Abstract
Crop disease leaf image segmentation is an important and challenging step, because the disease leaf image and corresponding lesions are often complex, various, and variant, and the segmenting results directly impact on subsequent disease recognition rate. A segmentation algorithm is proposed by modified fully convolutional networks (FCNs) to deal with the problem of segmenting spots from crop leaf disease image with complicated background. The experimental result shows that the proposed method can be used in complicated field environment with high extracting accuracy.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Martinelli, F., Scalenghe, R., Davino, S., et al.: Advanced methods of plant disease detection. A review. Agron. Sustain. Dev. 35(1), 1–25 (2015)
Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 1 (2016). https://doi.org/10.1109/tpami.2016.2572683
Du, M.G., Zhang, S.W.: Crop disease leaf image segmentation based on genetic algorithm and maximum entropy. Appl. Mech. Mater. 713–715 (2015)
Wang, L., Tao, Y., Tian, Y.: Crop disease leaf image segmentation method based on color features. In: International Conference on Computer & Computing Technologies in Agriculture, pp. 713–717 (2007)
Guan, H.O., Shao-Hua, X.U., Feng, T.: Fuzzy neural network based on T-S model and its application on image segmentation of plant diseases. J. China Agric. Univ. 16(3), 145–149 (2011)
Zhang, S., You, Z., Wu, X.: Plant disease leaf image segmentation based on superpixel clustering and EM algorithm. Neural Comput. Appl. 5, 1–8 (2017)
Camargo, A., Smith, J.S.: An image-processing based algorithm to automatically identify plant disease visual symptoms. Int. J. Food Eng. 102(1), 9–21 (2013)
Rodriguez, A.V.C., Kim, J.T.: Plant disease symptoms, identification from colored images. In: Gli, J., Horabik, J., Lipiec, J. (eds.) Encyclopedia of Agrophysics. Springer, Dordrecht (2011). https://doi.org/10.1007/978-90-481-3585-1_216
Mutka, A.M., Bart, R.S.: Image-based phenotyping of plant disease symptoms. Front. Plant Sci. 5 (2015). https://doi.org/10.3389/fpls.2014.00734
Baum, T., Navarro-Quezada, A., Knogge, W., et al.: HyphArea—automated analysis of spatiotemporal fungal patterns. J. Plant Physiol. 168(1), 72–78 (2011)
Dong, P., Wang, X.: Recognition of greenhouse cucumber disease based on image processing technology. Open J. Appl. Sci. 3(1), 27–31 (2013)
Revathi, P., Hemalatha, M.: Advance computing enrichment evaluation of cotton leaf spot disease detection using Image Edge detection. In: Third International Conference on Computing Communication & Networking Technologies. IEEE (2012)
Casady, W.W., Singh, N., Costello, T.A.: Machine vision for measurement of rice canopy dimensions. Trans. ASAE 39(5), 1891–1898 (1996)
Guanlin, L., Zhanhong, M., Chong, H., et al.: Segmentation of color images of grape diseases using K_means clustering algorithm. Trans. Chin. Soc. Agric. Eng. 26(14), 32–37 (2010)
Jaware, T.H., Badgujar, R.D., Patil, P.G.: Crop disease detection using image segmentation. World J. Sci. Technol. 2(4), 190–194 (2012)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (2007)
Changji, W., Helong, Yu.: An image segmentation method for maize disease based on IGA-PCNN. In: Guo, C., Hou, Z.-G., Zeng, Z. (eds.) ISNN 2013. LNCS, vol. 7951, pp. 602–611. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39065-4_72
Amara, J., Bouaziz, B., Algergawy, A.: A deep learning-based approach for banana leaf diseases classification. In: Mitschang, B., et al. (eds.) BTW 2017 - Workshopband. LNI, pp. 79–88. Springer, Bonn (2017)
Hanson, A.M.G.J., Joel, M.G., Joy, A., Francis, J.: Plant leaf disease detection using deep learning and convolutional neural network. Int. J. Eng. Sci. Comput. 7, 5324–5328 (2017)
Liu, B., Zhang, Y., He, D.J., et al.: Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry 10(11), 1–16 (2018). https://doi.org/10.3390/sym10010011
Yu, S., Cheng, Y., Xie, L., et al.: Fully convolutional networks for action recognition. IET Comput. Vis. 11(8), 744–749 (2017)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2014)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, pp. 1–9 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, Xf., Wang, Z., Zhang, Sw. (2019). Segmenting Crop Disease Leaf Image by Modified Fully-Convolutional Networks. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11643. Springer, Cham. https://doi.org/10.1007/978-3-030-26763-6_62
Download citation
DOI: https://doi.org/10.1007/978-3-030-26763-6_62
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26762-9
Online ISBN: 978-3-030-26763-6
eBook Packages: Computer ScienceComputer Science (R0)