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...
Saved in:
Main Authors: | Zachary M. Choffin, Lingyan Kong, Yu Gan, Nathan Jeong |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2025-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10856151/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Genetic diversity of soybean cultivars belonging to different ripeness groups with regard to performance and quality
by: A. I. Abugaliyeva, et al.
Published: (2016-08-01) -
Powdery Mildew on Watermelon in Florida
by: Pamela D. Roberts, et al.
Published: (2019-01-01) -
Powdery Mildew on Watermelon in Florida
by: Pamela D. Roberts, et al.
Published: (2019-01-01) -
A Review of Watermelon Production and Price Trends from 2010 to 2021
by: Tara Wade, et al.
Published: (2023-06-01) -
Beekeeping: Watermelon Pollination
by: Malcolm T. Sanford, et al.
Published: (2009-11-01)