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Article|01 Jun 2021|OPEN
Easy domain adaptation method for filling the species gap in deep learning-based fruit detection
Kaizhen Chen1, Jiaqi Wang1, Yun Shi2, Wenli Zhang1, & Wei Guo3
1Information Department, Beijing University of Technology, Beijing 100022, China
2Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
3International Field Phenomics Research Laboratory, Institute for Sustainable Agro-Ecosystem Services, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 188-0002, Japan

Horticulture Research 8,
Article number: 119 (2021)
doi: 10.1038/hortres.2021.119
Views: 172

Received: 14 Nov 2020
Revised: 16 Feb 2021
Accepted: 26 Mar 2021
Published online: 01 Jun 2021


Fruit detection and counting are essential tasks for horticulture research. With computer vision technology development, fruit detection techniques based on deep learning have been widely used in modern orchards. However, most deep learning-based fruit detection models are generated based on fully supervised approaches, which means a model trained with one domain species may not be transferred to another. There is always a need to recreate and label the relevant training dataset, but such a procedure is time-consuming and labor-intensive. This paper proposed a domain adaptation method that can transfer an existing model trained from one domain to a new domain without extra manual labeling. The method includes three main steps: transform the source fruit image (with labeled information) into the target fruit image (without labeled information) through the CycleGAN network; Automatically label the target fruit image by a pseudo-label process; Improve the labeling accuracy by a pseudo-label self-learning approach. Use a labeled orange image dataset as the source domain, unlabeled apple and tomato image dataset as the target domain, the performance of the proposed method from the perspective of fruit detection has been evaluated. Without manual labeling for target domain image, the mean average precision reached 87.5% for apple detection and 76.9% for tomato detection, which shows that the proposed method can potentially fill the species gap in deep learning-based fruit detection.