Identification of hickory nuts with different oxidation levels by integrating self-supervised and supervised learning

Frontiers in Sustainable Food Systems

Sustainable Food Systems

Jian Zheng¹  Lizhong Ding²  Haoyu Kang³  Dan Dai³  Zile Liang⁴  Siwei Chen⁴ 

[1] College of Food and Health, Zhejiang Agriculture and Forestry University, Hangzhou, China;Key Laboratory of Forestry Perception Technology and Intelligent Equipment of the State Forestry Administration, Hangzhou, China;Lin'an District Agricultural and Forestry Technology Extension Centre, Hangzhou, China;School of Mathematics and Computer Science, Zhejiang Agriculture and Forestry University, Hangzhou, China;School of Mathematics and Computer Science, Zhejiang Agriculture and Forestry University, Hangzhou, China;Key Laboratory of Forestry Perception Technology and Intelligent Equipment of the State Forestry Administration, Hangzhou, China;Zhejiang Key Laboratory of Forestry Intelligent Monitoring and Information Technology Research, Hangzhou, China;

关键词: hickory nuts;    oxidation levels;    image classification;    Masked autoencoders;    vision transformer;    self-supervised;    supervised;   

DOI  :  10.3389/fsufs.2023.1144998

 received in 2023-01-15, accepted in 2023-02-14,  发布年份 2023

来源: Frontiers

【 摘 要 】

The hickory (Carya cathayensis) nuts are considered as a traditional nut in Asia due to nutritional components such as phenols and steroids, amino acids and minerals, and especially high levels of unsaturated fatty acids. However, the edible quality of hickory nuts is rapidly deteriorated by oxidative rancidity. Deeper Masked autoencoders (DEEPMAE) with a unique structure for automatically extracting some features that could be scaleable from local to global for image classification, has been considered to be a state-of-the-art computer vision technique for grading tasks. This paper aims to present a novel and accurate method for grading hickory nuts with different oxidation levels. Owing to the use of self-supervised and supervised processes, this method is able to predict images of hickory nuts with different oxidation levels effectively, i.e., DEEPMAE can predict the oxidation level of nuts. The proposed DEEPMAE model was constructed from Vision Transformer (VIT) architecture which was followed by Masked autoencoders(MAE). This model was trained and tested on image datasets containing four classes, and the differences between these classes were mainly caused by varying levels of oxidation over time. The DEEPMAE model was able to achieve an overall classification accuracy of 96.14% on the validation set and 96.42% on the test set. The results on the suggested model demonstrated that the application of the DEEPMAE model might be a promising method for grading hickory nuts with different levels of oxidation.

【 授权许可】

Unknown   
Copyright © 2023 Kang, Dai, Zheng, Liang, Chen and Ding.