Technical vision system for apple defects recognition: justification, development, testing

IF 0.2 Q4 AGRICULTURE, MULTIDISCIPLINARY
P. Kazakevich, A. Yurin, G. Prokopovich
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引用次数: 4

Abstract

The most rational method for identifying the quality of fruits is the optical method using PPE, which has the accuracy and stability of measurement, as well as distance and high productivity. The paper presents classification of fruit quality recognition systems and substantiates the design and technological scheme of the vision system for sorting them, consisting of an optical module with installed structural illumination and a video camera, an electronic control unit with an interface and actuators for the sorter and conveyor for fruits. In the course of the study, a single-stream type of fruit flow in PPE with forced rotation was substantiated, a structural and technological scheme of an STZ with a feeding conveyor, an optical module and a control unit, an algorithm for functioning of the STZ software was developed based on algorithm for segmentation of fruit colors, tracking algorithm, etc. deep learning ANN, which provide recognition of the size and color of fruits, as well as damage from mechanical stress, pests and diseases. The developed STZ has been introduced into the processing line for sorting and packing apples, LSP-4 has successfully passed preliminary tests and production tests at OJSC Ostromechevo. In the course of preliminary tests of the LSP-4 line, it was found that it provided fruit recognition with a probability of at least 95%, while the labor productivity made 2.5 t/h.
苹果缺陷识别技术视觉系统:论证、开发、测试
鉴定水果质量最合理的方法是使用PPE的光学方法,具有测量的准确性和稳定性,而且距离远,生产率高。介绍了水果品质识别系统的分类,提出了水果品质识别视觉分拣系统的设计和工艺方案,该系统由带有结构照明的光学模块和摄像机、带有接口的电子控制单元、水果分拣机和输送机的执行机构组成。在研究过程中,验证了强制旋转的PPE单流型水果流,提出了带有进料输送机、光模块和控制单元的STZ的结构和工艺方案,并基于水果颜色分割算法、跟踪算法等深度学习人工神经网络,开发了STZ软件的功能算法,实现了对水果大小、颜色以及机械应力损伤的识别。病虫害。开发的STZ已被引入苹果分拣和包装生产线,LSP-4已成功通过OJSC Ostromechevo的初步测试和生产测试。在对LSP-4系的初步试验过程中,发现其提供果实识别的概率至少为95%,而劳动生产率为2.5 t/h。
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