Identification of apple leaf varieties using image processing and adaptive neuro- fuzzy inference system

Document Type : Research Paper

Authors

1 graguated of Biosystem Engineering, University College of Agriculture and Natural Resources, University Of Tehran

2 professor of Biosystem Engineering, University College of Agriculture and Natural Resources, University Of Tehran,

3 professor of Biosystem Engineering, University College of Agriculture and Natural Resources, University Of Tehran

4 Assistant of Biosystem Engineering, University College of Agriculture and Natural Resources, University Of Tehran

Abstract

In modern agriculture, image processing technique is used for mechanization and intelligent machines instead of humans. One of them is identifying varieties of plants and fruits. Identifying plant varieties is important in Plant eugenic programs. Visual examination of plant leaves and fruits are the common processes for this aim. Identification and classification of plants using machine vision techniques can be performed more quickly. In this study, four varieties of apple, Granny Smith, Golab Kohans, Gala, and Delbar-astyval were studied. After collecting leaf samples, the images of leaves were captured and then color, texture, and morphological properties from each image were extracted and adaptive neuro - fuzzy inference system (Anfis) was used for classification. The results showed that ANFIS was able to successfully classify leaves with input and output membership functions, respectively, linear and triangular and hybrid learning method in grid partitioning FIS mood with 95.83% accuracy.

Keywords

Main Subjects


 
Chen, Y.-R., Chao, K.,  and Kim, M. S.  (2002). Machine Vision Technology For Agricultural Applications. Computers and Electronics In Agriculture 36(2): 173-191.
Dowlati, M., Mohtasebi, S. S., Omid, M.,  Razavi, S. H., Jamzad, M.  & De La Guardia, M.  (2013). Freshness Assessment of Gilthead Sea Bream (Sparus Aurata) by Machine Vision Based on Gill And Eye Color Changes. Journal of Food Engineering 119(2): 277-287.
Gonzalez, R. C., Woods, R. E.  and Eddins, S. L. (2009). Digital Image Processing Using MATLAB.
Jang, J. S. (1993). ANFIS: Adaptive-Network-Based Fuzzy Inference System. Systems, Man and Cybernetics, IEEE Transactions On 23(3): 665-685.
Keshavarzmehr, M. (2011). Neural Networks, Fuzzy Logic and Genetic Algorithms, Tehran (In Farsi).
Kolyayee, R., Khabbaz, H., Kamali, H., (2001). Guide to Pests, Diseases and Weeds, Tehran (In Farsi).
Neethirajan, S. & Karunakaran, C.  (2006). Classification of Vitreousness In Durum Wheat Using Soft X-Rays and Transmitted Light Images. Computers and Electronics in Agriculture 53: 71–78.
Mahmoudi, M., Khazaei, J., Vahdati, K., Taleb, M. (2010). Walnut Genotype Detection Using Machine Vision Technique, the 5th National Congress of Agricultural Machinery And Mechanization, Mashhad (In Farsi).
Mehl, P. M., Chen, Y. R., Kim, M. S. And Chan, D. E.  (2004). Development of Hyperspectral Imaging Technique For The Detection of Apple Surface Defects And Contaminations. Journal of Food Engineering 61(1): 67-81.
Mollazade, K., Omid, M.  and Arefi, A. (2012). Comparing Data Mining Classifiers For Grading Raisins Based on Visual Features. Computers And Electronics In Agriculture 84(0): 124-131.
Xing, J., Saeys, W.  and De Baerdemaeker, J. (2007). Combination of Chemometric Tools And Image Processing For Bruise Detection on Apples. Computers And Electronics In Agriculture 56(1): 1-13.
 Tsheko, R.,( 2007). Discrimination of Plant Species Using Co-occurrence Matrix of Leaves. 12.
Zhao-Yan, L. and C. Fang, (2005) Identification of Rice Seed Varieties Using Neural Network. Journal of Zhejiang University Science, 6.