Mahmood A. Mahmood1,2,*, Khalaf Alsalem1
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3431-3448, 2024, DOI:10.32604/cmc.2024.047604
Abstract Olive trees are susceptible to a variety of diseases that can cause significant crop damage and economic losses. Early detection of these diseases is essential for effective management. We propose a novel transformed wavelet, feature-fused, pre-trained deep learning model for detecting olive leaf diseases. The proposed model combines wavelet transforms with pre-trained deep-learning models to extract discriminative features from olive leaf images. The model has four main phases: preprocessing using data augmentation, three-level wavelet transformation, learning using pre-trained deep learning models, and a fused deep learning model. In the preprocessing phase, the image dataset is augmented using techniques such as… More >
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