{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T17:18:55Z","timestamp":1789233535852,"version":"build-2803163510"},"reference-count":49,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T00:00:00Z","timestamp":1771891200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Plant Sci."],"abstract":"<jats:p>In response to the challenges posed by the large number of small targets, complex backgrounds and significant computational load involved in detecting olives, this study presents YOLO-TinyFuse, a lightweight detection model developed based on YOLOv8n. This model incorporates the P2 high-resolution feature layer, a ModifiedNeck cross-scale fusion structure (ModifiedNeck) and a bidirectional feature pyramid network (BiFPN) dynamic weighting module within a unified architecture. This architecture simultaneously preserves high-resolution feature representations, enhances bidirectional multi-scale interaction and optimises weighted feature aggregation. This synergistic design substantially improves the recognition of small objects while reducing model complexity further. Evaluations conducted on a multi-scenario olive phenotyping dataset demonstrate that YOLO-TinyFuse achieves an mAP50 of 92.3% and a Recall of 84.5%. This represents improvements of 2.6% and 3.2% respectively over YOLOv8n, while reducing the parameter count by 6.76%. These results confirm that the proposed model provides a deployable, computationally efficient, real-time solution for target recognition on mainstream edge computing platforms in automated olive harvesting scenarios, and offers a reusable, lightweight framework for agricultural small-object detection tasks requiring high performance and optimised computational efficiency.<\/jats:p>","DOI":"10.3389\/fpls.2026.1773377","type":"journal-article","created":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T06:46:49Z","timestamp":1771915609000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["A lightweight YOLO-TinyFuse model for small target detection of olive fruits"],"prefix":"10.3389","volume":"17","author":[{"given":"Xinyu","family":"Yang","sequence":"first","affiliation":[{"name":"College of Information Engineering, Sichuan Agricultural University","place":["Ya\u2019an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yichun","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Sichuan Agricultural University","place":["Ya\u2019an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiwen","family":"Xiao","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Sichuan Agricultural University","place":["Ya\u2019an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyao","family":"Liang","sequence":"additional","affiliation":[{"name":"College of Food Science, Sichuan Agricultural University","place":["Ya\u2019an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luyao","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Water Conservancy and Hydropower Engineering, Sichuan Agricultural University","place":["Ya\u2019an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaxi","family":"ShuoGuo","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Sichuan Agricultural University","place":["Ya\u2019an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kugu","family":"Ade","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Sichuan Agricultural University","place":["Ya\u2019an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaoguo","family":"Tong","sequence":"additional","affiliation":[{"name":"Panxi Crop Improvement Key Laboratory of Sichuan Province, Xichang University","place":["Xichang, Sichuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Arts and Media, Sichuan Agricultural University","place":["Ya\u2019an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Cao","sequence":"additional","affiliation":[{"name":"College of Water Conservancy and Hydropower Engineering, Sichuan Agricultural University","place":["Ya\u2019an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,2,24]]},"reference":[{"key":"B1","unstructured":"Bee image object detection\n          \n          \n            \n              Andrew\n              L. 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