CLASSIFICATION OF CITRUS LEAF DISEASES BY IMAGE PROCESSING BASED ON SHAPE RELATED FEATURES

D.K. PARMAR¹*, D.R. KATHIRIYA², K.P. PATEL^3
1College of Agricultural Information Technology, Anand Agricultural University, Anand, Gujarat, 388110, India
²College of Agricultural Information Technology, Anand Agricultural University, Anand, Gujarat, 388110, India
³College of Agricultural Information Technology, Anand Agricultural University, Anand, Gujarat, 388110, India
* Corresponding Author : dkparmaranand@gmail.com

Received : 09-09-2018     Accepted : 12-10-2018     Published : 15-10-2018
Volume : 10     Issue : 19       Pages : 7288 - 7293
Int J Agr Sci 10.19 (2018):7288-7293

Keywords : Plant diseases, shape features, neural network, image classification
Conflict of Interest : None declared
Acknowledgements/Funding : Author thankful to College of Agricultural Information Technology, Anand Agricultural University, Anand, Gujarat, 388110, India
Author Contribution : All author equally contributed

The whole image has plenty number of pixels, but only a small number of pixels are essential and those only required to be separate out. In image processing, feature extraction is a special technique for dimension reduction. Transforming the input data into a small and meaningful set of features is called feature extraction. If the features extraction process is carefully chosen then it is expected that the feature set will find the relevant information from the input image in order to perform the desired task using the reduced formation of features instead of the full-size input image. In machine learning, pattern recognition and image processing, feature extraction starts from the initial set of measured data and creates the values acquired for the purpose of being informative and non-redundant, which facilitates subsequent learning and generalization measures, and in some cases the leading human interpretation Better way Being a human can better understand the reduced feature set rather than the full image. The paper presents total nine shape related features like: Area, Perimeter, Major and Minor axis, Eccentricity, Orientation, Equivalent Diameter, Solidity and Extent. These shape features extracted by using four leaf disease segmentation techniques and they are: Segmentation with RGB and HSI, K--Means Clustering Algorithm, Segmentation by Different Transformation and Segmentation by RGB, HIS and Contrast respectively. Finally, the extracted features applied to neural network with three training functions and results analyzed. The paper compares the benefits and limitations of these potential methods.

1. Alham F. Aji, Qorib Munajat, Ardhi P. Pratama, Hafizh Kalamullah, Aprinaldi, Jodi Setiyawan, and Aniati M. Arymurthy (2013) International Journal of Computer Theory and Engineering, 5, 3.
2. Brita Fritsch, Janine Reis, Keri Martinowich, Heidi Schambra, Yuanyuan Ji (2010) Neuron, 29, 66(2), 198–204.
3. Demuth, Howard, Mark Beale, and Martin Hagan. Neural Network Toolbox™ 6, User Guide
4. Feng Qin, Dongxia Liu, Bingda Sun, Liu Ruan, Zhanhong Ma, Haiguang Wang (2016) PLoS ONE, 11(12): e0168274.
5. Guru D.S., Mallikarjuna P.B., Manjunath S. (2011) Segmentation and Classification of Tobacco Seedling Diseases. In: Fourth Annual ACM Bangalore Conference, Bangalore, March 25-26 31.
6. Jonas Alberto RiosDaniel DebonaHenrique Silva Silveira DuarteFabrício Avila Rodrigues (2013) European Journal of Plant Pathology, 136(3), 603–611.
7. Kumar A. (2008) IEEE Transactions on Industrial Electronics, 55(1), 348-363.
8. Marchant J.A., & Onyango C.M. (2003) Computers and Electronics in Agriculture, 39(1), 3–22.
9. Otsu N. (1979) (IEEE) Transactions on Systems, Man and Cybernetics, 9(1),62-66.
10. Rupali Patil, Sayali Udgave, Supriya More, Dhanashri Nemishte International Research Journal of Engineering and Technology, 3(4), 2330-2333.
11. Sachin Jagtap, Shailesh Hambarde (2014) IOSR Journal of VLSI and Signal Processing, 4(5) I, 24-30.
12. Toran Verma, Sipi Dubey (2017) Advances in Computational Sciences and Technology, 721-732.
13. Tewari V.K., Ashok Kumar Arudra, Satya Prakash Kumar, Vishal Pandey, Nandera Singh Chandel (2013) Agriculture Eng CIGR, Journal, 15, 2.
14. Vladimir Cherkassky, Yunqian Ma (2004) Neural Networks, 17, 113-126.
15. Xie C.Q., Wang J.Y., Feng L., Liu F., Wu D., He Y. (2013) Spectrosc. Spect. Anal., 33,1603–1607.
16. Zaller J. G. (2004) Weed Research, 44(6), 414–432.