Fruit ripeness classification with few-shot learning

Ng, Hui-Fuang and Lo, Jie-Jin and Lin, Chih-Yang and Tan, Hung-Khoon and Chuah, Joon-Huang and Leung, Kar-Hang (2022) Fruit ripeness classification with few-shot learning. Lecture Notes in Electrical Engineering, 829 LN. 715 – 720. ISSN 1876-1100, DOI

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Deep learning based image classification systems require large amount of training data and long training time. However, the availability of large annotated image dataset is usually limited and expensive to generate, which limits a vision system to adapt to new task efficiently. In this paper, a few-shot classification framework is proposed which can adapt one fruit ripeness classification system to classify new types of fruits using only a few training samples. The proposed framework adopts the meta-learning paradigm where a base network learns to extract meta-features and few-shot classification tasks from the base classes with abundant training samples and then apply the network to similar task on the novel classes using only a few support samples. Experimental results indicate that the proposed framework is able to achieve over 75 ripeness classification accuracy on various fruits using a little as five samples. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Item Type: Article
Funders: None
Additional Information: Cited by: 2; Conference name: 11th International Conference on Robotics, Vision, Signal Processing and Power Applications, RoViSP 2021; Conference date: 5 April 2021 through 6 April 2021; Conference code: 272139
Uncontrolled Keywords: Deep learning; Fruits; Image classification; Large dataset; Sampling; Deep learning; Few-shot learning; Fruit ripeness; Image classification systems; Image datasets; Large amounts; Shot classification; Training data; Training sample; Training time; Classification (of information)
Subjects: S Agriculture > SB Plant culture
Divisions: Faculty of Engineering > Department of Electrical Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 19 Nov 2023 15:04
Last Modified: 03 Dec 2023 01:59

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