{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T18:20:44Z","timestamp":1792002044981,"version":"4.4.0"},"reference-count":39,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T00:00:00Z","timestamp":1747958400000},"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:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>To achieve fast detection of pear fruits in natural pear orchards and optimize path planning for harvesting robots, this study proposes the AHG-YOLO model for multi-category detection of pear fruit occlusion in complex orchard environments.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Using the Red Delicious pear as the research object, the pears are classified into three categories based on different occlusion statuses: non-occluded fruits (NO), fruits occluded by leaves\/branches (OBL), and fruits in close contact with other fruits but not obstructed by leaves\/branches (FCC). The YOLOv11n model is used as the base model for a lightweight design. First, the sampling method in the backbone and neck networks is replaced with ADown downsampling to capture higher-level image features, reducing floating-point operations and computational complexity. Next, shared weight parameters are introduced in the head network, and group convolution is applied to achieve a lightweight detection head. Finally, the boundary box loss function is changed to Generalized Intersection over Union (GIoU), improving the model\u2019s convergence speed and further enhancing detection performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Experimental results show that the AHG-YOLO model achieves 93.5% (FCC), 95.3% (NO), and 93.4% (OBL) in AP, with an mAP@0.5 of 94.1% across all categories. Compared to the base YOLOv11n network, precision, recall, mAP@0.5, and mAP@0.5:0.95 are improved by 2.5%, 3.6%, 2.3%, and 2.6%, respectively. The model size is only 5.1MB, with a 16.9% reduction in the number of parameters.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The improved model demonstrates enhanced suitability for deployment on pear-harvesting embedded devices, providing technical support for the path planning of fruit-picking robotic arms.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpls.2025.1580325","type":"journal-article","created":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T01:30:45Z","timestamp":1747963845000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":20,"title":["AHG-YOLO: multi-category detection for occluded pear fruits in complex orchard scenes"],"prefix":"10.3389","volume":"16","author":[{"given":"Na","family":"Ma","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yile","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenfei","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zonglin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiyan","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,5,23]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2406.10139","article-title":"YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domain","volume":"13","author":"Alif","year":"2024","journal-title":"arxiv preprint arxiv:2406.10139"},{"key":"B2","doi-asserted-by":"publisher","first-page":"154","DOI":"10.51470\/PLANTARCHIVES.2024.v24.SP-GABELS.023","article-title":"Agricultural fruit harvesting robot: An overview of digital agriculture","volume":"24","author":"Bharad","year":"2024","journal-title":"Plant Arch."},{"key":"B3","doi-asserted-by":"publisher","first-page":"1-2","DOI":"10.3390\/agronomy14102233","article-title":"Key technologies for autonomous fruit-and vegetable-picking robots: A review","volume":"14","author":"Chen","year":"2024","journal-title":"Agronomy"},{"key":"B4","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.aiia.2022.09.007","article-title":"Deep learning based computer vision approaches for smart agricultural applications","volume":"6","author":"Dhanya","year":"2022","journal-title":"Artif. 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