基于Yolov5的蘑菇精密分级计量专用机器人

IF 2.5 4区 综合性期刊 Q2 CHEMISTRY, MULTIDISCIPLINARY Applied Sciences-Basel Pub Date : 2023-09-07 DOI:10.3390/app131810104
Xiaoyang Zhu, K. Zhu, Pingzeng Liu, Yan Zhang, Honghua Jiang
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引用次数: 0

摘要

为了实现准确的蘑菇分类和测量,有必要优化现有的分类算法和测量设备,并设计一个特定的机器人来提高分类精度和测量效率。为了实现上述目标,使用Yolov5+OpenCV和使用电阻应变计传感器的蘑菇测量系统对蘑菇分级进行了研究级验证。在蘑菇分级方面,使用了一种基于OpenCV视觉库的方法来识别蘑菇轮廓外的最小四边形,从而可以测量蘑菇的大小。实验结果表明,该方法可以对不同光照条件下相互遮挡的目标物体进行评估,准确率为96%。在蘑菇的测量中,电阻的应变被转换成模拟信号,不同等级蘑菇的重量经过检测电路模块处理后根据线性关系进行转换。通过该方法,成功地将误差范围控制在±0.02 kg以内,满足了蘑菇精确测量的要求。田间试验结果表明,所提出的香菇精确分级测量方法是有效可行的,为生产单位香菇的智能分级测量提供了技术支持。
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A Special Robot for Precise Grading and Metering of Mushrooms Based on Yolov5
In order to accomplish accurate mushroom classification and measurement, it is necessary to optimize the existing classification algorithm and measurement devices, as well as to design a specific robot to improve classification accuracy and measurement efficiency. In order to achieve the above objectives, a research-level verification of mushroom grading using Yolov5 + OpenCV and a mushroom measuring system using a resistance strain gauge sensor was carried out. In the aspect of mushroom grading, a method based on the OpenCV visual library was used to identify the minimum quadrilateral outside the mushroom contour, allowing the size of the mushroom to be measured. The experiment’s results show that the method can assess target objects that are occluded with each other under different illumination conditions with 96% accuracy. In the measurement of mushrooms, the strain of the resistance is converted into an analog signal, and the weight of different grades of mushrooms is converted according to the linear relationship after processing by the detection circuit module. Through this method, the error range is successfully controlled within ±0.02 kg, which meets the requirements of accurate measurement of mushrooms. The results of field experiments show that the proposed accurate grading and measurement method of Lentinula edodes is effective and feasible, and provides technical support for the intelligent grading and measurement of Lentinula edodes in production units.
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来源期刊
Applied Sciences-Basel
Applied Sciences-Basel CHEMISTRY, MULTIDISCIPLINARYMATERIALS SCIE-MATERIALS SCIENCE, MULTIDISCIPLINARY
CiteScore
5.30
自引率
11.10%
发文量
10882
期刊介绍: Applied Sciences (ISSN 2076-3417) provides an advanced forum on all aspects of applied natural sciences. It publishes reviews, research papers and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files and software regarding the full details of the calculation or experimental procedure, if unable to be published in a normal way, can be deposited as supplementary electronic material.
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