Design and experiment of the key components of peanut planter based on EDEM
In response to the problems of high rates of missed seeding and reseeding in traditional peanut planter,the K-Means algorithm in the scikit-learn library was used to implement Euclidean distance clustering.Peanut seeds with complex sizes were divided into three categories and corresponding sizes were obtained.Preliminary EDEM simulation experiments were conducted on the first type of planter,and the results showed that the missed seeding rate of the improved planter decreased by an average of 2.814 percentage points compared to the pre improved planter.The reseeding rate of the improved planter decreased by an average of 2.27 percentage points compared to the pre improved planter.It is verified that the size categorization derived from the Euclidean distance clustering algorithm is reliable for the size improvement of the rowing spoon in the seed dispenser,which provides a theoretical reference for the subsequent optimization design and simulation experiment of the planter.
万方数据
维普
