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. 2024 Dec 6:15:1423338.
doi: 10.3389/fpls.2024.1423338. eCollection 2024.

Research progress and prospect of key technologies of fruit target recognition for robotic fruit picking

Shaohua Liu  1 , Jinlin Xue  1 , Tianyu Zhang  1 , Pengfei Lv  1 , Huanhuan Qin  2 , Tianxing Zhao  1
Affiliations

Research progress and prospect of key technologies of fruit target recognition for robotic fruit picking

Shaohua Liu et al. Front Plant Sci. .

Abstract

It is crucial for robotic picking fruit to recognize fruit accurately in orchards, this paper reviews the applications and research results of target recognition in orchard fruit picking by using machine vision and emphasizes two methods of fruit recognition: the traditional digital image processing method and the target recognition method based on deep learning. Here, we outline the research achievements and progress of traditional digital image processing methods by the researchers aiming at different disturbance factors in orchards and summarize the shortcomings of traditional digital image processing methods. Then, we focus on the relevant contents of fruit target recognition methods based on deep learning, including the target recognition process, the preparation and classification of the dataset, and the research results of target recognition algorithms in classification, detection, segmentation, and compression acceleration of target recognition network models. Additionally, we summarize the shortcomings of current orchard fruit target recognition tasks from the perspectives of datasets, model applicability, universality of application scenarios, difficulty of recognition tasks, and stability of various algorithms, and look forward to the future development of orchard fruit target recognition.

Keywords: deep learning; fruit; machine vision; robotic picking; target recognition.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The main picking method in the market at this stage.
Figure 2
Figure 2
Orchard interference factors.
Figure 3
Figure 3
Content logic diagram.
Figure 4
Figure 4
R - CNN network model structure diagram.
Figure 5
Figure 5
Fast R - CNN network model structure diagram.
Figure 6
Figure 6
Faster R - CNN network model structure diagram.
Figure 7
Figure 7
Mask R - CNN network model structure diagram.
Figure 8
Figure 8
SSD network model structure diagram.
Figure 9
Figure 9
YOLOv8 network model structure diagram.
Figure 10
Figure 10
Classification of anchor-free detector.
Figure 11
Figure 11
Common methods of network compression and acceleration.
See this image and copyright information in PMC

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