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Fruit Leaf Diseases Classification: A Hierarchical Deep Learning Framework

计算机、材料和连续体（英文） 2023年第4期75卷 1179-1194页

作者： Samra Rehman Muhammad Attique Khan Majed Alhaisoni Ammar Armghan Fayadh Alenezi Abdullah Alqahtani Khean Vesal Yunyoung Nam Department of Computer Science HITEC UniversityTaxila47080Pakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySakakah72311Saudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj16242Saudi Arabia Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea

Manual inspection of fruit diseases is a time-consuming and costly because it is based on naked-eye *** authors present computer vision techniques for detecting and classifying fruit leaf *** of computer vision techniques are preprocessing original images for visualization of infected regions,feature extraction from raw or segmented images,feature fusion,feature selection,and *** following are the major challenges identified by researchers in the literature:(i)lowcontrast infected regions extract irrelevant and redundant information,which misleads classification accuracy;(ii)irrelevant and redundant information may increase computational time and reduce the designed model’s *** paper proposed a framework for fruit leaf disease classification based on deep hierarchical learning and best feature *** the proposed framework,contrast is first improved using a hybrid approach,and then data augmentation is used to solve the problem of an imbalanced *** next step is to use a pre-trained deep model named Darknet53 and fine-tune ***,deep transfer learning-based training is carried out,and features are extracted using an activation function on the average pooling ***,an improved butterfly optimization algorithm is proposed,which selects the best features for classification using machine learning *** experiment was carried out on augmented and original fruit datasets,yielding a maximum accuracy of 99.6%for apple diseases,99.6%for grapes,99.9%for peach diseases,and 100%for cherry *** overall average achieved accuracy is 99.7%,higher than previous techniques.

关键词： Fruit diseases data augmentation contrast enhancement deep learning improved butterfly optimization

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