Developing a new hybrid system for detection of apple tree leaves diseases | ||
| مهندسی بیوسیستم ایران | ||
| Article 6, Volume 49, Issue 2, July 2018, Pages 215-225 PDF (1.06 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.22059/ijbse.2017.232483.664936 | ||
| Authors | ||
| Zohreh Ghasemi Varjani1; Seyed Saeid Mohtasebi* 2; Hadi Ghasemi3; Elham Omrani1 | ||
| 1Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran | ||
| 2University of Tehran | ||
| 3Department 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. | ||
| Keywords | ||
| plant diseases; image processing; artificial neural network; Particle Swarm Optimization | ||
| References | ||
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