Classification of banana stages using microwave spectroscopy by machine learning
Year: 2025
Type: article
Abstract: In the food industry, the need to assess and predict the ripening process of fruits plays an important role in optimizing storage and transportation strategies, ensuring the product quality when reaching consumers. The study proposes a method using microwave spectrum based on vector network analyzer combined with machine learning models. This method is used to evaluate among numerous machine learning models and predict the number of days required for an unripe banana to turn to semi ripe and then ripe banana. Data is collected through scattering parameters, including reflection parameter S11 and transmission parameter S21, in the frequency range from 1 GHz to 5 GHz. The S-parameters are processed, analyzed and extracted characteristic data and fed into the machine learning models to perform the comparison and prediction process.
Authors: Ja Lam Nguyen, Thi Hai Anh Nguyen, Vo Dang Khoa Trinh, Minh Huy Bao Bui, Thanh Ven Huynh, Thi Huyen Tran Nguyen, Hoang Minh Le, Anh Tu Tran
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: hybrid
OpenAlex ID: W4412118263