Experiments and Analysis of Close-Shot Identification of On-Branch Citrus Fruit with RealSense.

Jizhan Liu,Xinxin Zhu,Tabinda Naz Syed,Yan Yuan,Yao Zhou

doi:10.3390/s18051510

Jizhan Liu, Xinxin Zhu + Show 3 more

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https://doi.org/10.3390/s18051510

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Journal: Sensors	Publication Date: May 11, 2018
Citations: 20	License type: CC BY 4.0

Affiliation: Jiangsu University, Nanjing Forestry University

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Fruit recognition based on depth information has been a hot topic due to its advantages. However, the present equipment and methods cannot meet the requirements of rapid and reliable recognition and location of fruits in close shot for robot harvesting. To solve this problem, we propose a recognition algorithm for citrus fruit based on RealSense. This method effectively utilizes depth-point cloud data in a close-shot range of 160 mm and different geometric features of the fruit and leaf to recognize fruits with a intersection curve cut by the depth-sphere. Experiments with close-shot recognition of six varieties of fruit under different conditions were carried out. The detection rates of little occlusion and adhesion were from 80–100%. However, severe occlusion and adhesion still have a great influence on the overall success rate of on-branch fruits recognition, the rate being 63.8%. The size of the fruit has a more noticeable impact on the success rate of detection. Moreover, due to close-shot near-infrared detection, there was no obvious difference in recognition between bright and dark conditions. The advantages of close-shot limited target detection with RealSense, fast foreground and background removal and the simplicity of the algorithm with high precision may contribute to high real-time vision-servo operations of harvesting robots.

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The rapid development of the global fruit and vegetable industry has contributed to agricultural upgrading and increased income for agricultural practitioners
The RealSense F200 was fixed on a miniature tripod whose plane was perpendicular to the desktop
The results show that little occlusion, which led to significant depth-point clouds of fruit, and little adhesion, which could be cut by depth-sphere cutting to obtain a single intersection curve, brought the rate of fruit recognition up to 80–100%

Summary

Introduction

The rapid development of the global fruit and vegetable industry has contributed to agricultural upgrading and increased income for agricultural practitioners. According to the statistics of the Food and Agriculture Organization of the United Nations, global vegetable and fruit production increased by 3.7-times and 4.3-times, respectively, over the past 50 years [1]. The fruit and vegetable industry is labor-intensive. There are about 150 million people engaged in the daily planting and management of fruits and vegetables in China alone, for which harvesting takes up 40–50% of the total work [2,3]. Harvesting fresh fruits and vegetables still generally relies on people, which takes up most of the labor and is the most difficult area to utilize mechanized operations [2,4].

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