Statistical Methods for Quantitatively Detecting Fungal Disease from Fruits’ Images

Year 2013, Volume: 1 Issue: 4, 60 - 67, 03.10.2013

Jagadeesh Pujari Rajesh Yakkundimath , Abdulmunaf Byadgi

Abstract

In this paper we have proposed statistical methods for detecting fungal disease and classifying based on disease severity levels.  Most fruits diseases are caused by bacteria, fungi, virus, etc of which fungi are responsible for a large number of diseases in fruits. In this study images of fruits, affected by different fungal symptoms are collected and categorized based on disease severity. Statistical features like block wise, gray level co-occurrence matrix (GLCM), gray level runlength matrix (GLRLM) are extracted from these images. The nearest neighbor classifier using Euclidean distance was used to classify images as partially affected, moderately affected, severely affected and normal. The average classification accuracies are 91.37% and 86.715% using GLCM and GLRLM features. The average classification accuracy has increased to 94.085% using block wise features.

Keywords

Fungal disease; Fruits; Statistical features; Euclidean distance; Nearest neighbor classifier

References

• Jagadeesh D. Pujari, Rajesh.Yakkundimath and A.S.Byadgi (2013). Grading and Classification of anthracnose fungal disease in fruits. International Journal of Advanced Science and Technology. Vol.52.
• M.Akhill.Jabbar, D.L.Deekshatulu and Priti.Chandra (2013). Heart disease classification using nearest neighbor classifier with feature subset selection, Annals Computer Science Series. 11th Tome 1st Fasc.
• Vishal S.Thakare, Nitin N. Patil and Jayshri S. Sonawane (2013). Survey on Image Texture Classification Techniques International Journal of Advancements in Technology. Vol.4. Page.1.
• Sudheer reddy bandi, Varadharajan A and A Chinnasamy (2013). Performance evaluation of various statiscal classifiers in detecting the diseased citrus leaves. International Journal of Engineering Science and Technology (IJEST). Vol. 5.
• Shiv Ram Dubey and Anand Singh Jalal (2012). Detection and Classification of Apple Fruit
• Diseases using Complete Local Binary Patterns. Third IEEE International Conference on Computer and Communication Technology (ICCCT-2012), MNNIT Allahabad, India. Pages. 346-351.
• Shiv Ram Dubey and Anand Singh Jalal (2012). Adapted Apple for Fruit Disease Identification
• using Images. International Journal of Computer Vision and image Processing (IJCVIP). Vol. 2. Pages. 51 – 65.
• Patil J.K and Raj Kumar (2012). Feature extraction of diseased leaf images. Journal of signal and image processing. Vol.3. Pages.60-63.
• R. Mishra, D. Karimi, R. Ehsani and W. S. Lee (2012). Identification of citrus greening using a
• VIS-NIR spectroscopy technique. Transactions of the ASABE. Vol. 55. Pages. 711-720.
• Mandeep Singh and Bharti Chauhan (2012). Classification: A holistic View. International Journal of Computer Science & Communication. Vol. 3. Pages. 69-72.
• Sabine D. Bauer, Filip Korc and Wolfgang Forstner (2011). The potential of automatic methods of classification to identify leaf diseases from multispectral images. Precision Agriculture, Springer-Verlag. Vol.12.Pages.361-377.
• Jayamala K. Patil and Raj Kumar (2011). Advances in image processing in image processing for detection of plant diseases. Journal of Advanced Bioinformatics Applications and Research Vol.2. Issue 2. Pages. 135-141.
• Lili N.A, F. Khalid and N.M. Borhan(2011).Classification of Herbs Plant Diseases via Hierarchical Dynamic Artificial Neural Network after Image Removal using Kernel Regression Framework. International Journal on Computer Science and Engineering. Vol. 3. No.1.
• D. Moshou, C. Bravo, R. Oberti, J.S. West, H. Ramon, S. Vougioukas and D. Bochtis (2011). Intelligent multi-sensor system for the detection and treatment of fungal diseases in arable crops. Biosystems Engineering. Vol.18. Pages. 3 11 -3 2 1.
• D S Guru, P B Mallikarjuna and S Manjunath (2011). Segmentation and Classification of Tobacco Seedling Diseases. COMPUTE '11 Proceedings of the Fourth Annual ACM Bangalore Conference.
• Basvaraj .S. Anami, J.D.Pujari and Rajesh.Yakkundimath (2011). Identification and Classification of Normal and Affected Agriculture/horticulture Produce Based on Combined Color and Texture Feature Extraction. International Journal of Computer Applications in Engineering Sciences. Vol.1.
• H. Al-Hiary, S. Bani-Ahmad, M. Reyalat, M. Braik and ALRahamneh (2011). Fast and Accurate Detection and Classification of Plant Diseases. International Journal of Computer Applications. Vol.17.
• Ryusuke Nosaka, Yasuhiro Ohkawa, and Kazuhiro Fukui (2011). Feature Extraction Based on Co-occurrence of Adjacent Local Binary Patterns. Springer-Verlag Berlin Heidelberg, Part II, LNCS 7088. Pages. 82–91.
• Di Cui, Qin Zhang, Minzan Li, Glen L.Hartman and Youfu Zhao (2010). Image processing methods for quantatively detecting soyabean rust from multispectral images. Biosystems Engineering. Vol.107. Pages.186-193.
• T.Rumpf, A.K.Mahlein, U.Steiner, E.C.Oerke, H.W.Dehne and L.Plumer (2010). Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance. Computers and Electronics in Agriculture. Vol.74. Pages.91-99.
• A. Camargo and J.S. Smith (2009). Image pattern classification for the identification of disease causing agents in plants. Computers and Electronics in Agriculture, Vol.66, page.121–125.
• Qing Yao, Zexin Guan, Yingfeng Zhou, Jian Tang, Yang Hu and Baojun Yang (2009). Application of support vector machine for detecting rice diseases using shape and color texture features. International Conference on Engineering Computation.
• Di Cui, Qin Zhang, Minzan Li, Glen L.Hartman and Youfu Zhao (2009). Detection of soyabean rust using a multispectral image sensor, Sensor & Instruments, Food Quality. Springer-Verlag. Vol.3. Pages.49-56.
• Dae Gwan Kim, Thomas F. Burks, Jianwei Qin and Duke M. Bulanon. (2009). Classification of grapefruit peel diseases using color texture feature analysis. International journal of Agricultural & Biological Engineering. Vol. 2.
• Geng Ying, Li Miao, Yuan Yuan and Hu Zelin (2008). A Study on the Method of Image Pre-Processing for Recognition of Crop Diseases. International Conference on Advanced Computer Control.
• Abdul Malik Khan and Andrew P. Papliński (2008). Blemish detection in citrus fruits. Proceedings of SPIT-IEEE Colloquium and International Conference, Mumbai, India. Vol.1.Pages.203-211.
• Kuo-Yi Huang (2007). Application of artificial neural network for detecting Phalaenopsis seedling diseases using color and texture features. Computers and Electronics in agriculture. Vol.57. Pages.3–11.
• Alexander A. Doudkin, Alexander V. Inyutin, Albert I. Petrovsky and Maxim E. Vatkin (2007). Three-level neural network for data clusterization on images of infected crop field. Journal of Research and Applications in Agricultural Engineering. 2007. Vol. 52.
• R. Pydipati, T.F. Burks and W.S. Lee (2006). Identification of citrus disease using color texture features and discriminate Analysis. Computers and Electronics in Agriculture. Vol.52. Pages. 49-59.
• Hamid Muhammed and Hamed (2005). Hyperspectral crop reflectance data for characterizing and estimating fungal disease severity in wheat, Biosystems Engineering. Vol.91. Pages.9-20.
• D.G. Sena Jr, F.A.C. Pinto1, D.M. Queiroz1 and P.A. Viana (2003). Fall Armyworm Damaged Maize Plant Identification using Digital Images. Biosystems Engineering. Vol.85. Pages.449–454
• Pinstrup-Andersen (2001). The Future World Food Situation and the Role of Plant Diseases. DOI: 10.1094/PHI-I-2001-0425-01.
• Marc Lefebvre, Sylvia Gil, Denis Brunet, E. Natonek, C. Baur, P. Gugerli and Thierry Pun(1993). Computer vision and agricultural robotics for disease control: the Potato operation.
• Computers and Electronics in Agriculture. Vol.9. Pages. 85-102
• R.M.Harlick, K.shanmugam and H.Dinstein (1973). Texture features for image classification. IEEE Transactions on systems, man and cybernetics. Vol.3. Pages.610-621.
• Rafael C. Gonzalez(2009). Digital Image Processing, Using MATLAB, 2nd Edition.
• R. O. Duda, P. E. Hart and D. G. Stork (2000). Pattern classification, John Wiley & Sons.
• Dr. K T. Chandy. Important Fungal Diseases: Plant Disease Control, Booklet No. 342, PDCS.
• C. H. Chen, L. F. Pau and P. S. P. Wang(1998).The Handbook of Pattern Recognition and Computer Vision, 2nd Edition, Pages. 207-248, World Scientific Publishing Corporation.