A CNN-Based Microwave Imaging System for Detecting Watermelon Ripeness

A CNN-Based Microwave Imaging System for Detecting Watermelon Ripeness

The integration of a non-invasive microwave imaging system with a machine learning algorithm could improve food quality and food safety. In this paper, a S- and C-band microwave imaging system that utilizes DAS (Delay and Sum) beamforming with an automated high-frequency switching network is built t...

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Bibliographic Details
Main Authors: Zachary M. Choffin, Lingyan Kong, Yu Gan, Nathan Jeong
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Microwave imaging system
convolutional neural network
watermelon ripeness
food quality
Brix sugar content
Online Access:https://ieeexplore.ieee.org/document/10856151/
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https://ieeexplore.ieee.org/document/10856151/

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