Plant Disease Detection and Classification Using Deep Learning and Image Processing

Oguzhan  BASPINAR; Ali  YASAR; Yavuz Selim  TASPINAR

doi:10.58190/imiens.2026.166

Authors

Oguzhan BASPINAR Technology Faculty, Mechatronics Engineering, Selcuk University, Konya, Türkiye https://orcid.org/0009-0006-9054-0204
Ali YASAR Technology Faculty, Mechatronics Engineering, Selcuk University, Konya, Türkiye https://orcid.org/0000-0001-9012-7950
Yavuz Selim TASPINAR Technology Faculty, Mechatronics Engineering, Selcuk University, Konya, Türkiye https://orcid.org/0000-0002-7278-4241

DOI:

https://doi.org/10.58190/imiens.2026.166

Keywords:

Classification, Comparison of squeezenet and inceptionV3, Image processing, Performance analysis, Plant diseases

Abstract

In agriculture, plants play a vital role in sustaining both human life and the ecosystem. However, plant diseases significantly affect crop yield and quality, making early detection essential. In this study, a dataset consisting of 458 healthy and 435 diseased plant leaf images was used for classification. SqueezeNet and InceptionV3 deep learning architectures were employed for feature extraction, and machine learning models including Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Logistic Regression (LR) were used for classification. Model performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrix metrics. The experimental results show that the InceptionV3-based models achieved higher classification performance compared to SqueezeNet with the LR classifier providing the best overall accuracy of 98.48%. This study provides a comprehensive comparison of deep learning architectures combined with traditional machine learning classifiers and demonstrates the effectiveness of hybrid approaches for plant disease detection. The findings contribute to the development of accurate and efficient systems for early plant disease diagnosis.

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