Abstract
Digital India program will help in agriculture field in various ways, including a weather forecast to agriculture consultation. To find all the causing symptoms of diseased leaf, the knowledge-based Android app is proposed to refer the disease of a leaf. The user can directly capture the disease leaf image from their smartphone and upload that image into the app, and they will get all the causes and symptoms of a particular disease. Moreover, users can get information in the form of text and audio in their proffered language. This system will accept the query based on images and text format which is very useful to the farmers. In this proposed work, texture-based feature extraction using Shape Adaptive Discreet Curvelet Transform (SADCT) is developed using big data computing framework.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Rajkumar, K., Sudheer, D.: A review of visual information retrieval on massive image data using hadoop. Int. J. Control Theor. Appl. 9, 425–430 (2016)
Bravo, C., Moshou, D., West, J., McCartney, A., Ramon, H.: Early disease detection in wheat fields using spectral reflectance. Biosyst. Eng. 84(2), 137–145 (2003)
Carson, C., Belongie, S., Greenspan, H., Malik, J.: Recognition of images in large databases using color and texture. IEEE Trans. Pattern Anal. Mach. Intell. 24(8), 1026–1028 (2002)
Miller, S.A., Beed, F.D., Harmon, C.L.: Plant disease diagnostic capabilities and networks. Annu. Rev. Phytopathol. 47, 15–38 (2009)
Klatt, B., Kleinhenz, B., Kuhn, C., Bauckhage, C., Neumann, M., Kersting, K., Oerke, E.C., Hallau, L., Mahlein, A.K., Steiner-Stenzel, U., Röhrig, M.: SmartDDS-Plant disease setection via smartphone. EFITA-WCCA-CIGR Conference “Sustainable Agriculture through ICT Innovation”, Turin, Italy, 24–27 June (2013)
Prince, G., Clarkson, J.P., Rajpoot, N.M.: Automatic detection of diseased tomato plants using thermal and stereo visible light images. PloS One, 10(4), e0123262 (2015)
Majumdar, J., Naraseeyappa, S., Ankalaki, S.: Analysis of agriculture data using data mining techniques: application of big data. J. Big Data, Springer (2017)
Rouached, H., Rhee, S.Y.: System-level understanding of plant mineral nutrition in the big data era. Curr. Opin. Syst. Biol. 4, 71–77 (2017)
Xie, H., He, Y., Xie, X.: Exploring the factors influencing ecological land change for China’s Beijinge-Tianjine-Hebei Region using big data. J. Cleaner Prod. 142, 677e687 (2017)
Kamilaris, A., Kartakoullis, A., Prenafeta-Boldú, F.X.: A review on the practice of big data analysis in agriculture. Comput. Electron. Agric. 143, 23–37 (2017)
Manjunath, B.S.: Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Anal. Mach. Intell. 18(8), 837–842 (1996)
Iakovidis, D.K., Pelekis, N., Kotsifakos, E.E., Kopanakis, I., Karanikas, H., Theodoridis, Y.: A pattern similarity scheme for medical image retrieval. IEEE Trans. Inf. Technol. Biomed. 13(4), 442–450 (2009)
Rajakumar, K., Revathi, S.: An efficient face recognition system using curvelet with PCA. ARPN J. Eng. Appl. Sci. 10, 4915–4920 (2015)
Rajakumar, K., Muttan, S.: Texture-based MRI image retrieval using curvelet with statistical similarity matching. Int. J. Comput. Sci. Issues 10, 483–487 (2013)
Manipoonchelvi, P., Muneeswaran, K.: Significant region-based image retrieval using curvelet transform. In: IEEE Conference Publications, pp. 291–294 (2011)
Quellec, G., Lamard, M., Cazuguel, G., Cochener, B., Roux, C.: Fast wavelet-based image characterization for highly adaptive image retrieval. IEEE Trans. Image Process. 21(4), 1613–1623 (2012)
Rajakumar, K., Muttan, S.: MRI image retrieval using wavelet with mahalanobis distance measurement. J. Electr. Eng. Technol. 8, 1188–1193 (2013)
Zhang, L., Wang, L., Lin, W.: Generalized biased discriminant analysis for content-based image retrieval systems. IEEE Trans. Man Cybern. Part B Cybern. 42(1), 282–290 (2012)
Zajić, G., Kojić, N., Reljin, B.: Searching image database based on content. In: IEEE Conference Publications, pp. 1203–1206 (2011)
Akakin, H.Ç., Gürcan, M.N.: Content-based microscopic image retrieval system for multi-image queries. IEEE Trans. Inf. Technol. Biomed. 16(4), 758–769 (2012)
Li, Y., Gong, H., Feng, D., Zhang, Y.: An adaptive method of speckle reduction and feature enhancement for SAR images based on curvelet transform and particle swarm optimization. IEEE Trans. Geosci. Remote Sens. 49(8), 3105–3116 (2011)
Liu, S., Cai, W., Wen, L., Eberl, S., Fulham, M.J., Feng, D.: Localized functional neuroimaging retrieval using 3D discrete curvelet transform. In: IEEE Conference Publications, pp. 1877–1880 (2011)
Minakshi, Banerjee, Sanghamitra, Yopadhyay, Sankar, K.P.: Rough Sets and Intelligent Systems, vol. 2, Springer link, pp. 391–395
Prasad, B.G., Krishna, A.N.: Statistical texture feature-based retrieval and performance evaluation of CT brain images. In: IEEE Conference Publications, pp. 289–293 (2011)
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
Santhana Krishnan, J., SivaKumar, P. (2019). Big Data-Based Image Retrieval Model Using Shape Adaptive Discreet Curvelet Transformation. In: Peter, J., Alavi, A., Javadi, B. (eds) Advances in Big Data and Cloud Computing. Advances in Intelligent Systems and Computing, vol 750. Springer, Singapore. https://doi.org/10.1007/978-981-13-1882-5_20
Download citation
DOI: https://doi.org/10.1007/978-981-13-1882-5_20
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1881-8
Online ISBN: 978-981-13-1882-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)