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Academic Journal of Computing & Information Science, 2018, 1(1); doi: 10.25236/AJCIS.010002.

Segmentation of Corn Leaf Disease Based on Fully Convolution Neural Network


Zhen Wang, Shanwen Zhang

Corresponding Author:
Zhen Wang

School of Information and Engineering, Xijing University, Xi’an, 710123, China


Aiming at the problem that traditional computer vision is susceptible to different illumination and complex background in the segmentation of corn leaf disease, a method of segmentation of corn leaf disease based on full convolution neural network is proposed. Firstly, pre-processing and data enhancement are performed on the acquired images to establish a training set and a test set for deep learning. Secondly, the training centralized image is input into the FCN, and the feature map is obtained through multiple convolution, pooling, and activation layers; The feature map is upsampled to obtain a feature map of the same size as the input image. Finally, the resolution of the segmented image is restored by the process of deconvolution, and the segmentation result is output. This method was used to segment the common corn leaf disease images. The results show that the method has better segmentation effect and the segmentation accuracy is over 96%.


Corn leaf disease image, Computer vision, Convolutional neural network, Image segmentation

Cite This Paper

Zhen Wang, Shanwen Zhang. Segmentation of Corn Leaf Disease Based on Fully Convolution Neural Network. Academic Journal of Computing & Information Science (2018) Vol. 1: 9-18.


[1] Li, H.Y., Chen, Z.Y., Zhou, J.C., Zhang, J.X.: Occurrence and Damage of the Main Diseases of Cucumber in Greenhouse During Growth Period and Shelf Life (in Chinese). Modern Agricultural Sciences and Technology, 20, 187--189, 191 (2010)
[2] Sasaki, Y., Okamoto, T., Imou, K., Torii, T.: Automatic Diagnosis of Plant Disease-recognition Between Healthy and Diseased Leaf. Journal of the Japanese Society of Agricultural Machinery. 61, 119--126 (1999)
[3] Zhao, Y.X., Wang K.R., Bai, Z.Y., Li, S.K., Xie, R.Z., Gao, S.J.: Research of Maize Leaf Disease Identifying System Based Image Recognition (in Chinese). Scientia Agricultura Sinica. 40, 698--703 (2007)
[4] Sanyala, P., Patel, S.C.: Pattern Recognition Method to Detect Two Diseases in Rice Plants. Imaging Science Journal. 56, 319--325 (2008)
[5] Cui, Y.L., Cheng, P.F., Dong, X.Z., Liu, Z.H., Wang, S.X.: Image Processing and Extracting Color Features of Greenhouse Diseased Leaf (in Chinese). Transactions of the CSAE. 21(supp.), 32--35 (2005)
[6] Tian, Y.W., Zhang, C.H., Li C.H.: Study on Plant Disease Recognition Using Support Vector Machine and Chromaticity Moments (in Chinese). Transactions of the Chinese Society of Agricultural Machinery. 35, 95--98 (2004)
[7] Tian, Y.W., Niu, Y.: Applied Research of Support Vector Machine on Recognition of Cucumber Disease (in Chinese). Journal of Agricultural Mechanization Research. 31, 36--39 (2009)
[8] Cen, Z.X., Li, B.J., Shi, Y.X., Huang, H.Y., Liu, J., Liao, N.F., Feng, J.: Discrimination of Cucumber Anthracnose and Cucumber Brown Speck Based on Color Image Statistical Characteristics (in Chinese). Acta Horticulturae Sinica. 34, 1425--1430 (2007)
[9] Pydipati, R., Burks, T.F., Lee, W.S.: Identification of Citrus Disease Using Color Texture Features and Discriminant Analysis. Computers and Electronics in Agriculture. 52, 49--59 (2006)
[10] Pydipati, R., Burks, T.F., Lee, W.S.: Statistical and Neural Network Classifiers for Citrus Disease Detection Using Machine Vision. Transactions of the ASAE. 48, 2007--2014 (2005)
[11] Camargo, A., Smith, J.S.: Image Pattern Classification for the Identification of Disease Causing Agents in Plants. Computers and Electronics in Agriculture. 66, 121--125 (2009)
[12] Wang, N., Wang, K.R., Xie, R.Z., Lai, J.C., Ming, B., Li, S.K.: Maize Leaf Disease Identification Based on Fisher Discrimination Analysis. Scientia Agricultura Sinica. 42, 3836--3842 (2009)
[13] Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer-Verlag, New York (1995)
[14] Song, K., Sun, X.Y., Ji J.W.: Corn Leaf Disease Recognition Based on Support Vector Machine Method (in Chinese). Transactions of the CSAE. 23, 155--157 (2007)
[15] Ren, D., Yu, H.Y., Wang, J.H.: Research on Plant Disease Recognition Based on Linear Combination of the Kernel Function Support Vector Machine (in Chinese). Journal of Agricultural Mechanization Research. 29, 41--43 (2007)
[16] Zhang, J., Wang, S.X.: A Study on the Segmentation Method in Image Processing for Plant Disease of Greenhouse (in Chinese). Journal of Inner Mongolia Agricultural University. 28, 19--22 (2007)
[17] Mao, H.P., Zhang, Y.C., Hu, B.: Segmentation of Crop Disease Leaf Images Using Fuzzy C-means Clustering Algorithm (in Chinese). Transactions of the CSAE. 24, 136--140 (2008)
[18] Tian, Y.W., Li C.H.: Color Image Segmentation Method Based on Statistical Pattern Recognition for Plant Disease Diagnose (in Chinese). Journal of Jilin University (Engineering and Technology Edition). 34, 291--293 (2003)
[19] Selim, S.Z., Ismail, M.A. K-means-type Algorithm: a Generalized Convergence Theorem and Characterization of Local Optimality. IEEE Transactions on Pattern Analysis and Machine Intelligence. 6, 81--87 (1984)
[20] Li, G.L., Ma, Z.H., Huang, C., Chi, Y.W., Wang, H.G.: Segmentation of Color Images of Cucumber Diseases Using K_means Clustering Algorithm. Transactions of the CSAE. 26(Supp.2), 32--37 (2010)
[21] Li, Z.R., He, D.J.: Research on Identify Technologies of Apple’s Disease Based on Mobile Photograph Image Analysis (in Chinese). Computer Engineering and Design. 31, 3051--3053, 3095 (2010)