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An Efficient Automatic Fruit-360 Image Identification and Recognition Using a Novel Modified Cascaded-ANFIS

Namal Rathnayake1, Upaka Rathnayake2, Tuan Linh Dang3

  • 1School of Systems Engineering, Kochi University of Technology, 185 Miyanokuchi, Tosayamada, Kami 782-8502, Kochi, Japan.

Sensors (Basel, Switzerland)
|June 24, 2022
PubMed
Summary

This study introduces a novel Cascaded Adaptive Network-based Fuzzy Inference System (Cascaded-ANFIS) for comprehensive automated fruit identification. The new algorithm achieves 98.36% accuracy on the full Fruit-360 dataset, outperforming existing methods.

Keywords:
Fruit-360 datasetautomated image classificationcascaded-ANFISconfusion matrixfeatures descriptors

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Automated fruit identification is complex due to variations in fruit types and subtypes, often location-dependent.
  • Previous studies using Convolutional Neural Network (CNN) algorithms like VGG16, Inception V3, MobileNet, and ResNet18 have not addressed the entire Fruit-360 dataset of 131 classes.
  • Existing CNN models often lack computational efficiency for comprehensive fruit classification tasks.

Purpose of the Study:

  • To present a novel, robust, and comprehensive study for identifying and predicting all 131 fruit classes within the Fruit-360 dataset.
  • To address the limitations of previous studies by utilizing a complete dataset and improving computational efficiency.
  • To introduce and evaluate the effectiveness of the Cascaded Adaptive Network-based Fuzzy Inference System (Cascaded-ANFIS) for fruit identification.

Main Methods:

  • Utilized the complete Fruit-360 dataset comprising 90,483 sample images across 131 fruit classes.
  • Employed a Cascaded Adaptive Network-based Fuzzy Inference System (Cascaded-ANFIS) as the core identification algorithm.
  • Integrated multiple feature descriptors including 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.

Main Results:

  • Achieved a relative accuracy of 98.36% on the comprehensive Fruit-360 dataset.
  • Calculated weighted precision, recall, and F-score as 0.9843, 0.9841, and 0.9840, respectively, addressing the dataset's imbalance.
  • Demonstrated superior performance and high computational efficiency compared to state-of-the-art algorithms in comparative studies.

Conclusions:

  • The proposed Cascaded-ANFIS algorithm effectively handles the entire Fruit-360 dataset, offering a robust solution for automated fruit identification.
  • The developed system provides high accuracy and computational efficiency, surpassing existing methods.
  • This study establishes a new benchmark for comprehensive fruit classification using advanced fuzzy inference systems.