DYNAMIC POTATO IDENTIFICATION AND CLEANING METHOD BASED ON RGB-D

Abstract

ABSTRACT 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.

Keywords

• potato
• dynamic identification
• cleaning method
• RGB-D
• Mask R-CNN