Volume 17, Issue 109 (2021)                   FSCT 2021, 17(109): 143-152 | Back to browse issues page

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Khanramaki M, Askari Asli‐Ardeh E, Kozegar E, loni R. Detection of common citrus pests in northern Iran using an artificial neural network. FSCT 2021; 17 (109) :143-152
URL: http://fsct.modares.ac.ir/article-7-43117-en.html
1- Ph.D. student, Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
2- Associate professor, Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil, Iran , ezzataskari@uma.ac.ir
3- Department of Computer Sciences and Engineering, University of Guilan, Guilan, Iran
4- Postdoc Researcher, Department of Biosystems Engineering, Tarbiat Modares University, Tehran, Iran
Abstract:   (1569 Views)
Plant pests and diseases are categorized as one major group threatening to food security. In large farms, accurate and timely human diagnosis is not possible due to time consuming and possible misdiagnosis. Therefore, for immediate, automatic, appropriate and accurate detection of agricultural pests, the use of image processing and artificial intelligence, including deep learning can be very useful. In this study, convolutional neural network models have been developed to identify three common citrus pests in northern Iran such as citrus leafminer, sooty mold and pulvinaria using images of infected leaves, through deep learning methods. For this purpose, Resnet50 and VGG16 architectures are trained as well-known convolutional neural networks, applying the transfer learning method on 1774 images of infected citrus leaves, accumulated from natural and field conditions. In the training phase, data augmentation is used to increase the number of training samples, and to improve the generalizability of the classifiers. For experimental analysis, cross validation strategy is used to evaluate the accuracy of the convolutional neural network. In this strategy, all images are tested without any overlap between training and test sets. Based on the results, the accuracies of Resnet 50 and VGG 16 models are evaluated as 96.05 and 89.34%, respectively. Hence, the Resnet 50 model can convert the above method into a very suitable early warning or consulting system.
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Article Type: Original Research | Subject: Food industry machines
Received: 2020/05/24 | Accepted: 2020/09/2 | Published: 2021/03/9

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