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Cherry-Net: real-time segmentation algorithm of cherry maturity based on improved PIDNet.

Jie Cui^1,2,3, Lilian Zhang^1,2,3, Lutao Gao^1,2,3

¹College of Big Data, Yunnan Agricultural University, Kunming, China.

Frontiers in Plant Science

|September 9, 2025

Summary

This summary is machine-generated.

This study introduces an efficient machine vision model for cherry picking robots, improving cherry maturity and contour detection in real-world orchards. The developed lightweight model achieves high accuracy and speed for intelligent harvesting systems.

Keywords:

cherry lightweight segmentation model real-time semantic segmentation ripeness identification smart agricultural

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Area of Science:

Agricultural Robotics
Computer Vision
Machine Learning

Background:

Accurate cherry maturity identification and contour detection are crucial for robotic harvesting.
Challenges include occlusion, lighting variations, and blurriness in natural orchard environments.

Purpose of the Study:

To develop a lightweight, accurate, and efficient real-time semantic segmentation model for cherry picking robots.
To enhance robustness in challenging agricultural conditions.

Main Methods:

Utilized a modified PIDNet framework with a lightweight SwiftFormer-XS backbone.
Introduced novel SwiftRep-Hybrid and Light Fusion Enhance (LFE) modules for improved feature representation.
Implemented a post-processing module for refined class determination and maturity visualization.

Main Results:

Achieved a mean Intersection over Union (MIoU) of 72.2% and pixel accuracy (PA) of 99.82%.
Outperformed state-of-the-art real-time segmentation models.
Demonstrated competitive inference speed and accuracy on an embedded Jetson TX2 platform.

Conclusions:

The proposed model offers a robust and efficient solution for cherry maturity recognition and contour detection.
It shows strong potential for deployment in intelligent harvesting systems and precision agriculture.
Contributes to the advancement of automated agricultural technologies.