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Related Experiment Video

Updated: May 27, 2025

Fruit Volatile Analysis Using an Electronic Nose

Fruit Volatile Analysis Using an Electronic Nose

Published on: March 30, 2012

A new maturity recognition algorithm for Xinhui citrus based on improved YOLOv8.

Fuqin Deng¹, Zhenghong He¹, Lanhui Fu¹

¹School of Electronic and Information Engineering, the Wuyi University, Jiangmen, China.

Frontiers in Plant Science

|February 21, 2025

Summary

This study enhances citrus maturity detection using an improved YOLOv8 model, achieving higher accuracy and efficiency for automated harvesting. The new method boosts precision and recall, reducing fruit waste in orchards.

Keywords:

CARAFE lightweight operator GhostConv XinHui citrus YOLOv8 maturity detection multi-dimensional collaborative attention mechanism (MCA)object detection

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Area of Science:

Agricultural Engineering
Computer Vision
Machine Learning

Background:

Current object detection models struggle with accurate citrus maturity color identification.
Improved feature extraction is crucial for reducing waste in automated harvesting due to incorrect maturity evaluations.

Purpose of the Study:

To develop an enhanced YOLOv8-based object detection model for accurate Xinhui citrus maturity detection.
To improve automated harvesting systems by providing reliable maturity assessment.

Main Methods:

Implemented GhostConv to reduce parameters and enhance detection accuracy in the YOLOv8 Head.
Integrated CARAFE (Content-Aware Reassembly of Features) upsampling for detailed feature retention.
Introduced the MCA (Multidimensional Collaborative Attention) mechanism for improved local feature interaction and extraction.

Main Results:

The improved YOLOv8 achieved a precision of 88.6%, recall of 93.1%, and average precision of 93.4%.
Significant improvements over the original model: +16.5% precision, +20.2% recall, +14.7% average precision.
Reduced model parameter volume by 0.57% while enhancing performance.

Conclusions:

The proposed YOLOv8 enhancement effectively improves Xinhui citrus maturity detection accuracy in complex orchard environments.
This advancement supports the development of more efficient and less wasteful automated fruit-picking systems.