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Enhanced plant identification and disease diagnosis through SqueezeNet and SVM for smart agriculture applications

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Abstract

The agriculture sector is a cornerstone of human sustenance and economic stability. Timely detection of plant diseases is essential for optimizing crop yields and mitigating potential losses. This paper presents a novel approach for plant identification and disease diagnosis based on leaf analysis. The proposed approach harnesses the capabilities of transfer learning through SqueezeNet, an eighteen-layer deep Convolutional Neural Network (CNN). A pre-trained SqueezeNet model is utilized for feature extraction from plant leaf images, followed by classification using a supervised machine learning technique, namely the Support Vector Machine (SVM) classifier. A stepwise disease detection framework was developed, integrating SqueezeNet and SVM classifier, and evaluated using image datasets comprising both diseased and healthy plant pairs. The experimental results reveal that the proposed approach achieves exceptional accuracy, ranging from 96.9% to 100%, in plant identification and disease detection through leaf analysis.

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Acknowledgements

The authors are very grateful to all the institutions in the affiliation list for successfully performing this research work. The authors would like to thank Prince Sultan University for their support.

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The authors did not receive support from any organization for the submitted work.

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Authors and Affiliations

  1. Tanta High Institute of Engineering and Technology (THIET), Tanta, Egypt

    Samia A. El-Moneim Kabel

  2. Automated Systems and Computing Lab, Prince Sultan University, 11586, Riyadh, Saudi Arabia

    Walid El-Shafai

  3. Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Walid El-Shafai & Fathi E. Abd El-Samie

  4. Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia

    Fathi E. Abd El-Samie

  5. Giza Higher Institute for Managerial Sciences, Tomah, Egypt

    Basma Mohamed

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  1. Samia A. El-Moneim Kabel
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  2. Walid El-Shafai
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  3. Fathi E. Abd El-Samie
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  4. Basma Mohamed
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Correspondence to Samia A. El-Moneim Kabel.

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Kabel, S.A.EM., El-Shafai, W., El-Samie, F.E.A. et al. Enhanced plant identification and disease diagnosis through SqueezeNet and SVM for smart agriculture applications. Iran J Comput Sci 8, 1353–1369 (2025). https://doi.org/10.1007/s42044-025-00237-9

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