Hybrid Deep Feature Extraction and Machine Learning for Potato Disease Classification
DOI:
https://doi.org/10.24252/shift.v6i2.285Keywords:
EfficientNetB3, SVM, XGBoost, Random ForestAbstract
Accurate classification of potato leaf diseases is crucial for improving agricultural productivity and reducing crop losses. This study proposes a hybrid classification framework that combines EfficientNetB3 as a deep feature extractor with classical machine learning classifiers, namely Support Vector Machine (SVM), Random Forest (RF), and XGBoost, to overcome the limitations of end-to-end convolutional neural networks in terms of computational cost and training time. Deep features extracted by EfficientNetB3 are used as inputs for the respective classifiers, and the proposed models are evaluated using accuracy, precision, recall, F1-score, and training time. Experimental results demonstrate that the hybrid models achieve superior performance and efficiency compared to the baseline EfficientNetB3 with Softmax classifier. Among all methods, EfficientNetB3 + XGBoost attains the highest accuracy of 0.966 with reduced training time, while EfficientNetB3 + SVM offers competitive accuracy with the fastest training process. These findings indicate that integrating deep feature extraction with traditional machine learning classifiers provides an effective and computationally efficient approach for potato leaf disease classification, making it suitable for practical implementation in precision agriculture.
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Copyright (c) 2026 Bimantyoso Hamdikatama, Kusrini, Arif Setyanto, Julius Jeremiah Allieu
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