Quality detection and storage and preservation of black diamond apples in Linzhi, Tibet based on hyperspectral and deep learning approach-Reference-Cited by-同舟云学术

Quality detection and storage and preservation of black diamond apples in Linzhi, Tibet based on hyperspectral and deep learning approach

Published:2024-01-01 Issue:1 Volume:9 Page:
ISSN:2444-8656
Container-title:Applied Mathematics and Nonlinear Sciences
language:en
Short-container-title:

Author:

Xu Dandan¹²,Yuan Chao²,Quan Hong³

Affiliation:

1. School of Foundational Education , Xizang agriculture and Animal Husbandry University , Nyingchi , Tibet , , China .

2. College of Electrical Engineering , Xizang Agricultural and Animal Husbandry University , Nyingchi , Tibet , , China .

3. Collaborative Innovation Center for R&D in Xizang Characteristic Agricultural and Animal Husbandry Resources, Ministry of Education , Xizang Agriculture and Animal Husbandry University , Nyingchi , Tibet , , China .

Abstract

Abstract Apples are abundant in essential nutrients, including a variety of minerals and vitamins, and are readily digestible and assimilated by the human body. Commonly consumed worldwide, apples are particularly diverse in their varieties. The “Black Diamond” apple from Linzhi, Tibet, stands out as a significant fresh variety. This study utilizes hyperspectral imaging and deep learning techniques to investigate the impact of room temperature storage on the quality of Linzhi “Black Diamond” apples. Specifically, we assess alterations in quality indices such as hardness, weight loss, sugar-acid ratio, as well as the content of phenolics ketones, and volatile aromatic compounds. Our findings provide detailed insights into the effects of ambient storage conditions on the sensory and nutritional quality of these distinctive apples.

Publisher

Walter de Gruyter GmbH

Link

https://www.sciendo.com/pdf/10.2478/amns-2024-1940

Reference20 articles.

1. Haiyan, Fan, Yunjin, Chen, Yulan, & Guo,(2017). Hyperspectral image restoration using low-rank tensor recovery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(10), 4589-4604.

2. Crevoisier, C., Nobileau, D., Fiore, A. M., Armante, R., A Chédin, & Scott, N. A. (2017). Tropospheric methane in the tropics – first year from iasi hyperspectral infrared observations. Atmospheric Chemistry & Physics Discussions, 9(17), 6337-6350.

3. Chen, Y., Jiang, H., Li, C., Jia, X., & Ghamisi, P. (2016). Deep feature extraction and classification of hyperspectral images based on CNNs. IEEE Transactions on Geoscience & Remote Sensing, 54(10), 6232-6251.

4. Lee, C. M., Cable, M. L., Hook, S. J., Green, R. O., Ustin, S. L., & Mandl, D. J.,(2015). An introduction to the nasa hyperspectral infrared imager (hyspiri) mission and preparatory activities. Remote Sensing of Environment, 6-19.

5. A, H. A., B, A. B., A, A. B., & A, G. B. (2015). Generating 3d hyperspectral information with lightweight uav snapshot cameras for vegetation monitoring: from camera calibration to quality assurance. ISPRS Journal of Photogrammetry and Remote Sensing, 108(5), 245-259.