jats:title>\n                    <jats:p>The proposed TeaBudLiteNet effectively reduces model complexity at the same time as preserving high detection accuracy and real\u2010time performance. Its lightweight architecture and superior efficiency make it highly suitable for deployment on resource\u2010limited smart agricultural devices. By providing an efficient and robust solution for tea leaf bud detection in complex environments, this study demonstrates significant potential for automating tea harvesting and contributes to the advancement of intelligent agricultural systems. \u00a9 2026 Society of Chemical Industry.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jsfa.70441","type":"journal-article","created":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T06:54:54Z","timestamp":1767941694000},"page":"3436-3450","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>TeaBudLiteNet<\/scp>\n                    : a lightweight network for detecting tea leaf buds"],"prefix":"10.1002","volume":"106","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-8589-4615","authenticated-orcid":false,"given":"Xiaolei","family":"Chen","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology Zhejiang Sci\u2010Tech University  Hangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Long","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology Zhejiang Sci\u2010Tech University  Hangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology Zhejiang Sci\u2010Tech University  Hangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology Zhejiang Sci\u2010Tech University  Hangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuyu","family":"Chen","sequence":"additional","affiliation":[{"name":"Keyi College of Zhejiang Sci\u2010Tech University  Shaoxing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiemin","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology Zhejiang Sci\u2010Tech University  Hangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Optical Target Simulation and Test Technology Harbin Institute of Technology  Harbin China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianlong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Optical Target Simulation and Test Technology Harbin Institute of Technology  Harbin China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,1,8]]},"reference":[{"key":"e_1_2_11_2_1","first-page":"217","article-title":"Tea bud detection based on faster R\u2010CNN network","volume":"53","author":"Zhu HC","year":"2022","journal-title":"Trans Chin Soc Agric Mach"},{"key":"e_1_2_11_3_1","doi-asserted-by":"crossref","unstructured":"LiuT BaiL RayhanaR ZouXandLiuZ TAME\u2010faster R\u2010CNN model for image\u2010based tea diseases detection. 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