Cucumber is one of the most consumed products. Performance and quality of the cucumber production is affected by various factors such as pests, insects, and various diseases. Diagnosing the diseases in early stages can reduce economic losses, and increase the quality of production. Agriculture is an important area for the implementation of common techniques based on AI-powered machine vision. Deep learning is one of the different types of common techniques in artificial intelligence, which has made significant contributions to the classification and identification of operations used in precision agriculture. In this research, Convolutional Neural Networks (CNN) based on deep learning were used to identify and classify healthy and unhealthy cucumber leaves. ReseNet-101 and MobileNet-v3 architectures were used to train healthy and unhealthy leaves of cucumber. The dataset was obtained from the Kaggle platform, and after appropriate preprocessing, it was trained and evaluated. Despite being shallow and with a small number of training parameters, MobileNet-v3 architecture provided significant results. The accuracy of the presented architecture identification and classification was equal to %98.64. The use of this type of architecture will be very suitable for use in smartphones and embedded systems due to its light and shallow structure.
Akhtari, H. , Navid, H. , Gaffarnejad, A. and Etminan far, N. (2025). A comparative study of two deep and shallow convolutional neural network architectures for classifying of cucumber leaf diseases. Journal of Modeling in Engineering, (), -. doi: 10.22075/jme.2025.32983.2609
MLA
Akhtari, H. , , Navid, H. , , Gaffarnejad, A. , and Etminan far, N. . "A comparative study of two deep and shallow convolutional neural network architectures for classifying of cucumber leaf diseases", Journal of Modeling in Engineering, , , 2025, -. doi: 10.22075/jme.2025.32983.2609
HARVARD
Akhtari, H., Navid, H., Gaffarnejad, A., Etminan far, N. (2025). 'A comparative study of two deep and shallow convolutional neural network architectures for classifying of cucumber leaf diseases', Journal of Modeling in Engineering, (), pp. -. doi: 10.22075/jme.2025.32983.2609
CHICAGO
H. Akhtari , H. Navid , A. Gaffarnejad and N. Etminan far, "A comparative study of two deep and shallow convolutional neural network architectures for classifying of cucumber leaf diseases," Journal of Modeling in Engineering, (2025): -, doi: 10.22075/jme.2025.32983.2609
VANCOUVER
Akhtari, H., Navid, H., Gaffarnejad, A., Etminan far, N. A comparative study of two deep and shallow convolutional neural network architectures for classifying of cucumber leaf diseases. Journal of Modeling in Engineering, 2025; (): -. doi: 10.22075/jme.2025.32983.2609
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