Olive Leaf Disease Detection via Wavelet Transform and Feature Fusion of Pre-Trained Deep Learning Models  

作　　者：Mahmood A.Mahmood Khalaf Alsalem 

机构地区：[1]Department of Information Systems,College of Computer and Information Sciences,Jouf University,Sakakah,Kingdom of Saudi Arabia [2]Department of Information Systems and Technology,Faculty of Graduate Studies and Research,Cairo University,Giza,Egypt

出　　处：《Computers, Materials & Continua》2024年第3期3431-3448,共18页计算机、材料和连续体（英文）

摘　　要：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 resizing,rescaling,flipping,rotation,zooming,and contrasting.In wavelet transformation,the augmented images are decomposed into three frequency levels.Three pre-trained deep learning models,EfficientNet-B7,DenseNet-201,and ResNet-152-V2,are used in the learning phase.The models were trained using the approximate images of the third-level sub-band of the wavelet transform.In the fused phase,the fused model consists of a merge layer,three dense layers,and two dropout layers.The proposed model was evaluated using a dataset of images of healthy and infected olive leaves.It achieved an accuracy of 99.72%in the diagnosis of olive leaf diseases,which exceeds the accuracy of other methods reported in the literature.This finding suggests that our proposed method is a promising tool for the early detection of olive leaf diseases.

关 键 词：Olive leaf diseases wavelet transform deep learning feature fusion 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]