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Hybrid Feature-Driven Deep Autoencoder Framework for Enhanced Plant Disease Detection via Supervised Learning
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Keywords
Plant disease detection
Deep autoencoder
Hybrid feature extraction
Supervised classification
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Abstract
Plant disease detection is a critical challenge in modern agriculture, directly affecting crop yield, quality, and food security. Traditional visual inspection methods are time-consuming, subjective, and error-prone, necessitating the development of automated computational approaches. This study proposes a hybrid feature-driven deep autoencoder framework that combines deep learning-based image enhancement with hybrid feature extraction to improve supervised classification accuracy in plant disease detection. The framework leverages both color-texture descriptors and learned latent representations to effectively capture disease-specific patterns from leaf images. Empirical evaluations demonstrate superior performance compared to conventional machine learning and autoencoder-based methods, achieving higher classification accuracy, robustness against noise, and adaptability to multiple plant species. The proposed method addresses critical gaps in the literature, particularly in integrating handcrafted and deep features for automated plant disease recognition.
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