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Related Experiment Video

Updated: Jun 17, 2025

Fruit Volatile Analysis Using an Electronic Nose

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Published on: March 30, 2012

A lightweight Color-changing melon ripeness detection algorithm based on model pruning and knowledge distillation:

Guojun Chen¹, Yongjie Hou¹, Haozhen Chen¹

¹Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China.

Frontiers in Plant Science

|August 7, 2024

Summary

This summary is machine-generated.

This study introduces an improved YOLOv8s network for efficient color-changing melon detection, enhancing harvesting. The optimized model significantly reduces size and computational cost while maintaining high accuracy.

Keywords:

Color-changing melon YOLOv8s knowledge distillation model pruning multi-scale feature fusion

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Area of Science:

Agricultural Technology
Computer Vision
Machine Learning

Background:

Color-changing melons offer dual ornamental and food value.
Efficient harvesting of these melons is crucial for agricultural productivity.
Current detection models face challenges in deployment cost and efficiency on agricultural equipment.

Purpose of the Study:

To develop an efficient and lightweight object detection model for color-changing melon harvesting.
To improve the accuracy and speed of detecting color-changing melons at various maturity stages.
To reduce the computational and storage requirements of detection models for agricultural equipment.

Main Methods:

An improved YOLOv8s network incorporating Dilated Wise Residual (DWR) and Dilated Reparam Block (DRB) for enhanced feature fusion.
A multilevel scale fusion feature pyramid network (HS-PAN) was designed to enrich semantic and localization information.
Model pruning (Layer-Adaptive Sparsity Pruning) and knowledge distillation (Block-Correlation Knowledge Distillation) were employed for model simplification and accuracy recovery.

Main Results:

The improved model achieved a mean Average Precision (mAP0.5) of 96.1% on the color-changing melon dataset.
Detection speed increased by 9.1% compared to the standard YOLOv8s.
Model parameters reduced from 6.47M to 1.14M, FLOPs from 22.8G to 7.5G, and model size from 12.64MB to 2.47MB.

Conclusions:

The proposed enhanced YOLOv8s network significantly improves detection efficiency and reduces model complexity for color-changing melons.
The method demonstrates superior performance compared to other lightweight networks in complex scenarios.
This research provides a strong technical foundation for the automatic picking of color-changing melons.