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Rahman , \u00abPerformance evaluation of deep learning object detectors for weed,\u00bb Smart Agricultural Technology , October 2023 . A. Rahman , \u00abPerformance evaluation of deep learning object detectors for weed,\u00bb Smart Agricultural Technology, October 2023."},{"key":"e_1_3_2_1_5_1","volume-title":"January","author":"Ong P.","year":"2023","unstructured":"P. Ong , \u00abUAV-based weed detection in Chinese cabbage using deep learning,\u00bb Smart Agricultural Technology , January 2023 . P. Ong , \u00abUAV-based weed detection in Chinese cabbage using deep learning,\u00bb Smart Agricultural Technology, January 2023."},{"key":"e_1_3_2_1_6_1","volume-title":"December","author":"Imran M. S.","year":"2023","unstructured":"M. S. Imran , \u00abTowards automated weed detection through two-stage semantic segmentation of tobacco and weed pixels in aerial Imagery,\u00bb Smart Agricultural Technology , December 2023 . M. S. 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Strothmann \u00abPlant classification with In-Field-Labeling for crop\/weed discrimination using spectral features and 3D surface features from a multi-wavelength laser line profile system \u00bb Computers and Electronics in Agriculture p. P. 79\u201393 January 2017.  W. Strothmann \u00abPlant classification with In-Field-Labeling for crop\/weed discrimination using spectral features and 3D surface features from a multi-wavelength laser line profile system \u00bb Computers and Electronics in Agriculture p. P. 79\u201393 January 2017.","DOI":"10.1016\/j.compag.2017.01.003"},{"key":"e_1_3_2_1_43_1","volume-title":"Tokushima","author":"Amziane A.","year":"2021","unstructured":"A. Amziane , \u00abWeed detection by analysis of multispectral images acquired under uncontrolled illumination conditions,\u00bb chez Fifteenth International Conference on Quality Control by Artificial Vision , Tokushima , Japan , 2021 . A. Amziane , \u00abWeed detection by analysis of multispectral images acquired under uncontrolled illumination conditions,\u00bb chez Fifteenth International Conference on Quality Control by Artificial Vision, Tokushima, Japan, 2021."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.3390\/s20247262"},{"key":"e_1_3_2_1_45_1","volume-title":"6th International Conference for Convergence in Technology","author":"S. Badhan","year":"2021","unstructured":"S. Badhan , \u00ab Real-Time Weed Detection using Machine and Stereo-Vision,\u00bb chez 6th International Conference for Convergence in Technology , Pune, India , 2021 . S. Badhan , \u00abReal-Time Weed Detection using Machine and Stereo-Vision,\u00bb chez 6th International Conference for Convergence in Technology, Pune, India, 2021."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2023.1096802"},{"key":"e_1_3_2_1_47_1","volume-title":"July","author":"Pei H.","year":"2022","unstructured":"H. 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Tanji , \u00abTechniques de production du fraisier: r\u00e9sultats d'enqu\u00eates au Loukkos,\u00bb vol. Vo, n\u00b0 %1No 201, pp. p. 2-8, Juillet 2014."},{"key":"e_1_3_2_1_51_1","volume-title":"Hong Kong","author":"Xinshao W.","year":"2015","unstructured":"W. Xinshao and C. Cheng , \u00abWeed seeds classification based on PCANet deep learning baseline,\u00bb chez 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference , Hong Kong , 2015 . W. Xinshao and C. Cheng, \u00abWeed seeds classification based on PCANet deep learning baseline,\u00bb chez 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference , Hong Kong, 2015."},{"key":"e_1_3_2_1_52_1","first-page":"226","article-title":"\u00abA review on weed detection using ground-based machine vision and image processing techniques,\u00bb Computers and Electronics in Agriculture, vol","volume":"158","author":"Wang A.","year":"2019","unstructured":"A. Wang , \u00abA review on weed detection using ground-based machine vision and image processing techniques,\u00bb Computers and Electronics in Agriculture, vol . Vo. 158 , pp. p. 226 - 240 , March 2019 . A. Wang , \u00abA review on weed detection using ground-based machine vision and image processing techniques,\u00bb Computers and Electronics in Agriculture, vol. Vo. 158, pp. p. 226-240, March 2019.","journal-title":"Vo."},{"key":"e_1_3_2_1_53_1","first-page":"322","volume-title":"Sci.","volume":"156","author":"Kamilaris A.","year":"2018","unstructured":"A. Kamilaris and F. Prenafeta-Bold\u00fa , \u00abA review of the use of convolutional neural networks in agriculture,\u00bb Agric . Sci. , vol. Vo. 156 , p. P. 312\u2013 322 , 2018 . A. Kamilaris and F. Prenafeta-Bold\u00fa, \u00abA review of the use of convolutional neural networks in agriculture,\u00bb Agric. Sci., vol. Vo. 156, p. 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