Detection of common citrus pests in northern Iran using an artificial neural network

Authors
Morteza Khanramaki 1
Ezzatollah Askari Asli‐Ardeh 2
Ehsan Kozegar 3
reihaneh loni 4
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
3 Department of Computer Sciences and Engineering, University of Guilan, Guilan, Iran
4 Postdoc Researcher, Department of Biosystems Engineering, Tarbiat Modares University, Tehran, Iran
10.52547/fsct.17.109.143
Abstract
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.
Keywords
citrus pests
deep learning
Convolutional Neural Networks
Resnet50
VGG16

Subjects

[1] Ali, H., Lali, M. I., Nawaz, M. Z., Sharif, M. and Saleem, B. A. 2017. Symptom based automated detection of citrus diseases using color histogram and textural descriptors. Computers and Electronics in Agriculture, 138, 92–104.
[2] Abdulridha, J., Ampatzidis, Y., Ehsani, R. and Decastro, A. 2018. Evaluating the performance of spectral features and multivariate analysis tools to detect laurel wilt disease and nutritional deficiency in avocado. Computers and Electronics in Agriculture, 155, 203–211.
[3] Ampatzidis, Y., Debellis, L. and Luvisi, A. 2017. IPathology: robotic applications and management of plants and plant diseases. Sustainability, 9(6): 1010 - 1118.
[4] Zhou, R., Kaneko, S., Tanaka, F., Kayamori, M. and Shimizu, M. 2014. Disease detection of cercospora leaf Spot in sugar beet by robust template matching. Computers and Electronics in Agriculture, 108, 58–70.
[5] Kaur, S., Pandey, S. and Goel, S. 2018. A semi-automatic leaf disease detection and classification system for soybean culture. IET Image Processing, 12 (6): 1038-1048.
[6] Sengar, N., Dutta, M.K. and Travieso, C.M. 2018. Computer vision based technique for identification and quantification of powdery mildew disease in cherry leaves. Computing, 100(11): 1–13.
[7] Sharif, M., Khana, M.A., Iqbala, Z., Azama, M.F., Lalib, M.I.U. and Javedc, M.Y. 2018. Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection. Computers and Electronics in Agriculture, 150, 220–234.
[8] LeCun, Y., Bottou, L., Bengio, Y. and Haffner, P. 1998. Gradient-based learning applied to document recognition. Proceedings of the Institute of Electrical and Electronics Engineering (IEEE), 86(11): 2278–2324.
[9] Luvisi, A., Ampatzidis, Y. and Debellis, L. 2016. Plant pathology and information technology: Opportunity for management of disease outbreak and applications in regulation frameworks. Sustainability, 8(8), 831.
[10] Cruz, A.C., Luvisi, A., Debellis, L. and Ampatzidis, Y. 2017. X-FIDO: An effective application for detecting olive quick decline syndrome with deep learning and data fusion. Frontiers in Plant Science, 8, 1–12.
[11] Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311–318.
[12] Dhankhar, P. 2019. ResNet-50 and VGG-16 for recognizing Facial Emotions. International Journal of Innovations in Engineering and Technology. 13(4): 126-130
[13] He, K., Zhang, X., Ren, S. and Sun, J. 2016. Deep residual learning for image recognition. Institute of Electrical and Electronics Engineering (IEEE) Conference on Computer Vision and Pattern Recognition, pp. 770–778. Las Vegas, USA.
[14] Simonyan, K. and Zisserman, A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv, 1409. 1556