Developing a new hybrid system for detection of apple tree leaves diseases

Document Type : Research Paper

Authors

1 Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran

2 University of Tehran

3 Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran

Abstract

Each year, plant diseases cause considerable damages to the agricultural sector which their effect on the economy and food security is very important. Early detection of plant diseases is a useful strategy to reduce these losses. In recent years, researchers have used a variety of techniques such as machine vision for the diagnosis of plant diseases. In this study, a new system, consisting of digital image processing technique and also combination model of artificial neural network to distinguish three apple tree leaf diseases (namely Alternaria, apple black spot, and apple leaf miner pest) were used. In short, the process of digital image processing technique involves preparation, processing, and extraction of features of each of the sample images and the hybrid artificial neural network model was used to classify diseases. In this model, particle swarm optimization algorithm for network training (PSO) and Levenberg-Marquardt (LM) were used. After that, the operation of the proposed system for diagnosis of diseases of apple trees was evaluated. It is concluded that the system has a good performance for diagnosis accuracy was 99% and R2=0.985, RMSE=0.099. Finally, in comparison with other methods mentioned by other researchers for diagnosis of apple tree leaves diseases, the proposed system has higher ability.

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Main Subjects

References

Asraf, H. M., Nooritawati, M. T., Shah Rizam, M. S. B. (2012). A Comparative Study in Kernel-Based Support Vector Machine of Oil Palm Leaves Nutrient Disease, Journal of Procedia Engineering, 41, 1353–1359.
Billah, M., Miah, M. B. A., Hanifa, A. and Ruhul Amin, M. d. (2015). Adaptive Neuro Fuzzy Inference System based Tea Leaf Disease Recognition using Color Wavelet Features, Journal of Communications on Applied Electronics, 3(5), 1–4.
Eberhart, R. and Kennedy, J. (1995). A new optimizer using particle swarm theory, Journal of IEEE Service Center, 39–43.
Fang, J., Zhang, C., Wang, S. (2008). Application of genetic algorithm (GA) trained artificial neural to identify tomatoes with physiological, Journal of Computer and Computing Technologies in Agriculture, 259, 1103-1111.
Food and Agriculture Organization. (2013). Statistics: Crops (Production) in FAO. Retrieved January 12, 2013, From http://www.fao.org/faostat/en/#data/QC
Kumar, A., Lee, W. S., Ehsani, R. J., Albrigo, G., Yang, C., Mangan, R. L.  (2012). Citrus greening disease detection using aerial hyperspectral and multispectral imaging techniques, Journal of Applied Remote Sensing, 6, 1–22.
Loutfi, A., Coradeschi, S., Mani, G.K., Shankar, P. Rayappan, J.B. (2015). Electronic noses for food quality: a review. Journal of Food Engineering, 144, 103–111.
Mahlein, A. K., Steiner, U., Hillnhütter, C., Dehne, H. W., Oerke, E. C. (2012). Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases, Journal of Plant Methods, 1–13.
Massimo, F. R, Daciana, I., Clarkson, J. P., Covington, J. A. (2016). Early identification of potato storage disease using an array of metal-oxide based gas sensors, Journal of Postharvest Biology and Technology, 116, 50–58.
Omrani, E. (2013). Plant Diseases Detection Using Image Processing Technique. M. Sc. Thesis, University of Tehran, College of Agriculture and Natural Resources. (In Farsi)
Omrani, E., Khoshnevisan, B., Shamshirband, S., Saboohi, H., Anuar, N. B., Nasir, M. H. N. M. (2014). Potential of radial basis function-based support vector regression for apple disease detection, Journal of Measurement, 55, 512–519.
Pan, L., Zhang, W., Zhu, N., Mao, Sh., Wangd, J., Tu, K. (2014). Early detection and classification of pathogenic fungal disease in post-harvest strawberry fruit by electronic nose and gas chromatography–mass spectrometry, Journal of Food Research International, 62, 162–168.
Tian, J., Qiuxia, H., Xiaoyi, M. A., Mingyu, H. A. N. (2012). An Improved KPCA/GA-SVM Classification Model for Plant Leaf Disease Recognition, Journal of Computational Information Systems, 33, 7737–7745.
Tian, X., Wang, J. & Cui, S. (2013). Analysis of pork adulteration in minced mutton using electronic nose of metal oxide sensors. Journal of Food Engineering, 119(4), 744–749.
UGA Extension. (2014). Georgia Plant Disease Loss Estimates 2014. AP 102-7, From http://extension.uga.edu/publications/detail.cfm?number=AP102-7
Yeh, Y. H., Chung W. C., Liao, J.Y., Chung, C. L., Kuo, Y. F. and Te, L. T. (2016). Strawberry foliar anthracnose assessment by hyperspectral imaging, Journal of Computers and Electronics in Agriculture, 122, 1–9.
Zhanga, Z., Hea, X., Sunb, X., Guoc, L., Wangd, J., Wangd, F. (2015). Image Recognition of Maize Leaf Disease Based on GA-SVM, Journal of Chemical Engineering Transactions, 46, 199–204.
Zhang, Sh., Wu, X., You, Zh., Zhang, L. (2017). Leaf image based cucumber disease recognition using sparse representation classification, Journal of Computers and Electronics in Agriculture, 134, 135–141.
Iranian Journal of Biosystems Engineering
Volume 49, Issue 2
July 2018
Pages 215-225

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History

  • Receive Date: 02 May 2017
  • Revise Date: 06 November 2017
  • Accept Date: 08 November 2017

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