{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T01:55:07Z","timestamp":1774317307655,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,3,3]],"date-time":"2021-03-03T00:00:00Z","timestamp":1614729600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Deep learning convolutional neural networks (CNNs) are an emerging technology that provide an opportunity to increase agricultural efficiency through remote sensing and automatic inferencing of field conditions. This paper examined the novel use of CNNs to identify two weeds, hair fescue and sheep sorrel, in images of wild blueberry fields. Commercial herbicide sprayers provide a uniform application of agrochemicals to manage patches of these weeds. Three object-detection and three image-classification CNNs were trained to identify hair fescue and sheep sorrel using images from 58 wild blueberry fields. The CNNs were trained using 1280x720 images and were tested at four different internal resolutions. The CNNs were retrained with progressively smaller training datasets ranging from 3780 to 472 images to determine the effect of dataset size on accuracy. YOLOv3-Tiny was the best object-detection CNN, detecting at least one target weed per image with F1-scores of 0.97 for hair fescue and 0.90 for sheep sorrel at 1280 \u00d7 736 resolution. Darknet Reference was the most accurate image-classification CNN, classifying images containing hair fescue and sheep sorrel with F1-scores of 0.96 and 0.95, respectively at 1280 \u00d7 736. MobileNetV2 achieved comparable results at the lowest resolution, 864 \u00d7 480, with F1-scores of 0.95 for both weeds. Training dataset size had minimal effect on accuracy for all CNNs except Darknet Reference. This technology can be used in a smart sprayer to control target specific spray applications, reducing herbicide use. Future work will involve testing the CNNs for use on a smart sprayer and the development of an application to provide growers with field-specific information. Using CNNs to improve agricultural efficiency will create major cost-savings for wild blueberry producers.<\/jats:p>","DOI":"10.3390\/rs13050943","type":"journal-article","created":{"date-parts":[[2021,3,3]],"date-time":"2021-03-03T20:33:57Z","timestamp":1614803637000},"page":"943","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Hair Fescue and Sheep Sorrel Identification Using Deep Learning in Wild Blueberry Production"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0786-7382","authenticated-orcid":false,"given":"Patrick J.","family":"Hennessy","sequence":"first","affiliation":[{"name":"Department of Engineering, Faculty of Agriculture, Dalhousie University, Truro, NS B2N 5E3, Canada"}]},{"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"}]},{"given":"Aitazaz A.","family":"Farooque","sequence":"additional","affiliation":[{"name":"School of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada"}]},{"given":"Arnold W.","family":"Schumann","sequence":"additional","affiliation":[{"name":"Citrus Research and Education Center, University of Florida, Lake Alfred, FL 33850, USA"}]},{"given":"Qamar U.","family":"Zaman","sequence":"additional","affiliation":[{"name":"Department of Engineering, Faculty of Agriculture, Dalhousie University, Truro, NS B2N 5E3, Canada"}]},{"given":"Kenny W.","family":"Corscadden","sequence":"additional","affiliation":[{"name":"Centre for Technology, Environment &amp; Design, Lethbridge College, Lethbridge, AB T1K 1L6, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"415","DOI":"10.5962\/p.346999","article-title":"The biological flora of Canada 1. 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