Amani, M. A. and Aghamohammadi, N. (2024) A novel technology to monitor effects of ethylene on the food products' supply chain: a deep learning approach. International Journal of Environmental Science and Technology, 21 (5). pp. 5007-5018. ISSN 1735-1472, DOI https://doi.org/10.1007/s13762-023-05328-3.
Full text not available from this repository.Abstract
Industry 4.0 recommends utilizing novel technologies such as artificial intelligence (AI) for automating industrial operations, and the sustainable development goals express targets to decrease food losses during the supply chain, which both lead to increased productivity. One of the situations in which AI methods such as deep learning can be used is the warehousing step, where before storing the fruits, the rotten fruits need to be separated from healthy products due to intensified ethylene from rotten products, which leads to spoiling of fresh fruits. Therefore, to automate this process and prevent the old phrase ``one rotten apple spoils the whole barrel'' from happening, a classification system was built by a hybrid of the deep convolutional neural network and particle swarm optimization algorithms with 99.76% precision, which distinguish six different types of perishable products such as apple, banana, bitter gourd, tomato, capsicum, and orange from their rotten ones.
| Item Type: | Article |
|---|---|
| Funders: | University of Tehran |
| Uncontrolled Keywords: | Deep learning; Image classification; Industry 4.0; Supply chain management |
| Subjects: | Q Science > Q Science (General) S Agriculture > S Agriculture (General) T Technology > T Technology (General) |
| Divisions: | Faculty of Medicine > Social & Preventive Medicine |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 14 Nov 2024 06:23 |
| Last Modified: | 14 Nov 2024 06:23 |
| URI: | http://eprints.um.edu.my/id/eprint/45929 |
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