توسعه سامانه هوشمند تشخیص بیماری آتشک در گیاه لیلیوم با استفاده از روش پردازش تصویر

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد، دانشکده مهندسی زراعی و عمران روستایی، گروه مکانیک بیوسیستم، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان

2 استادیار گروه مهندسی مکانیک بیوسیستم، دانشکده مهندسی زراعی و عمران روستایی، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان، اهواز، ایران

3 استادیار دانشکدة کشاورزی، گروه علوم باغبانی، دانشگاه کشاورزی و منابع طبیعی رامین خوزستان

چکیده

تشخیص خودکار بیماری­های گیاهی در مراحل اولیه در مزارع بزرگ می­تواند علاوه بر افزایش کیفیت محصول نهایی از  بروز خسارات جبران ناپذیر نیز جلوگیری نماید. لذا در این پژوهش سامانه­ای هوشمند بر مبنای پردازش تصاویر به منظور شناسایی و رفع بیماری آتشک در­ برگ گیاه لیلیوم و همچنین طبقه­بندی گیاه سالم از بیمار طراحی و توسعه یافت. بر این اساس تعداد 20 گل­ سالم و  20 گل آلوده توسط سامانه بینایی ماشین ارزیابی شدند. به منظور طبقه­بندی گیاهان تعداد 19 ویژگی رنگی و موفولوژیگی از گیاه استخراج و موثرترین این ویژگی­ها (L برگ، a برگ، b برگ، L ساقه و طول ساقه) با کمک روش آنتروپی فازی انتخاب و به وسیله طبقه­بند مشابه گروه­بندی گردیدند. راندمان الگوریتم پیشنهادی در تشخیص و طبقه­بندی بیماری برای آنتروپی فازی H1، آنتروپی فازی H2/H3 و بدون انتخاب ویژگی به ترتیب 15/96، 18/93 و 3/84  بدست آمد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

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

نویسندگان [English]

  • Hadis Biabi 1
  • Saman Abdanan Mehdizadeh 2
  • Mohamadreza Salehi Salmi 3
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
چکیده [English]

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.

کلیدواژه‌ها [English]

  • Plant leaf disease
  • image processing
  • fuzzy entropy
  • similarity classifier

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