Fruit Ripeness Identification Using Transformers

Xiao, Bingjie; Nguyen, Minh; Yan, Wei Qi

Fruit Ripeness Identification Using Transformers

Files

Journal article(1.11 MB)

Date

2023-06-29

Authors

Xiao, Bingjie

Nguyen, Minh

Yan, Wei Qi

Item type

Journal Article

Publisher

Springer Science and Business Media LLC

Abstract

Pattern classification has always been essential in computer vision. Transformer paradigm having attention mechanism with global receptive field in computer vision improves the efficiency and effectiveness of visual object detection and recognition. The primary purpose of this article is to achieve the accurate ripeness classification of various types of fruits. We create fruit datasets to train, test, and evaluate multiple Transformer models. Transformers are fundamentally composed of encoding and decoding procedures. The encoder is to stack the blocks, like convolutional neural networks (CNN or ConvNet). Vision Transformer (ViT), Swin Transformer, and multilayer perceptron (MLP) are considered in this paper. We examine the advantages of these three models for accurately analyzing fruit ripeness. We find that Swin Transformer achieves more significant outcomes than ViT Transformer for both pears and apples from our dataset.