Author : Sachin B. Jadhav 1
Date of Publication :26th April 2018
Abstract: Soybean Blight Brown Spot, Soybean powdery mildew and Downy Mildew are most common destructive foliar diseases of soybean and can cause significant yield loss. Timely application of fungicide, in the early stage of fungal infection, is important for effective control of the disease and is largely dependent upon the capability to quantitatively detection of the infection. The main purpose of this work is to identify and classify the soybean leaf disease based on the symptoms that are visible in leaf image. In this paper, Color-based segmentation method (K-means clustering) has been in corporate for separating the infected region from the leaf image. The infected stains are characterized by the features like color and textures. In the classification phase the color co-occurrence features, based on SGDM, are extracted and compared with the corresponding feature values stored in the feature library
Reference :
-
- DaeGwan Kim, Thomas F. Burks, Jianwei Qin, Duke M. Bulanon, “Classification of grapefruit peel diseases using color texture feature analysis”, International Journal on Agriculture and Biological Engineering, Vol:2, No:3,September 2009.
- H.Al-Hiary, S. Bani-Ahmad, M.Reyalat, M.Braik and Z.AlRahamneh, “Fast and Accurate Detection and Classification of Plant Diseases”, International Journal of Computer Applications (0975-8887), Volume 17-No.1.March 2011.
- Mohammed El.Helly, Ahmed Rafea and Salwa-ElGammal, “An Integrated image Processing System for Leaf Disease Detection and Diagnosis”.
- Al-Bashish, D., M. Braik and S. Bani-Ahmad, 2011. “Detection and classification of leaf diseases using K-meansbased segmentation and neural networks based classification”. Inform. Technol. J., 10: 267-275. DOI:10.3923/itj.2011.267.275, January, 2011.
- Qin J, Burks T F, Kim M S, Chao K, Ritenour M A. “CitrusPCA-based image classification method”. Sens. Instrum. FoodQual. Saf., 2008.
- F.Argenti, L.Alparone and G.Benelli , Fast Algorithms for Texture Analysis using Co-occurrence Matrices, IEEE proceedings,Vol.137, No.6, pp. 443-448, 1990.
- Sanjeev S Sannakki, Vijay S Rajpurohit, V B Nargund, et.al(2011), “ Leaf Disease Grading by Machine Vision and FuzzyLogic”, Int. J. Comp. Tech. Appl., Vol 2 (5), 1709- 1716
- Prasad Babu, M. S. and SrinivasaRao, B. “Leaves recognition using back-propagation neural network - advice for pest and disease control on crops’. Technical report, Department of Computer Science &system Engineering, Andhra University, India
- J. MacQueen. “Some methods for classification and analysis of multivariate observations”. In L. M. LeCam and J. Neyman, editors,Proceedings of the Fifth Berkeley Symposium on MathematicaStatistics and Probability, volume 1, pages 281--297, Berkeley, CA.
- A. Camargo, J.S. Smith, An image-processing based algorithm toautomatically identify plant disease visual symptoms, Biosystems Engineering, Volume 102, Issue 1, January 2009, Pages 9-21, ISSN1537- 5110,DOI:10.1016/j.biosystemseng.2008.09.030.
- 10.1016/j.biosystemseng.2008.09.030. [11] Dr.Sanjay B. Patil, Dr. S.K.Bhodhe, “Betel Leaf Area Measurement Using Image Processing.”International Journal on Computer Science and Engineering (IJCSE)ISSN: 0975-3397Vol. 3 No. 7 July 2011.pp 2656-2660.
- Dr.Sanjay B. Patil, Dr. S.K.Bhodhe, “leaf disease severity Measurement using image Processing.”International Journal of Engineering and Technology Vol.3 (5), 2011,pp 297-301