{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T04:28:09Z","timestamp":1775276889439,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,12,15]],"date-time":"2024-12-15T00:00:00Z","timestamp":1734220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Sciences and Engineering Research Council (NSERC) of Canada Discovery Grants Program","award":["RGPIN-06295-2019"],"award-info":[{"award-number":["RGPIN-06295-2019"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>This study introduced a novel approach to 3D image segmentation utilizing a neural network framework applied to 2D depth map imagery, with Z axis values visualized through color gradation. This research involved comprehensive data collection from mechanically harvested wild blueberries to populate 3D and red\u2013green\u2013blue (RGB) images of filled totes through time-of-flight and RGB cameras, respectively. Advanced neural network models from the YOLOv8 and Detectron2 frameworks were assessed for their segmentation capabilities. Notably, the YOLOv8 models, particularly YOLOv8n-seg, demonstrated superior processing efficiency, with an average time of 18.10 ms, significantly faster than the Detectron2 models, which exceeded 57 ms, while maintaining high performance with a mean intersection over union (IoU) of 0.944 and a Matthew\u2019s correlation coefficient (MCC) of 0.957. A qualitative comparison of segmentation masks indicated that the YOLO models produced smoother and more accurate object boundaries, whereas Detectron2 showed jagged edges and under-segmentation. Statistical analyses, including ANOVA and Tukey\u2019s HSD test (\u03b1 = 0.05), confirmed the superior segmentation performance of models on depth maps over RGB images (p &lt; 0.001). This study concludes by recommending the YOLOv8n-seg model for real-time 3D segmentation in precision agriculture, providing insights that can enhance volume estimation, yield prediction, and resource management practices.<\/jats:p>","DOI":"10.3390\/jimaging10120324","type":"journal-article","created":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T09:17:58Z","timestamp":1734340678000},"page":"324","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Exploiting 2D Neural Network Frameworks for 3D Segmentation Through Depth Map Analytics of Harvested Wild Blueberries (Vaccinium angustifolium Ait.)"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-4633-0941","authenticated-orcid":false,"given":"Connor C.","family":"Mullins","sequence":"first","affiliation":[{"name":"Department of Engineering, Faculty of Agriculture, Dalhousie University, Truro, NS B2N 5E3, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1513-1471","authenticated-orcid":false,"given":"Travis J.","family":"Esau","sequence":"additional","affiliation":[{"name":"Department of Engineering, Faculty of Agriculture, Dalhousie University, Truro, NS B2N 5E3, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qamar U.","family":"Zaman","sequence":"additional","affiliation":[{"name":"Department of Engineering, Faculty of Agriculture, Dalhousie University, Truro, NS B2N 5E3, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmad A.","family":"Al-Mallahi","sequence":"additional","affiliation":[{"name":"Department of Engineering, Faculty of Agriculture, Dalhousie University, Truro, NS B2N 5E3, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aitazaz A.","family":"Farooque","sequence":"additional","affiliation":[{"name":"Faculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada"},{"name":"Canadian Center for Climate Change and Adaptation, University of Prince Edward Island, St Peters Bay, PE C0A 2A0, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1007\/s11119-017-9557-y","article-title":"Machine Vision Smart Sprayer for Spot-Application of Agrochemical in Wild Blueberry Fields","volume":"19","author":"Esau","year":"2018","journal-title":"Precis. 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