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EasyDAM_V2: Efficient Data Labeling Method for Multishape, Cross-Species Fruit Detection.

Wenli Zhang¹, Kaizhen Chen¹, Chao Zheng¹

¹Information Department, Beijing University of Technology, Beijing 100022, China.

Plant Phenomics (Washington, D.C.)

|October 7, 2022

Summary

This summary is machine-generated.

This study introduces EasyDAM_V2, an improved data labeling method for fruit detection. It effectively transfers labels across different fruit species, significantly reducing costs for smart orchard applications.

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Area of Science:

Computer Vision
Machine Learning
Agricultural Technology

Background:

Deep learning fruit detection models require costly labeled datasets.
Previous methods using GANs and pseudolabeling had limitations in feature transfer and accuracy.

Purpose of the Study:

To propose an improved data labeling method (EasyDAM_V2) for multishape and cross-species fruit detection.
To reduce the high costs associated with manual data labeling in smart orchards.

Main Methods:

Developed Across-CycleGAN for image translation between source and target domains, accommodating shape differences.
Implemented a pseudolabel adaptive threshold selection strategy for dynamic threshold adjustment and pseudolabel updating.
Evaluated the method using an orange dataset (source) and pitaya/mango datasets (target).

Main Results:

Achieved average labeling precision of 82.1% for pitaya and 85.0% for mango.
Demonstrated successful label transfer across different fruit species, even with partial shape variations.

Conclusions:

EasyDAM_V2 effectively reduces data labeling costs for cross-species fruit detection.
The model shows promise for enhancing the efficiency and applicability of smart orchard technologies.