基于机器视觉的苹果园果实识别研究综述

金保华; 殷长魁; 张卫正; 张伟伟

doi:10.3969/j.issn.2096-1553.2019.02.010

March 2019

Article Contents

JIN Baohua, YIN Changkui, ZHANG Weizheng and et al. Review on apple garden fruit recognition based on machine vision[J]. Journal of Light Industry, 2019, 34(2): 71-81. doi: 10.3969/j.issn.2096-1553.2019.02.010

Citation: JIN Baohua, YIN Changkui, ZHANG Weizheng and et al. Review on apple garden fruit recognition based on machine vision[J]. Journal of Light Industry, 2019, 34(2): 71-81. doi: 10.3969/j.issn.2096-1553.2019.02.010 shu

Review on apple garden fruit recognition based on machine vision

College of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China

Received Date: 2018-11-08

Abstract

The current situation of fruit recognition based on machine vision was reviewed from fruit recognition based on color threshold, shape and texture, three-dimensional fruit shape recognition, nocturnal fruit recognition, fruit recognition based on machine learning, fruit recognition under the influence of shadow and occlusion. It's thought that the algorithms involved in the above research were more complicated and features were very powerful. However, in view of the limitations of visual theory, image processing technology and hardware conditions, as well as the complex and varied environment of apple garden, there was no more ideal technology for machine vision-based fruit recognition, and it needed to be improved. Future research focuses include:1) Strengthening more effective algorithms for image enhancement, image segmentation, and feature extraction to effectively address the effects of fruit overlap, occlusion, color, and light changes; and improving the identification algorithms for day and night orchard field operations for the construction of an all-weather operation picking robot. 2) Strengthening the research on fruit recognition based on self-supervised learning to increase the feedback information received by the model and the complex applicable task types of model representation, reduce the proportion of human manual labor involved in the task, and improve the degree of automation. 3) Strengthening the research of automatic image acquisition and fruit recognition, combined with computer vision and near-infrared, laser radar and other detection technologies, integrating multi-modal image and non-image information for fruit recognition, improving processing speed and real-time, and identifing accuracy and system robustness to provide reference for apple's automatic picking and precise management of orchard.
- machine vision,
- apple garden,
- fruit recognition,
- image processing,
- machine learning
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Review on apple garden fruit recognition based on machine vision

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