Development of an Intelligent System for Diagnosis of the Botrytis Elliptica Disease in the Lilium Plant Using Image Processing

Document Type : Research Paper


1 Department of Mechanics of Biosystems Engineering, Faculty of Agricultural and Rural Development, Agricultural Sciences and Resources University of Khuzestan, Ahvaz, Khuzestan Iran

2 Assistant professor, Department of Mechanics of Biosystems Engineering, Faculty of Agricultural and Rural Development, Agricultural Sciences and Resources University of Khuzestan, Ahvaz, Khuzestan Iran

3 Assistant professor, Horticultural Science Department, Faculty of Agriculture, Agricultural Sciences and Resources University of Khuzestan


The automatic detection of plant diseases in the early stages of growth can increase the quality of the final product and prevent the occurrence of permanent damage in large part of farms. Therefore, in this research an intelligent system was designed and developed based on image processing in order to detect and eliminate the disease in the lilium plant leaf, as well as the classification of healthy plants from the unhealthy ones. Accordingly, 20 healthy flowers and 20 unhealthy were evaluated by machine vision system. In order to classify plants, 19 color and morphology parameters of the plant were extracted and the most effective ones (leaf L, leaf a, leaf b, stem L, and stem length) were selected by fuzzy entropy method and these suitable features were grouped by the similarity classifier. As result, the efficiency of the proposed algorithm to diagnose and classify the disease using fuzzy entropy H1, H2 / H3 fuzzy entropy and without applying selection of features method were 96.15, 93.18 and 84.3, respectively.


Main Subjects

Abdanan Mehdizadeh, S., & Soltani Kazemi, M. (2016).Manufacturing and testing of a system to detect bee colony density inside the hive using machine vision. Iranian Journal of Biosystem Engineering, 47(1), 21-29 (In Farsi with English abstract)
Arivazhagan, S., Shebiah, R.N., Nidhyanandhan, S.S. & Ganesan, L. (2010). July. Classification of citrus and non-citrus fruits using texture features. In Computing Communication and Networking Technologies (ICCCNT), 2010 International Conference on (pp. 1-4). IEEE
Bandemer, H. & Nather, W. (1992). Fuzzy data analysis. Kluwer Academic Publisher
Barbedo, J.G.A., Koenigkan, L.V. & Santos, T.T. (2016). Identifying multiple plant diseases using digital image processing. Biosystems Engineering, 147, 104-116
Bashish, D. A., Braik M. & Bani-Ahmad, S. (2011). Detection and Classification of Leaf Diseases using Kmeans-based Segmentation and Neural-networks-based Classification. Information technology journal, 10(2), 257-266
Bernardes, A., Rogeri, J.R., Oliveira, N., Marranghello, A., Pereira, A. & Tavares, J.S. (2013). Identification of foliar diseases in cotton crop. Topics in Medical Image Processing and Computational Vision, 8, 67-85
Brosnan, T. & Sun, D. W. (2003). Influence of Modulated Vacuum Cooling on the Cooling Rate, Mass Loss and Vase Life of Cut Lily Flowers, Biosystems Engineering, 86(1), 45-49
Camargo, A. & Smith, J.S. (2009). An image-processing based algorithm to automatically identify plant disease visual symptoms. Biosystems engineering, 102(1), 9-21
De Luca, A. & Termini, S. (1971). A definition of non-probabilistic entropy in setting of fuzzy set theory. Information Control, 20, 301–312
Elad, Y., Messika, Y., Brand, M., Rav David, D. & Sztejnberg, A. (2007). Effect of microclimate on Leveillula taurica powdery mildew of sweet pepper. Phytopathology, 97(7), 813–824
Formato, F., Gerla, G. & Scarpati, L. (1999). Fuzzy subgroups and similarities. Soft Computing, 3, 1–6
Goodridge, W., Bernard, M., Jordan, R. & Rampersad, R. (2017). Intelligent diagnosis of diseases in plants using a hybrid Multi-Criteria decision making technique. Computers and Electronics in Agriculture, 133, 80-87
Guzman, M.G. & Harris, E. (2015). Dengue. The Lancet, 385(9966), 453-465
Hosseini, H., Mohammad Zamani, D. & Arbab. A. (2018). A recognition system to detect powdery mildew and anthracnose fungal disease of cucumber leaf using image processing and artificial neural networks technique. Scientific Journal of Agriculture, 40(4), 15-28 (In Farsi with English abstract)
Jamalizavareh, A., Sharifi tehrani, A., Hejarood, GH., Zad, J., Mohammadi, M. & Talebi. KH. (2004). An Investigation of the Effectiv eness of Acibenzolar– S –Methyl for the Control of Cucumber Powdery Mildew. Iranian Journal of Agriculture Science, 35(2), 285-292 (In Farsi with English abstract)
Johannes, A., Picon, A., Alvarez-Gila, A., Echazarra, J., Rodriguez-Vaamonde, S., Navajas, A.D. & Ortiz-Barredo, A. (2017). Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case. Computers and Electronics in Agriculture, 138, 200-209
Luukka, P. & Leppalampi, T. (2006). Similarity classifier with generalized mean applied to medical data. Computers in Biology and Medicine, 36, 1026–1040
Luukka, P., Saastamoinen, K. & Kononen, V. (2001). A classifier based on the maximal fuzzy similarity in the generalized Łukasiewicz-structure. In Proceedings of the FUZZ-IEEE 2001 conference, Melbourne, Australia.
Macedo-Cruz, A., Pajares, G., Santos, M. & Villegas-Romero, I. (2011). Digital image sensor-based assessment of the status of oat (Avena sativa L.) crops after frost damage. Sensors, 11(6), 6015-6036
Naik, M. R. & Sivappagari, C. M. R. (2016). Plant Leaf and Disease Detection by Using HSV Features and SVM Classifier. International Journal of Engineering Science, 3794
Parkash, O. M., Sharma, P. K. & Mahajan, R. (2008). New measures of weighted fuzzy entropy and their applications for the study of maximum weighted fuzzy entropy principle. Information Sciences, 178(11), 2389–2395
Revathi, P. & Hemalatha, M. (2012). Classification of cotton leaf spot diseases using image processing edge detection techniques. In Emerging Trends in Science, Engineering and Technology (INCOSET), 169-173
Sankaran, S., Mishra, A., Ehsani, R. & Davis, C. (2010). A review of advanced techniques for detecting plant diseases. Computers and Electronics in Agriculture, 72(1), 1-13
Sannakki, S.S., Rajpurohit, V.S., Nargund, V.B. & Arunkumar, R. (2013). Disease identification and grading of pomegranate leaves using image processing and fuzzy logic. International journal of food engineering, 9(4), 467-479
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 379–423, 623–659
Tavakoli, N., Hemmat, A. & Nazari, B. (2013). Preventing spread of downy mildew in greenhouse cucumber with machine vision system. Proceeding of National Conference of Passive Defense in Agriculture, Qeshm, Iran. (In Farsi with English abstract)
Tejonidhi, M. R., Nanjesh, B. R., Math, J. G. & D'sa, A.G. (2016). March. Plant disease analysis using histogram matching based on Bhattacharya's distance calculation. In Electrical, Electronics, and Optimization Techniques (ICEEOT), 1546-1549
Xu, H. R., Ying, Y. B., Fu, X. P. & Zhu, S. P. (2007). Near-infrared spectroscopy in detecting leaf miner damage on tomato leaf. Biosystems Engineering 96 (4), 447–454