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Potato diseases detection and classification using deep learning methods

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Abstract

Using machine vision and image processing methods has an important role in the identification of defects of agricultural products, especially potatoes. The applications of image processing and artificial intelligence in agriculture in identifying and classifying pests and diseases of plants and fruits have increased and research in this field is ongoing. In this paper, we use the convolution neural network (CNN) methods, also, we examined 5 classes of potato diseases with the names: Healthy, Black Scurf, Common Scab, Black Leg, Pink Rot. We used a database of 5000 potato images. We compared the results of potato defect classification our methods with other methods such as Alexnet, Googlenet, VGG, R-CNN, Transfer Learning. The results show that the accuracy of the deep learning proposed method is higher than other existing works. We get 100% and 99% accuracy in some of the classes, respectively.

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Acknowledgements

I would like to thank Dr. Ashourian and Dr. Ghabili for participating in the writing or technical editing of the manuscript. I would also like to thank Dr. Navid Razmjooy for serving as scientific advisor.

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Authors and Affiliations

  1. Department of Electrical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran

    Ali Arshaghi & Leila Ghabeli

  2. Department of Electrical Engineering, Majlesi Branch, Islamic Azad University, Isfahan, Iran

    Mohsen Ashourian

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  1. Ali Arshaghi
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Correspondence to Ali Arshaghi.

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Arshaghi, A., Ashourian, M. & Ghabeli, L. Potato diseases detection and classification using deep learning methods. Multimed Tools Appl 82, 5725–5742 (2023). https://doi.org/10.1007/s11042-022-13390-1

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