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Fruit Detection and Segmentation for AppleHarvesting Using Visual Sensor in Orchards.

Hanwen Kang¹, Chao Chen²

¹Laboratory of Motion Generation and Analysis, Faculty of Engineering, Monash University, Clayton, VIC 3800,Australia. hanwen.kang@monash.edu.

Sensors (Basel, Switzerland)

|October 27, 2019

Summary

This study introduces a multi-function network for real-time apple and branch detection and semantic segmentation in orchards. The developed system enhances autonomous harvesting vision systems with improved accuracy and efficiency.

Keywords:

automated harvesting robot deep learning machine vision real-time fruit detection semantic segmentation visual sensor

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Area of Science:

Computer Vision
Agricultural Technology
Robotics

Background:

Autonomous harvesting is crucial for agriculture's future.
Vision systems are key but challenging components in autonomous harvesting.
Accurate real-time detection and segmentation of crops and obstacles are essential for robotic systems.

Purpose of the Study:

To develop a multi-function network for real-time detection and semantic segmentation of apples and branches in orchard environments.
To enhance feature extraction capabilities for improved accuracy.
To optimize the network for real-time computation efficiency.

Main Methods:

Utilized atrous spatial pyramid pooling and gate feature pyramid network for enhanced feature extraction.
Developed a lightweight backbone network based on residual network architecture for real-time performance.
Implemented a multi-function network for simultaneous detection and semantic segmentation tasks.

Main Results:

The network with a ResNet-101 backbone achieved an F1 score of 0.832 for apple detection and high accuracy for apple/branch segmentation (87.6%/77.2%).
A lightweight backbone network demonstrated superior computation efficiency (12.8 M weights, 32 ms computation time).
The lightweight model achieved an F1 score of 0.827 for apple detection and 86.5%/75.7% for apple/branch segmentation.

Conclusions:

The developed detection and segmentation network effectively performs real-time analysis of apples and branches in orchards.
The lightweight backbone network offers a practical solution for efficient real-time autonomous harvesting vision systems.
The proposed multi-function network significantly contributes to advancing autonomous harvesting technologies.