Abstract
Machine learning enriches the field of artificial intelligence that aims to make computers powerful by providing them information extracted from data. Flowers identification is highly significant and relevant for Plant Scientists. Carrying it out manually is not only a tedious task but also prone to errors due to a large number of flower types. Using machine learning algorithms to identify flowers is appealing. To this aim, two observations on flower leaves are relevant and leverage flower identification: one, flower plants have key knowledge in their leaves, thus enable distinctiveness; two, leaves have a much longer life on plants than flowers and fruits. In this paper, we have proposed a machine learning approach based on k Nearest Neighbor (k-NN) to identify rose types. Following steps are carried out during the identification process. First, rose plant images are taken using 23MP camera, ensuring temperature uniformity during the experiment. Second, texture and histogram features are extracted from the captured images. Third, k-NN algorithm is applied to these features with k taking values between 1 and 10. Our research brings to limelight the usefulness of selected features for rose type identification with histogram and texture features achieving maximum accuracies of 65% and 45.50% respectively.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wayne’s Word Gee-Whiz Trivia: Diversity of Flowering Plants. https://www2.palomar.edu/users/warmstrong/trmar98.htm. Accessed 16 Aug 2018
Rose. https://www.britannica.com/plant/rose-plant. Accessed 12 Aug 2018
The flower expert. https://www.theflowerexpert.com/content/mostpopularflowers/rose. Accessed 15 Aug 2018
Wäldchen, J., et al.: Automated plant species identification–Trends and future directions. PLoS Comput. Biol. 14(4), e1005993 (2018)
Zhang, Y., et al.: Fruit classification using computer vision and feed forward neural network. J. Food Eng. 143, 167–177 (2014)
El-Bendary, N., et al.: Using machine learning techniques for evaluating tomato ripeness. Expert Syst. Appl. 42(4), 1892–1905 (2015)
Singh, C., Kaur, K.P.: A fast and efficient image retrieval system based on color and texture features. J. Vis. Commun. Image Represent. 41, 225–238 (2016)
Pinto, L.S., et al.: Crop disease classification using texture analysis. In: IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (2016)
Pang, C., et al.: Rediscover flowers structurally. Multimedia Tools Appl. 77(7), 7851–7863 (2018)
Wäldchen, J., Mäder, P.: Plant species identification using computer vision techniques: a systematic literature review. Arch. Comput. Methods Eng. 25(2), 507–543 (2018)
Krishnaveni, S., Pethalakshmi, A.: Toward automatic quality detection of Jasmenum flower. ICT Express 3(3), 148–153 (2017)
Lee, S.H., et al.: How deep learning extracts and learns leaf features for plant classification. Pattern Recogn. 71, 1–13 (2017)
Barré, P., et al.: LeafNet: a computer vision system for automatic plant species identification. Ecol. Inform. 40, 50–56 (2017)
Cheng, K., Tan, X.: Sparse representations based attribute learning for flower classification. Neurocomputing 145, 416–426 (2014)
Anxiang, H., et al.: Region-of-interest based flower images retrieval. In: Proceeding of 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2003) (2003)
Miao, Z., et al.: A new image shape analysis approach and its application to flower shape analysis. Image Vis. Comput. 24(10), 1115–1122 (2006)
Siraj, F., et al.: Digital image classification for Malaysian blooming flower. In: 2010 Second International Conference on Computational Intelligence, Modelling and Simulation (2010)
Ashish, D., et al.: Land-use classification of multispectral aerial images using artificial neural networks. Int. J. Remote Sens. 30(8), 1989–2004 (2009)
Bharathi, S., et al.: Automatic land use/land cover classification using texture and data mining classifier. In: 2013 IEEE International Conference of IEEE Region 10 (TENCON 2013) (2013)
Wu, S.G., et al.: A leaf recognition algorithm for plant classification using probabilistic neural network. In: 2007 IEEE International Symposium on Signal Processing and Information Technology (2007)
Batista, G.E.A.P.A., et al.: Classification of live moths combining texture, color and shape primitives. In: 2010 Ninth International Conference on Machine Learning and Applications (2010)
CVIP tool. https://cviptools.ece.siue.edu. Accessed 16 Aug 2018
Umbaugh, S.E.: Computer Imaging: Digital Image Analysis and Processing, 1st edn, pp. 292–295. CRC Press, Boca Raton (2005)
Boland, M.V.: Haralick texture features. http://murphylab.web.cmu.edu/publications/boland/boland_node26.html#eqn:cho_g. Accessed 7 Sept 2018
Mohanaiah, P., et al.: Image texture feature extraction using GLCM approach. Int. J. Sci. Res. Publ. 3, 1–5 (2013)
Mohamad, F.S., et al.: Nearest neighbor for histogram-based feature extraction. Procedia Comput. Sci. 4, 1296–1305 (2011)
Ramli, S., et al.: Histogram of intensity feature extraction for automatic plastic bottle recycling system using machine vision. Am. J. Environ. Sci. 4(6), 583–588 (2008)
Lakhvir Kaur, L., Laxmi, V.: A review on plant leaf classification and segmentation. Int. J. Eng. Comput. Sci. 5(8), 2319–7242 (2016)
Blachnik, M., Laaksonen, J.: Image classification by histogram features created with learning vector quantization. In: Kůrková, V., Neruda, R., KoutnÃk, J. (eds.) ICANN 2008. LNCS, vol. 5163, pp. 827–836. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-87536-9_85
Gou, J., Luo, M., Xiong, T.: Improving K-nearest neighbor rule with dual weighted voting for pattern classification. In: Yu, Y., Yu, Z., Zhao, J. (eds.) CSEEE 2011. CCIS, vol. 159, pp. 118–123. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22691-5_21
Mittal, K., et al.: Performance study of K-nearest neighbor classifier and K-means clustering for predicting the diagnostic accuracy. Int. J. Inf. Technol. 1–6 (2018). https://link.springer.com/journal/41870/onlineFirst/page/3
Rahmani, M., et al.: Supervised machine learning for plants identification based on images of their leaves. Int. J. Agric. Environ. Inf. Syst. (IJAEIS) 7(4), 17–31 (2016)
GarcÃa-Pedrajas, N., et al.: A proposal for local k values for k-nearest neighbor rule. IEEE Trans. Neural Netw. Learn. Syst. 28(2), 470–475 (2017)
Sun, S., Huang, R.: An adaptive k-nearest neighbor algorithm. In: 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Malik, M., Ikram, A., Batool, S.N., Aslam, W. (2019). A Performance Assessment of Rose Plant Classification Using Machine Learning. In: Bajwa, I., Kamareddine, F., Costa, A. (eds) Intelligent Technologies and Applications. INTAP 2018. Communications in Computer and Information Science, vol 932. Springer, Singapore. https://doi.org/10.1007/978-981-13-6052-7_64
Download citation
DOI: https://doi.org/10.1007/978-981-13-6052-7_64
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6051-0
Online ISBN: 978-981-13-6052-7
eBook Packages: Computer ScienceComputer Science (R0)