Detection of Enhanced Apple Leaf Disease Using Fused Deep Features from Pre-trained CNNs
A comprehensive examination of the application of pre-trained Convolutional Neural Networks(CNNs)was discussed,such as GoogLeNet,VGGNet and EfficientNet in detecting apple leaf diseases and pests.By addressing the limitations and gaps in existing research,we focused on enhancing detection accuracy by leveraging deep features extracted from these CNN models.The methodology involved the fu-sion of deep features obtained from the final fully connected layers of the CNNs,followed by the training of a Support Vector Machine(SVM)classifier.Results showed that all the CNN models demonstrated significant accuracy in detecting apple leaf diseases using deep feature extrac-tion,achieving an overall classification accuracy of 99.42%.Furthermore,an improved deep learning approach was introduced which com-bined the deep features from the three CNN models,further boosting predictive performance.The methodology exhibited promising results in apple leaf disease detection and had potential applications in detecting diseases in other plant leaves.This research contributed to the develop-ment of automated and precise plant disease identification techniques,paving the way for intelligent and targeted agricultural production.
Apple leaf diseasesConvolutional Neural Networks(CNNs)Deep feature extractionSupport Vector Machine(SVM)Disease detection
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