Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/2804
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dc.contributor.authorRathnayake, N-
dc.contributor.authorRathnayake, U-
dc.contributor.authorDang, T. L-
dc.contributor.authorHoshino, Y-
dc.date.accessioned2022-07-19T06:56:21Z-
dc.date.available2022-07-19T06:56:21Z-
dc.date.issued2022-06-10-
dc.identifier.citationRathnayake, Namal, Upaka Rathnayake, Tuan Linh Dang, and Yukinobu Hoshino. 2022. "An Efficient Automatic Fruit-360 Image Identification and Recognition Using a Novel Modified Cascaded-ANFIS Algorithm" Sensors 22, no. 12: 4401. https://doi.org/10.3390/s22124401en_US
dc.identifier.issn1424-8220-
dc.identifier.urihttp://rda.sliit.lk/handle/123456789/2804-
dc.description.abstractAutomated fruit identification is always challenging due to its complex nature. Usually, the fruit types and sub-types are location-dependent; thus, manual fruit categorization is also still a challenging problem. Literature showcases several recent studies incorporating the Convolutional Neural Network-based algorithms (VGG16, Inception V3, MobileNet, and ResNet18) to classify the Fruit-360 dataset. However, none of them are comprehensive and have not been utilized for the total 131 fruit classes. In addition, the computational efficiency was not the best in these models. A novel, robust but comprehensive study is presented here in identifying and predicting the whole Fruit-360 dataset, including 131 fruit classes with 90,483 sample images. An algorithm based on the Cascaded Adaptive Network-based Fuzzy Inference System (Cascaded-ANFIS) was effectively utilized to achieve the research gap. Color Structure, Region Shape, Edge Histogram, Column Layout, Gray-Level Co-Occurrence Matrix, Scale-Invariant Feature Transform, Speeded Up Robust Features, Histogram of Oriented Gradients, and Oriented FAST and rotated BRIEF features are used in this study as the features descriptors in identifying fruit images. The algorithm was validated using two methods: iterations and confusion matrix. The results showcase that the proposed method gives a relative accuracy of 98.36%. The Fruit-360 dataset is unbalanced; therefore, the weighted precision, recall, and FScore were calculated as 0.9843, 0.9841, and 0.9840, respectively. In addition, the developed system was tested and compared against the literature-found state-of-the-art algorithms for the purpose. Comparison studies present the acceptability of the newly developed algorithm handling the whole Fruit-360 dataset and achieving high computational efficiency.en_US
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.relation.ispartofseriesSensors 2022,;Volume 22 Issue 12-
dc.subjectautomated image classificationen_US
dc.subjectcascaded-ANFISen_US
dc.subjectconfusion matrixen_US
dc.subjectfeatures descriptorsen_US
dc.subjectFruit-360 dataseten_US
dc.titleAn Efficient Automatic Fruit-360 Image Identification and Recognition Using a Novel Modified Cascaded-ANFIS Algorithmen_US
dc.typeArticleen_US
dc.identifier.doi10.3390/s22124401en_US
Appears in Collections:Research Papers - Department of Civil Engineering
Research Papers - Open Access Research
Research Papers - SLIIT Staff Publications

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