{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T14:44:00Z","timestamp":1774968240251,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,5,31]],"date-time":"2021-05-31T00:00:00Z","timestamp":1622419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This study aimed to propose an approach for orchard trees segmentation using aerial images based on a deep learning convolutional neural network variant, namely the U-net network. The purpose was the automated detection and localization of the canopy of orchard trees under various conditions (i.e., different seasons, different tree ages, different levels of weed coverage). The implemented dataset was composed of images from three different walnut orchards. The achieved variability of the dataset resulted in obtaining images that fell under seven different use cases. The best-trained model achieved 91%, 90%, and 87% accuracy for training, validation, and testing, respectively. The trained model was also tested on never-before-seen orthomosaic images or orchards based on two methods (oversampling and undersampling) in order to tackle issues with out-of-the-field boundary transparent pixels from the image. Even though the training dataset did not contain orthomosaic images, it achieved performance levels that reached up to 99%, demonstrating the robustness of the proposed approach.<\/jats:p>","DOI":"10.3390\/s21113813","type":"journal-article","created":{"date-parts":[[2021,5,31]],"date-time":"2021-05-31T21:42:06Z","timestamp":1622497326000},"page":"3813","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Orchard Mapping with Deep Learning Semantic Segmentation"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2277-5846","authenticated-orcid":false,"given":"Athanasios","family":"Anagnostis","sequence":"first","affiliation":[{"name":"Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology\u2013Hellas (CERTH), GR57001 Thessaloniki, Greece"},{"name":"Department of Computer Science & Telecommunications, University of Thessaly, GR35131 Lamia, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5743-625X","authenticated-orcid":false,"given":"Aristotelis C.","family":"Tagarakis","sequence":"additional","affiliation":[{"name":"Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology\u2013Hellas (CERTH), GR57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5731-9472","authenticated-orcid":false,"given":"Dimitrios","family":"Kateris","sequence":"additional","affiliation":[{"name":"Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology\u2013Hellas (CERTH), GR57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vasileios","family":"Moysiadis","sequence":"additional","affiliation":[{"name":"Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology\u2013Hellas (CERTH), GR57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Claus Gr\u00f8n","family":"S\u00f8rensen","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Aarhus University, DK-8000 Aarhus C, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4297-4837","authenticated-orcid":false,"given":"Simon","family":"Pearson","sequence":"additional","affiliation":[{"name":"Lincoln Institute for Agri-Food Technology (LIAT), University of Lincoln, Lincoln LN6 7TS, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7058-5986","authenticated-orcid":false,"given":"Dionysis","family":"Bochtis","sequence":"additional","affiliation":[{"name":"Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology\u2013Hellas (CERTH), GR57001 Thessaloniki, Greece"},{"name":"farmB Digital Agriculture P.C., Doiranis 17, GR54639 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.protcy.2013.11.043","article-title":"Precision Agriculture Application in Fruit Crops: Experience in Handpicked Fruits","volume":"8","author":"Gemtos","year":"2013","journal-title":"Procedia Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.biosystemseng.2012.08.009","article-title":"Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps","volume":"114","author":"Mulla","year":"2013","journal-title":"Biosyst. 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