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An Integrated Deep Learning Framework for Fruits Diseases Classification

Computers, Materials & Continua 2022年第4期71卷 1387-1402页

作者： Abdul Majid muhammad Attique Khan Majed Alhaisoni muhammad asfand eyar Usman Tariq Nazar Hussain Yunyoung Nam Seifedine Kadry Department of Computer Science HITEC University TaxilaTaxilaPakistan College of Computer Science and Engineering University of Ha’ilHa’ilSaudi Arabia Department of Computer Science Bahria UniversityIslamabad College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway

:Agriculture has been an important research area in the field of image processing for the last five *** affect the quality and quantity of fruits,thereby disrupting the economy of a *** computerized techniques have been introduced for detecting and recognizing fruit ***,some issues remain to be addressed,such as irrelevant features and the dimensionality of feature vectors,which increase the computational time of the ***,we propose an integrated deep learning framework for classifying fruit *** consider seven types of fruits,i.e.,apple,cherry,blueberry,grapes,peach,citrus,and *** proposed method comprises several important ***,data increase is applied,and then two different types of features are *** the first feature type,texture and color features,i.e.,classical features,are *** the second type,deep learning characteristics are extracted using a pretrained *** pretrained model is reused through transfer ***,both types of features are merged using the maximum mean value of the serial ***,the resulting fused vector is optimized using a harmonic threshold-based genetic ***,the selected features are classified using multiple *** evaluation is performed on the PlantVillage dataset,and an accuracy of 99%is achieved.A comparison with recent techniques indicate the superiority of the proposed method.

关键词： Fruit diseases data augmentation deep learning classical features features fusion features selection

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Citrus Diseases Recognition Using Deep Improved Genetic Algorithm

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Computers, Materials & Continua 2022年第5期71卷 3667-3684页

作者： Usra Yasmeen muhammad Attique Khan Usman Tariq Junaid Ali Khan muhammad asfand eyar ChAvais Hanif Senghour Mey Yunyoung Nam Department of Computer Science HITEC University TaxilaTaxilaPakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Computer Science Bahria UniversityIslamabadPakistan Department of EE HITEC University TaxilaTaxilaPakistan Department of ICT Convergence Soonchunhyang UniversityKorea Department of Computer Science and Engineering Soonchunhyang UniversityKorea

Agriculture is the backbone of each country,and almost 50%of the population is directly involved in *** Pakistan,several kinds of fruits are produced and exported the other *** is an important fruit,and its production in Pakistan is higher than the other ***,the diseases of citrus fruits such as canker,citrus scab,blight,and a few more impact the quality and quantity of this *** manual diagnosis of these diseases required an expert person who is always a time-consuming and costly *** the agriculture sector,deep learning showing significant success in the last five *** research work proposes an automated framework using deep learning and best feature selection for citrus diseases *** the proposed framework,the augmentation technique is applied initially by creating more training data from existing *** were then modifying the two pre-trained models named Resnet18 and Inception *** modified models are trained using an augmented dataset through transfer *** are extracted for each model,which is further selected using Improved Genetic Algorithm(ImGA).The selected features of both models are fused using an array-based approach that is finally classified using supervised learning classifiers such as Support Vector Machine(SVM)and name a few *** experimental process is conducted on three different datasets-Citrus Hybrid,Citrus Leaf,and Citrus *** these datasets,the best-achieved accuracy is 99.5%,94%,and 97.7%,*** proposed framework is evaluated on each step and compared with some recent techniques,showing that the proposed method shows improved performance.

关键词： Citrus diseases data augmentation deep learning features selection features fusion

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