A vision-based hybrid approach for identification of Anthurium flower cultivars
•Flowers cultivar identification is a key step for subsequent classification tasks.•Anthurium flowers could be identified based on their spadix.•The Viola-Jones algorithm was used to detect the spadix of Anthurium flower.•Computation time is a constraint especially for matching with a lot of templat...
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| Published in: | Computers and electronics in agriculture Vol. 174; p. 105460 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.07.2020
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| ISSN: | 0168-1699, 1872-7107 |
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| Abstract | •Flowers cultivar identification is a key step for subsequent classification tasks.•Anthurium flowers could be identified based on their spadix.•The Viola-Jones algorithm was used to detect the spadix of Anthurium flower.•Computation time is a constraint especially for matching with a lot of templates.•Application of the Viola-Jones algorithm decreased computation time significantly.
A hybrid approach was developed for highly accurate and effective identification of Anthurium flower cultivars in a computer vision-based sorting machine. Anthurium flowers have a small spike-shaped inflorescence called spadix. These flowers are distinguishable according to the color scheme of the spadix region. In the developed cultivar classification algorithm, the spadix region of test images was detected using the Viola-Jones object detection algorithm. The Viola-Jones detector was trained by positive images prepared from different cultivars of Anthurium flower, and the Oxford Flowers 17 dataset was used as negative images. Then, the detected region as Region of Interest (ROI) matched with images of various cultivars at different sizes and angles of rotation templates as a multi-template matching approach, in which each image was representative of a specified cultivar. The experiment results indicate that the proposed technique has acceptable performance in detecting the spadix region and inspiring performance in classifying the flower cultivars. At different conditions of the templates used for classification, the computation time as a critical criterion for real-time classification was less than 0.5 s, with the classification accuracy of more than 99%. In an automatic grading machine for flowers, cultivar classification of flowers is an important step for subsequent grading tasks. |
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| AbstractList | •Flowers cultivar identification is a key step for subsequent classification tasks.•Anthurium flowers could be identified based on their spadix.•The Viola-Jones algorithm was used to detect the spadix of Anthurium flower.•Computation time is a constraint especially for matching with a lot of templates.•Application of the Viola-Jones algorithm decreased computation time significantly.
A hybrid approach was developed for highly accurate and effective identification of Anthurium flower cultivars in a computer vision-based sorting machine. Anthurium flowers have a small spike-shaped inflorescence called spadix. These flowers are distinguishable according to the color scheme of the spadix region. In the developed cultivar classification algorithm, the spadix region of test images was detected using the Viola-Jones object detection algorithm. The Viola-Jones detector was trained by positive images prepared from different cultivars of Anthurium flower, and the Oxford Flowers 17 dataset was used as negative images. Then, the detected region as Region of Interest (ROI) matched with images of various cultivars at different sizes and angles of rotation templates as a multi-template matching approach, in which each image was representative of a specified cultivar. The experiment results indicate that the proposed technique has acceptable performance in detecting the spadix region and inspiring performance in classifying the flower cultivars. At different conditions of the templates used for classification, the computation time as a critical criterion for real-time classification was less than 0.5 s, with the classification accuracy of more than 99%. In an automatic grading machine for flowers, cultivar classification of flowers is an important step for subsequent grading tasks. A hybrid approach was developed for highly accurate and effective identification of Anthurium flower cultivars in a computer vision-based sorting machine. Anthurium flowers have a small spike-shaped inflorescence called spadix. These flowers are distinguishable according to the color scheme of the spadix region. In the developed cultivar classification algorithm, the spadix region of test images was detected using the Viola-Jones object detection algorithm. The Viola-Jones detector was trained by positive images prepared from different cultivars of Anthurium flower, and the Oxford Flowers 17 dataset was used as negative images. Then, the detected region as Region of Interest (ROI) matched with images of various cultivars at different sizes and angles of rotation templates as a multi-template matching approach, in which each image was representative of a specified cultivar. The experiment results indicate that the proposed technique has acceptable performance in detecting the spadix region and inspiring performance in classifying the flower cultivars. At different conditions of the templates used for classification, the computation time as a critical criterion for real-time classification was less than 0.5 s, with the classification accuracy of more than 99%. In an automatic grading machine for flowers, cultivar classification of flowers is an important step for subsequent grading tasks. |
| ArticleNumber | 105460 |
| Author | Soleimanipour, A. Chegini, G.R. |
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| CitedBy_id | crossref_primary_10_3390_agronomy13051271 crossref_primary_10_3390_agronomy11101980 crossref_primary_10_1016_j_scienta_2024_113561 crossref_primary_10_1155_2021_8555280 crossref_primary_10_3389_fpls_2023_1221346 crossref_primary_10_3390_horticulturae8010021 crossref_primary_10_3390_math10152767 crossref_primary_10_1007_s11416_023_00499_6 crossref_primary_10_1016_j_compag_2023_108592 crossref_primary_10_1109_TFUZZ_2022_3177764 crossref_primary_10_3389_fpls_2023_1281386 crossref_primary_10_1016_j_compag_2023_108345 crossref_primary_10_1109_ACCESS_2023_3244386 crossref_primary_10_3390_s23041818 |
| Cites_doi | 10.1117/12.406527 10.1016/j.postharvbio.2018.01.013 10.1109/ICSTCC.2015.7321390 10.1002/tax.583020 10.1631/jzus.2004.0764 10.17660/ActaHortic.1995.405.19 10.1007/978-3-319-14654-6_5 10.1016/j.scienta.2014.11.024 10.1109/CVPR.2000.855895 10.1016/j.compag.2014.07.004 10.1109/TENCON.2016.7848439 10.1016/j.patrec.2015.10.014 10.1109/CVPR.2006.42 10.1016/j.scienta.2016.05.021 10.1016/j.compag.2016.03.020 10.1016/j.scienta.2008.11.039 10.1109/MVA.2015.7153241 10.1016/j.foodres.2014.03.012 10.1016/j.compag.2016.08.001 10.1016/j.compag.2016.09.002 10.1007/s00138-014-0612-7 10.1016/j.robot.2004.08.011 10.1348/147608308X371778 10.1016/j.imavis.2017.01.013 10.1016/j.compag.2015.05.020 10.1016/j.procs.2015.02.137 10.1016/j.compag.2015.10.009 10.1016/S0031-3203(99)00176-4 10.1023/B:VISI.0000013087.49260.fb 10.1016/j.scienta.2005.01.022 10.1007/978-3-540-88693-8_9 10.1016/j.imavis.2009.10.001 10.1016/j.biosystemseng.2009.06.015 10.1016/j.jpdc.2013.01.012 10.1016/0168-1699(95)00056-9 10.1016/j.compag.2016.01.001 10.1016/j.compind.2018.03.007 10.1007/s11042-017-4415-5 10.1016/j.compag.2015.08.026 10.1049/iet-bmt.2016.0037 10.1109/ICVGIP.2008.47 10.1007/978-3-642-33709-3_36 10.1007/s11119-007-9037-x 10.17660/ActaHortic.2001.562.43 10.17660/ActaHortic.1998.421.8 10.1016/j.engappai.2005.05.009 10.1109/HIPC.2009.5433189 |
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| References | Parsons, Edmondson, Song (b0195) 2009; 104 Tadashi Higaki, J.S.L., 1995. Anthurium culture in Hawaii. https://doi.org/http://hdl.handle.net/10125/5482. Murphy, Broussard, Schultz, Rakvic, Ngo (b0150) 2017; 6 Nilsback, M.E., Zisserman, A., 2006. A visual vocabulary for flower classification, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. pp. 1447–1454. https://doi.org/10.1109/CVPR.2006.42. Guru, Sharath, Manjunath (b0060) 2010 Juman, Wong, Rajkumar, Goh (b0095) 2016; 128 Johansson, Pahlberg, Hagman (b0090) 2015; 118 Viola, Jones (b0260) 2004; 57 Nair, D., Rajagopal, R., Wenzel, L., 2000. Pattern matching based on a generalized Fourier transform, in: Advanced Signal Processing Algorithms, Architectures, and Implementations X. pp. 472–481. Belhumeur, P.N., Chen, D., Feiner, S., Jacobs, D.W., Kress, W.J., Ling, H., Lopez, I., Ramamoorthi, R., Sheorey, S., White, S., Zhang, L., 2008. Searching the world’s Herbaria: A system for visual identification of plant species, in: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). pp. 116–129. https://doi.org/10.1007/978-3-540-88693-8-9. Kumar, N., Belhumeur, P.N., Biswas, A., Jacobs, D.W., Kress, W.J., Lopez, I.C., Soares, J.V.B., 2012. Leafsnap: A computer vision system for automatic plant species identification, in: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). pp. 502–516. https://doi.org/10.1007/978-3-642-33709-3_36. Niu, Shan, Yan, Chen, Gao (b0185) 2006; 2 Puttemans, S., Goedeme, T., 2015. Visual detection and species classification of orchid flowers, in: Proceedings of the 14th IAPR International Conference on Machine Vision Applications, MVA 2015. pp. 505–509. https://doi.org/10.1109/MVA.2015.7153241. Yanikoglu, Aptoula, Tirkaz (b0270) 2014; 25 Zhenjiang, Gandelin, Baozong (b0280) 2006; 19 Rodrigues, Soares, Costa, Van Baalen, Salvini, Silva, Caliari, Cardoso, Ribeiro, Delbem, Federson, Coelho, Laureano, Lima (b0220) 2016; 123 Nguyen, Hefenbrock, Oberg, Kastner, Baden (b0160) 2013; 73 Pandolfi, C., Messina, G., Mugnai, S., Azzarello, E., Masi, E., Dixon, K., Mancuso, S., 2009. Discrimination and identification of morphotypes of Banksia integrifolia (Proteaceae) by an Artificial Neural Network (ANN), based on morphological and fractal parameters of leaves and flowers. Tax. 58(3), 925-933. https://doi.org/10.1002/tax.583020. Sharma, B., Thota, R., Vydyanathan, N., Kale, A., 2009. Towards a robust, real-time face processing system using CUDA-enabled GPUs. 2009 Int. Conf. High Perform. Comput. 368–377. https://doi.org/10.1109/HIPC.2009.5433189. Morel, Galopin, Donès (b0140) 2009; 120 Rao, Garg, Ghosh (b0210) 2007; 8 Aquino, Millan, Gutiérrez, Tardáguila (b0015) 2015; 119 Kohsel, L., 2001. New unsupervised approach for solving classification problems with computer vision, in: Acta Horticulturae. p. 361–375. https://doi.org/10.17660/ActaHortic.2001.562.43. Yang, Prasher, Landry, Ramaswamy, Ditommaso (b0265) 2000; 42 Bhardwaj, Kaur (b0030) 2013; 4 Mathworks, 2017. Statistics and Machine Learning Toolbox TM User’s Guide R2017a. MatLab 1–9214. Cuevas, J., Chua, A., Sybingco, E., Bakar, E.A., 2017. Identification of river hydromorphological features using Viola-Jones Algorithm, in: IEEE Region 10 Annual International Conference, Proceedings/TENCON. pp. 2300–2306. https://doi.org/10.1109/TENCON.2016.7848439. Jenkins, Barrie, Buggy, Morison (b0085) 2016; 69 Teixeira da Silva, Dobránszki, Winarto, Zeng (b0245) 2015 Celikel, F.G., Karacali, I., 1995. Effect of preharvest factors on flower quality and longevity of cut carnations, in: Acta Hort. (ISHS) 405. pp. 156–163. https://doi.org/10.17660/ActaHortic.1995.405.19. Bao, Cai, Qi, Xun, Zhang, Yang (b0020) 2016; 127 Zhou, Kaneko, Tanaka, Kayamori, Shimizu (b0285) 2015; 116 Mg, Hanson, Joy, Francis (b0135) 2017; 7 Hong, Chen, Li, Chi, Zhang (b0075) 2004; 5 Handa, Agarwal (b0070) 2015; 123 Rikken, M., 2010. The European market for fair and sustainable flowers and plants. Trade for Development Centre, Belgian Development Agency, Belgium. Moriondo, Leolini, Staglianò, Argenti, Trombi, Brilli, Dibari, Leolini, Bindi (b0145) 2016; 209 Garbez, Chéné, Belin, Sigogne, Labatte, Hunault, Symoneaux, Rousseau, Galopin (b0055) 2016; 121 Dufour, Guérin (b0045) 2005; 105 Kyriacou, Bugmann, Lauria (b0110) 2005 Soleimani Pour, Chegini, Zarafshan, Massah (b0235) 2018; 139 Zhang, B., Huang, W., Li, J., Zhao, C., Fan, S., Wu, J., Liu, C., 2014. Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review. Food Res. Int. https://doi.org/10.1016/j.foodres.2014.03.012. El Kaddouhi, Saaidi, Abarkan (b0050) 2017; 76 Nilsback, Zisserman (b0170) 2010; 28 Agrawal, K.N., Singh, K., Bora, G.C., Lin, D., 2012. Weed Recognition Using Image-Processing Technique Based on Leaf Parameters. J. Agric. Sci. Technol. B J. Agric. Sci. Technol. 2, 1939–1250. Nikolaidis, Pitas (b0165) 2000; 33 Liu, Ehsani, Toudeshki, Zou, Wang (b0120) 2018; 99 Hsiao, Kang, Chang, Lin (b0080) 2015; 591 Schneiderman, H., Kanade, T., 2000. A statistical method for 3D object detection applied to faces and cars, in: CVPR. https://doi.org/10.1109/CVPR.2000.855895. Guyer, G.E, M., D.L., G., M.M., S., 1993. Application of machine vision to shape analysis in leaf and plant identification. Trans. ASAE. Zhou, Kaneko, Tanaka, Kayamori, Shimizu (b0290) 2014; 108 Timmermans, Hulzebosch (b0255) 1996; 15 Lobban, F., Jones, S., 2008. Implementing clinical guidelines (or not?), Psychology and Psychotherapy: Theory, Research and Practice. https://doi.org/10.1348/147608308X371778. Alionte, E., Lazar, C., 2015. A practical implementation of face detection by using Matlab cascade object detector, in: 2015 19th International Conference on System Theory, Control and Computing, ICSTCC 2015 - Joint Conference SINTES 19, SACCS 15, SIMSIS 19. pp. 785–790. https://doi.org/10.1109/ICSTCC.2015.7321390. Nilsback, M.E., Zisserman, A., 2008. Automated flower classification over a large number of classes, in: Proceedings - 6th Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP 2008. pp. 722–729. https://doi.org/10.1109/ICVGIP.2008.47. Timmermans, A.J.M., 1998. Computer vision system for on-line sorting of pot plants based on learning techniques, in: Acta Horticulturae. pp. 91–98. https://doi.org/10.17660/ActaHortic.1998.421.8. Lee, Hong (b0115) 2017; 61 Pujari, Yakkundimath, Byadgi (b0200) 2015 Rodrigues (10.1016/j.compag.2020.105460_b0220) 2016; 123 Liu (10.1016/j.compag.2020.105460_b0120) 2018; 99 Zhou (10.1016/j.compag.2020.105460_b0285) 2015; 116 Handa (10.1016/j.compag.2020.105460_b0070) 2015; 123 Johansson (10.1016/j.compag.2020.105460_b0090) 2015; 118 Lee (10.1016/j.compag.2020.105460_b0115) 2017; 61 Yang (10.1016/j.compag.2020.105460_b0265) 2000; 42 Garbez (10.1016/j.compag.2020.105460_b0055) 2016; 121 10.1016/j.compag.2020.105460_b0180 Nikolaidis (10.1016/j.compag.2020.105460_b0165) 2000; 33 Moriondo (10.1016/j.compag.2020.105460_b0145) 2016; 209 Zhou (10.1016/j.compag.2020.105460_b0290) 2014; 108 10.1016/j.compag.2020.105460_b0065 10.1016/j.compag.2020.105460_b0100 Dufour (10.1016/j.compag.2020.105460_b0045) 2005; 105 10.1016/j.compag.2020.105460_b0025 10.1016/j.compag.2020.105460_b0225 10.1016/j.compag.2020.105460_b0105 Jenkins (10.1016/j.compag.2020.105460_b0085) 2016; 69 Yanikoglu (10.1016/j.compag.2020.105460_b0270) 2014; 25 Teixeira da Silva (10.1016/j.compag.2020.105460_b0245) 2015 Pujari (10.1016/j.compag.2020.105460_b0200) 2015 Hong (10.1016/j.compag.2020.105460_b0075) 2004; 5 Mg (10.1016/j.compag.2020.105460_b0135) 2017; 7 Timmermans (10.1016/j.compag.2020.105460_b0255) 1996; 15 Nguyen (10.1016/j.compag.2020.105460_b0160) 2013; 73 10.1016/j.compag.2020.105460_b0250 10.1016/j.compag.2020.105460_b0130 10.1016/j.compag.2020.105460_b0010 10.1016/j.compag.2020.105460_b0175 Guru (10.1016/j.compag.2020.105460_b0060) 2010 10.1016/j.compag.2020.105460_b0215 Bhardwaj (10.1016/j.compag.2020.105460_b0030) 2013; 4 El Kaddouhi (10.1016/j.compag.2020.105460_b0050) 2017; 76 Viola (10.1016/j.compag.2020.105460_b0260) 2004; 57 10.1016/j.compag.2020.105460_b0040 Kyriacou (10.1016/j.compag.2020.105460_b0110) 2005 10.1016/j.compag.2020.105460_b0240 Rao (10.1016/j.compag.2020.105460_b0210) 2007; 8 Parsons (10.1016/j.compag.2020.105460_b0195) 2009; 104 10.1016/j.compag.2020.105460_b0125 10.1016/j.compag.2020.105460_b0005 Morel (10.1016/j.compag.2020.105460_b0140) 2009; 120 10.1016/j.compag.2020.105460_b0205 Juman (10.1016/j.compag.2020.105460_b0095) 2016; 128 Zhenjiang (10.1016/j.compag.2020.105460_b0280) 2006; 19 Murphy (10.1016/j.compag.2020.105460_b0150) 2017; 6 10.1016/j.compag.2020.105460_b0190 Soleimani Pour (10.1016/j.compag.2020.105460_b0235) 2018; 139 Bao (10.1016/j.compag.2020.105460_b0020) 2016; 127 Hsiao (10.1016/j.compag.2020.105460_b0080) 2015; 591 10.1016/j.compag.2020.105460_b0230 10.1016/j.compag.2020.105460_b0275 10.1016/j.compag.2020.105460_b0155 10.1016/j.compag.2020.105460_b0035 Nilsback (10.1016/j.compag.2020.105460_b0170) 2010; 28 Aquino (10.1016/j.compag.2020.105460_b0015) 2015; 119 Niu (10.1016/j.compag.2020.105460_b0185) 2006; 2 |
| References_xml | – volume: 57 start-page: 137 year: 2004 end-page: 154 ident: b0260 article-title: Robust real-time face detection publication-title: Int. J. Comput. Vis. – reference: Agrawal, K.N., Singh, K., Bora, G.C., Lin, D., 2012. Weed Recognition Using Image-Processing Technique Based on Leaf Parameters. J. Agric. Sci. Technol. B J. Agric. Sci. Technol. 2, 1939–1250. – reference: Nilsback, M.E., Zisserman, A., 2006. A visual vocabulary for flower classification, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. pp. 1447–1454. https://doi.org/10.1109/CVPR.2006.42. – volume: 105 start-page: 269 year: 2005 end-page: 282 ident: b0045 article-title: Nutrient solution effects on the development and yield of Anthurium andreanum Lind. in tropical soilless conditions publication-title: Sci. Hortic. (Amsterdam) – volume: 116 start-page: 65 year: 2015 end-page: 79 ident: b0285 article-title: Image-based field monitoring of Cercospora leaf spot in sugar beet by robust template matching and pattern recognition publication-title: Comput. Electron. Agric. – reference: Nair, D., Rajagopal, R., Wenzel, L., 2000. Pattern matching based on a generalized Fourier transform, in: Advanced Signal Processing Algorithms, Architectures, and Implementations X. pp. 472–481. – reference: Puttemans, S., Goedeme, T., 2015. Visual detection and species classification of orchid flowers, in: Proceedings of the 14th IAPR International Conference on Machine Vision Applications, MVA 2015. pp. 505–509. https://doi.org/10.1109/MVA.2015.7153241. – volume: 2 start-page: 1216 year: 2006 end-page: 1219 ident: b0185 article-title: 2D cascaded AdaBoost for eye localization publication-title: Proc. - Int. Conf. Pattern Recognit. – volume: 123 start-page: 410 year: 2016 end-page: 414 ident: b0220 article-title: A feasibility cachaca type recognition using computer vision and pattern recognition publication-title: Comput. Electron. Agric. – volume: 5 start-page: 764 year: 2004 end-page: 772 ident: b0075 article-title: A flower image retrieval method based on ROI feature publication-title: J. Zhejiang Univ. Sci. – reference: Kumar, N., Belhumeur, P.N., Biswas, A., Jacobs, D.W., Kress, W.J., Lopez, I.C., Soares, J.V.B., 2012. Leafsnap: A computer vision system for automatic plant species identification, in: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). pp. 502–516. https://doi.org/10.1007/978-3-642-33709-3_36. – reference: Celikel, F.G., Karacali, I., 1995. Effect of preharvest factors on flower quality and longevity of cut carnations, in: Acta Hort. (ISHS) 405. pp. 156–163. https://doi.org/10.17660/ActaHortic.1995.405.19. – start-page: 21 year: 2010 end-page: 29 ident: b0060 article-title: Texture Features and KNN in Classification of Flower Images publication-title: Int. J. Comput. Appl. – volume: 128 start-page: 172 year: 2016 end-page: 180 ident: b0095 article-title: A novel tree trunk detection method for oil-palm plantation navigation publication-title: Comput. Electron. Agric. – reference: Guyer, G.E, M., D.L., G., M.M., S., 1993. Application of machine vision to shape analysis in leaf and plant identification. Trans. ASAE. – reference: Nilsback, M.E., Zisserman, A., 2008. Automated flower classification over a large number of classes, in: Proceedings - 6th Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP 2008. pp. 722–729. https://doi.org/10.1109/ICVGIP.2008.47. – year: 2015 ident: b0245 article-title: Anthurium in vitro: a review publication-title: Sci. Hortic. (Amsterdam). – volume: 25 start-page: 1369 year: 2014 end-page: 1383 ident: b0270 article-title: Automatic plant identification from photographs publication-title: Mach. Vis. Appl. – volume: 33 start-page: 1783 year: 2000 end-page: 1791 ident: b0165 article-title: Facial feature extraction and pose determination publication-title: Pattern Recognit. – volume: 139 start-page: 67 year: 2018 end-page: 74 ident: b0235 article-title: Curvature-based pattern recognition for cultivar classification of Anthurium flowers publication-title: Postharvest Biol. Technol. – reference: Cuevas, J., Chua, A., Sybingco, E., Bakar, E.A., 2017. Identification of river hydromorphological features using Viola-Jones Algorithm, in: IEEE Region 10 Annual International Conference, Proceedings/TENCON. pp. 2300–2306. https://doi.org/10.1109/TENCON.2016.7848439. – volume: 591 start-page: 77 year: 2015 end-page: 91 ident: b0080 article-title: Learning-based leaf image recognition frameworks publication-title: Stud. Comput. Intell. – reference: Tadashi Higaki, J.S.L., 1995. Anthurium culture in Hawaii. https://doi.org/http://hdl.handle.net/10125/5482. – volume: 121 start-page: 331 year: 2016 end-page: 346 ident: b0055 article-title: Predicting sensorial attribute scores of ornamental plants assessed in 3D through rotation on video by image analysis: A study on the morphology of virtual rose bushes publication-title: Comput. Electron. Agric. – volume: 76 start-page: 23077 year: 2017 end-page: 23097 ident: b0050 article-title: Eye detection based on the Viola-Jones method and corners points publication-title: Multimed. Tools Appl. – volume: 19 start-page: 79 year: 2006 end-page: 101 ident: b0280 article-title: An OOPR-based rose variety recognition system publication-title: Eng. Appl. Artif. Intell. – reference: Sharma, B., Thota, R., Vydyanathan, N., Kale, A., 2009. Towards a robust, real-time face processing system using CUDA-enabled GPUs. 2009 Int. Conf. High Perform. Comput. 368–377. https://doi.org/10.1109/HIPC.2009.5433189. – volume: 99 start-page: 9 year: 2018 end-page: 16 ident: b0120 article-title: Detection of citrus fruit and tree trunks in natural environments using a multi-elliptical boundary model publication-title: Comput. Ind. – volume: 73 start-page: 677 year: 2013 end-page: 685 ident: b0160 article-title: Software-based dynamic-warp scheduling approach for load-balancing the Viola-Jones face detection algorithm on GPUs publication-title: J. Parallel Distrib. Comput. – reference: Belhumeur, P.N., Chen, D., Feiner, S., Jacobs, D.W., Kress, W.J., Ling, H., Lopez, I., Ramamoorthi, R., Sheorey, S., White, S., Zhang, L., 2008. Searching the world’s Herbaria: A system for visual identification of plant species, in: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). pp. 116–129. https://doi.org/10.1007/978-3-540-88693-8-9. – volume: 104 start-page: 161 year: 2009 end-page: 168 ident: b0195 article-title: Image analysis and statistical modelling for measurement and quality assessment of ornamental horticulture crops in glasshouses publication-title: Biosyst. Eng. – volume: 209 start-page: 1 year: 2016 end-page: 13 ident: b0145 article-title: Use of digital images to disclose canopy architecture in olive tree publication-title: Sci. Hortic. (Amsterdam) – start-page: 1802 year: 2015 end-page: 1808 ident: b0200 article-title: Image processing based detection of fungal diseases in plants publication-title: Procedia Comput. Sci. – volume: 69 start-page: 82 year: 2016 end-page: 87 ident: b0085 article-title: Extended fast compressive tracking with weighted multi-frame template matching for fast motion tracking publication-title: Pattern Recognit. Lett. – volume: 108 start-page: 58 year: 2014 end-page: 70 ident: b0290 article-title: Disease detection of Cercospora Leaf Spot in sugar beet by robust template matching publication-title: Comput. Electron. Agric. – volume: 127 start-page: 754 year: 2016 end-page: 762 ident: b0020 article-title: Multi-template matching algorithm for cucumber recognition in natural environment publication-title: Comput. Electron. Agric. – volume: 4 start-page: 86 year: 2013 end-page: 91 ident: b0030 article-title: A review on plant recognition and classification publication-title: Int. J. Eng. Trends Technol. – volume: 42 start-page: 147 year: 2000 end-page: 152 ident: b0265 article-title: Application of artificial neural networks in image recognition and classification of crop and weeds publication-title: Can. Agric. Eng. – volume: 7 start-page: 5324 year: 2017 end-page: 5328 ident: b0135 article-title: Plant leaf disease detection using deep learning and convolutional neural network publication-title: Int. J. Eng. Sci. Comput. – volume: 120 start-page: 391 year: 2009 end-page: 398 ident: b0140 article-title: Using architectural analysis to compare the shape of two hybrid tea rose genotypes publication-title: Sci. Hortic. (Amsterdam) – reference: Mathworks, 2017. Statistics and Machine Learning Toolbox TM User’s Guide R2017a. MatLab 1–9214. – reference: Timmermans, A.J.M., 1998. Computer vision system for on-line sorting of pot plants based on learning techniques, in: Acta Horticulturae. pp. 91–98. https://doi.org/10.17660/ActaHortic.1998.421.8. – volume: 123 start-page: 20 year: 2015 end-page: 25 ident: b0070 article-title: A review and a comparative study of various plant recognition and classification techniques using leaf images publication-title: Int. J. Comput. Appl. – reference: Schneiderman, H., Kanade, T., 2000. A statistical method for 3D object detection applied to faces and cars, in: CVPR. https://doi.org/10.1109/CVPR.2000.855895. – volume: 119 start-page: 92 year: 2015 end-page: 104 ident: b0015 article-title: Grapevine flower estimation by applying artificial vision techniques on images with uncontrolled scene and multi-model analysis publication-title: Comput. Electron. Agric. – volume: 118 start-page: 85 year: 2015 end-page: 91 ident: b0090 article-title: Fast visual recognition of Scots pine boards using template matching publication-title: Comput. Electron. Agric. – volume: 8 start-page: 173 year: 2007 end-page: 185 ident: b0210 article-title: Development of an agricultural crops spectral library and classification of crops at cultivar level using hyperspectral data publication-title: Precis. Agric. – reference: Kohsel, L., 2001. New unsupervised approach for solving classification problems with computer vision, in: Acta Horticulturae. p. 361–375. https://doi.org/10.17660/ActaHortic.2001.562.43. – reference: Zhang, B., Huang, W., Li, J., Zhao, C., Fan, S., Wu, J., Liu, C., 2014. Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review. Food Res. Int. https://doi.org/10.1016/j.foodres.2014.03.012. – volume: 61 start-page: 98 year: 2017 end-page: 114 ident: b0115 article-title: Automatic recognition of flower species in the natural environment publication-title: Image Vis. Comput. – start-page: 69 year: 2005 end-page: 80 ident: b0110 article-title: Vision-based urban navigation procedures for verbally instructed robots publication-title: Robotics and Autonomous Systems. – reference: Alionte, E., Lazar, C., 2015. A practical implementation of face detection by using Matlab cascade object detector, in: 2015 19th International Conference on System Theory, Control and Computing, ICSTCC 2015 - Joint Conference SINTES 19, SACCS 15, SIMSIS 19. pp. 785–790. https://doi.org/10.1109/ICSTCC.2015.7321390. – reference: Pandolfi, C., Messina, G., Mugnai, S., Azzarello, E., Masi, E., Dixon, K., Mancuso, S., 2009. Discrimination and identification of morphotypes of Banksia integrifolia (Proteaceae) by an Artificial Neural Network (ANN), based on morphological and fractal parameters of leaves and flowers. Tax. 58(3), 925-933. https://doi.org/10.1002/tax.583020. – volume: 28 start-page: 1049 year: 2010 end-page: 1062 ident: b0170 article-title: Delving deeper into the whorl of flower segmentation publication-title: Image Vis. Comput. – reference: Lobban, F., Jones, S., 2008. Implementing clinical guidelines (or not?), Psychology and Psychotherapy: Theory, Research and Practice. https://doi.org/10.1348/147608308X371778. – reference: Rikken, M., 2010. The European market for fair and sustainable flowers and plants. Trade for Development Centre, Belgian Development Agency, Belgium. – volume: 15 start-page: 41 year: 1996 end-page: 55 ident: b0255 article-title: Computer vision system for on-line sorting of pot plants using an artificial neural network classifier publication-title: Comput. Electron. Agric. – volume: 6 start-page: 200 year: 2017 end-page: 210 ident: b0150 article-title: Face detection with a Viola-Jones based hybrid network publication-title: IET Biometrics – ident: 10.1016/j.compag.2020.105460_b0155 doi: 10.1117/12.406527 – volume: 139 start-page: 67 year: 2018 ident: 10.1016/j.compag.2020.105460_b0235 article-title: Curvature-based pattern recognition for cultivar classification of Anthurium flowers publication-title: Postharvest Biol. Technol. doi: 10.1016/j.postharvbio.2018.01.013 – ident: 10.1016/j.compag.2020.105460_b0010 doi: 10.1109/ICSTCC.2015.7321390 – ident: 10.1016/j.compag.2020.105460_b0190 doi: 10.1002/tax.583020 – volume: 5 start-page: 764 year: 2004 ident: 10.1016/j.compag.2020.105460_b0075 article-title: A flower image retrieval method based on ROI feature publication-title: J. Zhejiang Univ. Sci. doi: 10.1631/jzus.2004.0764 – ident: 10.1016/j.compag.2020.105460_b0035 doi: 10.17660/ActaHortic.1995.405.19 – ident: 10.1016/j.compag.2020.105460_b0130 – volume: 591 start-page: 77 year: 2015 ident: 10.1016/j.compag.2020.105460_b0080 article-title: Learning-based leaf image recognition frameworks publication-title: Stud. Comput. Intell. doi: 10.1007/978-3-319-14654-6_5 – year: 2015 ident: 10.1016/j.compag.2020.105460_b0245 article-title: Anthurium in vitro: a review publication-title: Sci. Hortic. (Amsterdam). doi: 10.1016/j.scienta.2014.11.024 – ident: 10.1016/j.compag.2020.105460_b0225 doi: 10.1109/CVPR.2000.855895 – volume: 4 start-page: 86 year: 2013 ident: 10.1016/j.compag.2020.105460_b0030 article-title: A review on plant recognition and classification publication-title: Int. J. Eng. Trends Technol. – volume: 108 start-page: 58 year: 2014 ident: 10.1016/j.compag.2020.105460_b0290 article-title: Disease detection of Cercospora Leaf Spot in sugar beet by robust template matching publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2014.07.004 – volume: 2 start-page: 1216 year: 2006 ident: 10.1016/j.compag.2020.105460_b0185 article-title: 2D cascaded AdaBoost for eye localization publication-title: Proc. - Int. Conf. Pattern Recognit. – ident: 10.1016/j.compag.2020.105460_b0040 doi: 10.1109/TENCON.2016.7848439 – volume: 69 start-page: 82 year: 2016 ident: 10.1016/j.compag.2020.105460_b0085 article-title: Extended fast compressive tracking with weighted multi-frame template matching for fast motion tracking publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2015.10.014 – start-page: 21 year: 2010 ident: 10.1016/j.compag.2020.105460_b0060 article-title: Texture Features and KNN in Classification of Flower Images publication-title: Int. J. Comput. Appl. – volume: 42 start-page: 147 year: 2000 ident: 10.1016/j.compag.2020.105460_b0265 article-title: Application of artificial neural networks in image recognition and classification of crop and weeds publication-title: Can. Agric. Eng. – ident: 10.1016/j.compag.2020.105460_b0180 doi: 10.1109/CVPR.2006.42 – volume: 209 start-page: 1 year: 2016 ident: 10.1016/j.compag.2020.105460_b0145 article-title: Use of digital images to disclose canopy architecture in olive tree publication-title: Sci. Hortic. (Amsterdam) doi: 10.1016/j.scienta.2016.05.021 – volume: 123 start-page: 410 year: 2016 ident: 10.1016/j.compag.2020.105460_b0220 article-title: A feasibility cachaca type recognition using computer vision and pattern recognition publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2016.03.020 – ident: 10.1016/j.compag.2020.105460_b0215 – volume: 120 start-page: 391 year: 2009 ident: 10.1016/j.compag.2020.105460_b0140 article-title: Using architectural analysis to compare the shape of two hybrid tea rose genotypes publication-title: Sci. Hortic. (Amsterdam) doi: 10.1016/j.scienta.2008.11.039 – ident: 10.1016/j.compag.2020.105460_b0205 doi: 10.1109/MVA.2015.7153241 – ident: 10.1016/j.compag.2020.105460_b0275 doi: 10.1016/j.foodres.2014.03.012 – volume: 127 start-page: 754 year: 2016 ident: 10.1016/j.compag.2020.105460_b0020 article-title: Multi-template matching algorithm for cucumber recognition in natural environment publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2016.08.001 – volume: 128 start-page: 172 year: 2016 ident: 10.1016/j.compag.2020.105460_b0095 article-title: A novel tree trunk detection method for oil-palm plantation navigation publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2016.09.002 – volume: 25 start-page: 1369 year: 2014 ident: 10.1016/j.compag.2020.105460_b0270 article-title: Automatic plant identification from photographs publication-title: Mach. Vis. Appl. doi: 10.1007/s00138-014-0612-7 – start-page: 69 year: 2005 ident: 10.1016/j.compag.2020.105460_b0110 article-title: Vision-based urban navigation procedures for verbally instructed robots publication-title: Robotics and Autonomous Systems. doi: 10.1016/j.robot.2004.08.011 – ident: 10.1016/j.compag.2020.105460_b0125 doi: 10.1348/147608308X371778 – volume: 61 start-page: 98 year: 2017 ident: 10.1016/j.compag.2020.105460_b0115 article-title: Automatic recognition of flower species in the natural environment publication-title: Image Vis. Comput. doi: 10.1016/j.imavis.2017.01.013 – volume: 7 start-page: 5324 year: 2017 ident: 10.1016/j.compag.2020.105460_b0135 article-title: Plant leaf disease detection using deep learning and convolutional neural network publication-title: Int. J. Eng. Sci. Comput. – volume: 116 start-page: 65 year: 2015 ident: 10.1016/j.compag.2020.105460_b0285 article-title: Image-based field monitoring of Cercospora leaf spot in sugar beet by robust template matching and pattern recognition publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2015.05.020 – start-page: 1802 year: 2015 ident: 10.1016/j.compag.2020.105460_b0200 article-title: Image processing based detection of fungal diseases in plants publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2015.02.137 – volume: 119 start-page: 92 year: 2015 ident: 10.1016/j.compag.2020.105460_b0015 article-title: Grapevine flower estimation by applying artificial vision techniques on images with uncontrolled scene and multi-model analysis publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2015.10.009 – volume: 33 start-page: 1783 year: 2000 ident: 10.1016/j.compag.2020.105460_b0165 article-title: Facial feature extraction and pose determination publication-title: Pattern Recognit. doi: 10.1016/S0031-3203(99)00176-4 – volume: 57 start-page: 137 year: 2004 ident: 10.1016/j.compag.2020.105460_b0260 article-title: Robust real-time face detection publication-title: Int. J. Comput. Vis. doi: 10.1023/B:VISI.0000013087.49260.fb – volume: 105 start-page: 269 year: 2005 ident: 10.1016/j.compag.2020.105460_b0045 article-title: Nutrient solution effects on the development and yield of Anthurium andreanum Lind. in tropical soilless conditions publication-title: Sci. Hortic. (Amsterdam) doi: 10.1016/j.scienta.2005.01.022 – ident: 10.1016/j.compag.2020.105460_b0025 doi: 10.1007/978-3-540-88693-8_9 – volume: 123 start-page: 20 year: 2015 ident: 10.1016/j.compag.2020.105460_b0070 article-title: A review and a comparative study of various plant recognition and classification techniques using leaf images publication-title: Int. J. Comput. Appl. – ident: 10.1016/j.compag.2020.105460_b0065 – volume: 28 start-page: 1049 year: 2010 ident: 10.1016/j.compag.2020.105460_b0170 article-title: Delving deeper into the whorl of flower segmentation publication-title: Image Vis. Comput. doi: 10.1016/j.imavis.2009.10.001 – volume: 104 start-page: 161 year: 2009 ident: 10.1016/j.compag.2020.105460_b0195 article-title: Image analysis and statistical modelling for measurement and quality assessment of ornamental horticulture crops in glasshouses publication-title: Biosyst. Eng. doi: 10.1016/j.biosystemseng.2009.06.015 – volume: 73 start-page: 677 year: 2013 ident: 10.1016/j.compag.2020.105460_b0160 article-title: Software-based dynamic-warp scheduling approach for load-balancing the Viola-Jones face detection algorithm on GPUs publication-title: J. Parallel Distrib. Comput. doi: 10.1016/j.jpdc.2013.01.012 – volume: 15 start-page: 41 year: 1996 ident: 10.1016/j.compag.2020.105460_b0255 article-title: Computer vision system for on-line sorting of pot plants using an artificial neural network classifier publication-title: Comput. Electron. Agric. doi: 10.1016/0168-1699(95)00056-9 – volume: 121 start-page: 331 year: 2016 ident: 10.1016/j.compag.2020.105460_b0055 article-title: Predicting sensorial attribute scores of ornamental plants assessed in 3D through rotation on video by image analysis: A study on the morphology of virtual rose bushes publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2016.01.001 – volume: 99 start-page: 9 year: 2018 ident: 10.1016/j.compag.2020.105460_b0120 article-title: Detection of citrus fruit and tree trunks in natural environments using a multi-elliptical boundary model publication-title: Comput. Ind. doi: 10.1016/j.compind.2018.03.007 – volume: 76 start-page: 23077 year: 2017 ident: 10.1016/j.compag.2020.105460_b0050 article-title: Eye detection based on the Viola-Jones method and corners points publication-title: Multimed. Tools Appl. doi: 10.1007/s11042-017-4415-5 – volume: 118 start-page: 85 year: 2015 ident: 10.1016/j.compag.2020.105460_b0090 article-title: Fast visual recognition of Scots pine boards using template matching publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2015.08.026 – volume: 6 start-page: 200 year: 2017 ident: 10.1016/j.compag.2020.105460_b0150 article-title: Face detection with a Viola-Jones based hybrid network publication-title: IET Biometrics doi: 10.1049/iet-bmt.2016.0037 – ident: 10.1016/j.compag.2020.105460_b0175 doi: 10.1109/ICVGIP.2008.47 – ident: 10.1016/j.compag.2020.105460_b0105 doi: 10.1007/978-3-642-33709-3_36 – volume: 8 start-page: 173 year: 2007 ident: 10.1016/j.compag.2020.105460_b0210 article-title: Development of an agricultural crops spectral library and classification of crops at cultivar level using hyperspectral data publication-title: Precis. Agric. doi: 10.1007/s11119-007-9037-x – ident: 10.1016/j.compag.2020.105460_b0240 – ident: 10.1016/j.compag.2020.105460_b0100 doi: 10.17660/ActaHortic.2001.562.43 – ident: 10.1016/j.compag.2020.105460_b0250 doi: 10.17660/ActaHortic.1998.421.8 – volume: 19 start-page: 79 year: 2006 ident: 10.1016/j.compag.2020.105460_b0280 article-title: An OOPR-based rose variety recognition system publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2005.05.009 – ident: 10.1016/j.compag.2020.105460_b0005 – ident: 10.1016/j.compag.2020.105460_b0230 doi: 10.1109/HIPC.2009.5433189 |
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| Snippet | •Flowers cultivar identification is a key step for subsequent classification tasks.•Anthurium flowers could be identified based on their spadix.•The... A hybrid approach was developed for highly accurate and effective identification of Anthurium flower cultivars in a computer vision-based sorting machine.... |
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| SubjectTerms | Algorithms Anthurium Classification color Computation time Computer vision Cultivars data collection Flowers Grading machines Identification accuracy Image classification Object recognition spadix Template matching Viola-Jones |
| Title | A vision-based hybrid approach for identification of Anthurium flower cultivars |
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