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Image recognition on raw and processed potato detection: A review

机译:生和加工马铃薯检测中的图像识别:综述

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Objective: Chinese potato staple food strategy clearly pointed out the need to improve potato processing, while the bottleneck of this strategy is technology and equipment of selection of appropriate raw and processed potato. The purpose of this paper is to summarize the advanced raw and processed potato detection methods. Method: According to consult research literatures in the field of image recognition based potato quality detection, including the shape, weight, mechanical damage, germination, greening, black heart, scab potato etc., the development and direction of this field were summarized in this paper. Result: In order to obtain whole potato surface information, the hardware was built by the synchronous of image sensor and conveyor belt to achieve multi-angle images of a single potato. Researches on image recognition of potato shape are popular and mature, including qualitative discrimination on abnormal and sound potato, and even round and oval potato, with the recognition accuracy of more than 83%. Weight is an important indicator for potato grading, and the image classification accuracy presents more than 93%. The image recognition of potato mechanical damage focuses on qualitative identification, with the main affecting factors of damage shape and damage time. The image recognition of potato germination usually uses potato surface image and edge germination point. Both of the qualitative and quantitative detection of green potato have been researched, currently scab and blackheart image recognition need to be operated using the stable detection environment or specific device. The image recognition of processed potato mainly focuses on potato chips, slices and fries, etc. Conclusion: image recognition as a food rapid detection tool have been widely researched on the area of raw and processed potato quality analyses, its technique and equipment have the potential for commercialization in short term, to meet to the strategy demand of development potato as staple food in China.
机译:目的:中国马铃薯主食战略明确指出了改善马铃薯加工的必要性,而该战略的瓶颈在于选择合适的生马铃薯和加工马铃薯的技术和设备。本文的目的是总结先进的生马铃薯和加工马铃薯检测方法。方法:根据基于图像识别技术的马铃薯质量检测领域的研究文献,包括形状,重量,机械损伤,发芽,绿化,黑心,ab等,总结了该领域的发展方向。纸。结果:为了获得完整的马铃薯表面信息,通过图像传感器和传送带的同步构建了硬件,以实现单个马铃薯的多角度图像。马铃薯形状图像识别的研究已经很成熟,包括对异常和声音马铃薯,甚至圆形和椭圆形马铃薯的定性判别,识别精度超过83%。重量是马铃薯分级的重要指标,图像分类的准确率超过93%。马铃薯机械损伤的图像识别主要集中在定性识别上,是影响损伤形状和损伤时间的主要因素。马铃薯发芽的图像识别通常使用马铃薯表面图像和边缘发芽点。已经研究了绿马铃薯的定性和定量检测,目前需要使用稳定的检测环境或特定设备来进行sc疮和黑心病图像识别。加工马铃薯的图像识别主要集中在马铃薯片,切片和炸薯条等上。结论:作为食品快速检测工具的图像识别已在加工马铃薯的质量和分析领域进行了广泛的研究,其技术和设备具有潜在的应用前景。短期商业化,以满足发展马铃薯作为中国主食的战略需求。

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