Deep autoencoder based image enhancement approach with hybrid feature extraction for plant disease detection using supervised classification

Abstract

Plant leaf diseases pose significant threats to global agriculture, leading to reduced crop yields and economic losses. Rapid and accurate disease detection is essential for timely interventions and sustainable farming practices. This study presents an innovative approach for plant leaf disease detection by integrating wavelet analysis, color, and texture features, coupled with autoencoder denoising and support vector machine (SVM) classification. Wavelet analysis is employed to extract multi-resolution features, capturing intricate details at different scales. Furthermore, color and texture characteristics are extracted to encompass a broad spectrum of visual information crucial for distinguishing diseases. The Autoencoder model helps to enhance the feature representation that mitigates the impact of noise and irrelevant data. The SVM classifier is utilized to learn complex patterns and accurately classify different disease classes. The combined model of wavelet, color, and texture attributes, in combination with autoencoder denoising and SVM classification, markedly enhances the precision and efficiency of disease detection in contrast to conventional methods. The system's performance is evaluated using a PlantVillage dataset, showcasing its adaptability to different plant species and disease types. The overall performance is obtained as 98.60%, 97.25%, 96.89%, and 97.20% in terms of accuracy, precision, recall, and F-Score, respectively.