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DYNAMIC POTATO IDENTIFICATION AND CLEANING METHOD BASED ON RGB-D

文献类型： 外文期刊

作者： Fu, Xiaoming 1 ; Meng, Zhijun 2 ; Wang, Zedong 1 ; Yin, Xiaohui 3 ; Wang, Chang 4 ;

作者机构： 1.Heilongjiang Bayi Agr Univ, Coll Engn, Daqing, Peoples R China

2.Beijing Res Ctr Informat Technol Agr, Beijing, Peoples R China

3.Daqing Oilfield Co Ltd, Oil Prod Plant Planning & Design Res Inst 4, Daqing, Peoples R China

4.Heilongjiang Bayi Agr Univ, Coll Sci, Daqing, Peoples R China

关键词： potato; dynamic identification; cleaning method; RGB-D; Mask R-CNN

期刊名称：ENGENHARIA AGRICOLA （ 影响因子：0.856； 五年影响因子：1.014 ）

ISSN： 0100-6916

年卷期： 2022 年 42 卷 3 期

页码：

收录情况： SCI

摘要： To solve the problems of a large number of clods remaining in potatoes after mechanized harvesting in northern heavy clay soil planting areas in China and requiring much labor to separate clods from potatoes, which leads to a heavy workload, inefficiency and a low cleaning rate, an RGB-D-based Mask R-CNN dynamic potato identification model is established by using acquired RBG-D image data of untreated potatoes after mechanized harvesting, and a potato cleaning method is presented in this paper. This makes it possible to automatically separate clod impurities from potatoes. The experimental results showed that the prediction accuracy of the identification model is more than 97%. With the increase in cleaning conveyance speed, the prediction accuracy of the model and the actual cleaning precision show a downward trend. Comprehensively considering the potato cleaning efficiency and accuracy, when the speed is set to 0.4 m.s(-1), the cleaning precision reaches as high as 96.35%. This research provides a method and theoretical reference for the further study of intelligent potato cleaning systems.

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