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 Determination of Fruit Freshness Using Near-Infrared Spectroscopy and Machine Learning Techniques
Tác giả hoặc Nhóm tác giả: Duy Khanh Ninh, Kha Duy Phan, Chi Khanh Ninh, Nhan Le Thanh
Nơi đăng: International Conference on Intelligent Systems & Networks (ICISN) 2022, In: Anh, N.L., Koh, SJ., Nguyen, T.D.L., Lloret, J., Nguyen, T.T. (eds) Intelligent Systems and Networks. Lecture Notes in Networks and Systems, vol. 471, Springer, Singapore.; Số: 471;Từ->đến trang: 455–464;Năm: 2022
Lĩnh vực: Công nghệ thông tin; Loại: Báo cáo; Thể loại: Quốc tế
TÓM TẮT
This paper addresses the problem of fruit freshness categorization in the context of fruit quality assessment during short storage periods. As it is hard to handle by using computer vision technology, we propose a novel method by using absorbance near-infrared spectroscopy combined with machine learning (ML) techniques. We collected several samples of five popular fruits with various properties and classified them into three degrees of freshness based on the storage duration. We then examined multiple combinations of feature extraction and machine learning techniques. Experimental results show that the proposed Convolutional Neural Network (CNN) architecture were superior to other traditional ML models regardless of the selected feature vector. In particular, the proposed CNN when trained on the concatenated first and second derivatives of the pre-processed absorbance spectrum achieved the highest accuracy of 80.0%. The obtained classification performance was evaluated on a variety of fruits, which shows the potential of our approach.
ABSTRACT
This paper addresses the problem of fruit freshness categorization in the context of fruit quality assessment during short storage periods. As it is hard to handle by using computer vision technology, we propose a novel method by using absorbance near-infrared spectroscopy combined with machine learning (ML) techniques. We collected several samples of five popular fruits with various properties and classified them into three degrees of freshness based on the storage duration. We then examined multiple combinations of feature extraction and machine learning techniques. Experimental results show that the proposed Convolutional Neural Network (CNN) architecture were superior to other traditional ML models regardless of the selected feature vector. In particular, the proposed CNN when trained on the concatenated first and second derivatives of the pre-processed absorbance spectrum achieved the highest accuracy of 80.0%. The obtained classification performance was evaluated on a variety of fruits, which shows the potential of our approach.
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