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Real-time banana freshness grading: A portable end-to-end detection system with high precision

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成果类型:
期刊论文
作者:
Liangyan Chen*;Junkang Zhu;Yisong Gui;Weihua Liu;Shan Zeng
通讯作者:
Liangyan Chen
作者机构:
[Liangyan Chen; Junkang Zhu; Yisong Gui; Weihua Liu] School of Electrical and Electronic Engineering, Wuhan Polytechnic University, P.O. Box: 430023, Wuhan, Hubei, China
[Shan Zeng] School of Mathematics and Computer Science, Wuhan Polytechnic University, P.O. Box: 430023, Wuhan, Hubei, China
通讯机构:
[Liangyan Chen] S
School of Electrical and Electronic Engineering, Wuhan Polytechnic University, P.O. Box: 430023, Wuhan, Hubei, China
语种:
英文
关键词:
AI;Fruit quality;Network algorithm;Non-destructive;YOLO
期刊:
Postharvest Biology and Technology
ISSN:
0925-5214
年:
2026
卷:
231
页码:
113904
机构署名:
本校为第一且通讯机构
院系归属:
电气与电子工程学院
数学与计算机学院
摘要:
To enhance the efficiency of banana quality management, an automated freshness grading system based on real-time image processing was developed. The system integrates a camera for image acquisition and employs portable edge devices (Nvidia Jetson Orin NX or Jetson TX2) to classify banana freshness into four levels, displaying results along with confidence scores in real-time. A lightweight YOLO-based banana freshness detection method (YOLO-BFD), built on the YOLOv7-tiny framework, was designed to enable high-accuracy, real-time processing on ed...

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