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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Ihzanurahman, Fazlur
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Computer Science & IT Engineering

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Hybrid machine learning for imbalanced lettuce disease classification Ihzanurahman, Fazlur; Mahmudy, Wayan Firdaus
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1783-1789

Abstract

This study investigates a hybrid machine learning framework combining EfficientNet-B3 feature extraction with classical classifiers for lettuce disease classification under conditions of extreme class imbalance. The system utilizes EfficientNet-B3 to extract high-dimensional feature embeddings from 2,337 images, which are subsequently classified using support vector machine (SVM), random forest (RF), and k-nearest neighbors (KNN). Although the proposed SVM-based model achieves a high overall accuracy of 94.01%, experimental results reveal a substantial performance discrepancy compared to the macro F1-score of 37.94%. This critical gap indicates that while the model successfully identifies the majority classes, it fails to recognize rare disease categories with limited samples. Theoretical analysis suggests that while SVM handles high-dimensional feature spaces more effectively than RF and KNN, the deep features extracted are biased toward majority class characteristics. These findings highlight the severe limitations of accuracy-centric evaluation in agricultural diagnostics and demonstrate that deep feature extraction alone is insufficient to guarantee robust detection for minority pathologies. The study concludes that relying on aggregate accuracy can mask diagnostic failures, emphasizing the urgent need for per-class performance analysis and data-level mitigation strategies in future research.
Co-Authors Wayan Firdaus Mahmudy