{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:24:07Z","timestamp":1781108647402,"version":"3.54.1"},"reference-count":64,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,4,14]],"date-time":"2020-04-14T00:00:00Z","timestamp":1586822400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000980","name":"Grains Research and Development Corporation","doi-asserted-by":"publisher","award":["WCA00004"],"award-info":[{"award-number":["WCA00004"]}],"id":[{"id":"10.13039\/501100000980","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100011335","name":"Photonic Detection Systems","doi-asserted-by":"publisher","award":["WCA00004"],"award-info":[{"award-number":["WCA00004"]}],"id":[{"id":"10.13039\/100011335","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Weed invasions pose a threat to agricultural productivity. Weed recognition and detection play an important role in controlling weeds. The challenging problem of weed detection is how to discriminate between crops and weeds with a similar morphology under natural field conditions such as occlusion, varying lighting conditions, and different growth stages. In this paper, we evaluate a novel algorithm, filtered Local Binary Patterns with contour masks and coefficient k (k-FLBPCM), for discriminating between morphologically similar crops and weeds, which shows significant advantages, in both model size and accuracy, over state-of-the-art deep convolutional neural network (CNN) models such as VGG-16, VGG-19, ResNet-50 and InceptionV3. The experimental results on the \u201cbccr-segset\u201d dataset in the laboratory testbed setting show that the accuracy of CNN models with fine-tuned hyper-parameters is slightly higher than the k-FLBPCM method, while the accuracy of the k-FLBPCM algorithm is higher than the CNN models (except for VGG-16) for the more realistic \u201cfieldtrip_can_weeds\u201d dataset collected from real-world agricultural fields. However, the CNN models require a large amount of labelled samples for the training process. We conducted another experiment based on training with crop images at mature stages and testing at early stages. The k-FLBPCM method outperformed the state-of-the-art CNN models in recognizing small leaf shapes at early growth stages, with error rates an order of magnitude lower than CNN models for canola\u2013radish (crop\u2013weed) discrimination using a subset extracted from the \u201cbccr-segset\u201d dataset, and for the \u201cmixed-plants\u201d dataset. Moreover, the real-time weed\u2013plant discrimination time attained with the k-FLBPCM algorithm is approximately 0.223 ms per image for the laboratory dataset and 0.346 ms per image for the field dataset, and this is an order of magnitude faster than that of CNN models.<\/jats:p>","DOI":"10.3390\/s20082193","type":"journal-article","created":{"date-parts":[[2020,4,14]],"date-time":"2020-04-14T03:10:01Z","timestamp":1586833801000},"page":"2193","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Performances of the LBP Based Algorithm over CNN Models for Detecting Crops and Weeds with Similar Morphologies"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2665-9332","authenticated-orcid":false,"given":"Vi Nguyen Thanh","family":"Le","sequence":"first","affiliation":[{"name":"Electronic Science Research Institute, Edith Cowan University, Perth 6000, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1277-1000","authenticated-orcid":false,"given":"Selam","family":"Ahderom","sequence":"additional","affiliation":[{"name":"Electronic Science Research Institute, Edith Cowan University, Perth 6000, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kamal","family":"Alameh","sequence":"additional","affiliation":[{"name":"Electronic Science Research Institute, Edith Cowan University, Perth 6000, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/S0168-1699(03)00076-0","article-title":"Weed and crop discrimination using image analysis and artificial intelligence methods","volume":"39","author":"Aitkenhead","year":"2003","journal-title":"Comput. 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