Prediction of seed distribution in rectangular vibrating tray using grey model and artificial neural network

To maintain good continuous working performance in a vacuum plate seeder, it is important to monitor the distribution of seeds in real time and automatically adjust vibration parameters accordingly. Seed motion in a rectangular vibrating tray with vibration varying with time and interference by direction angle was simulated using the discrete element method (DEM). A plane model P was used to describe the variation of seed layer thickness. Four square areas on the bottom of the tray were divided symmetrically near the four corners to measure seed layer thickness, and a monitoring plane model Pm was established. DEM simulation results showed that the models Pm and P had the similar change rules, although there were some differences in fitting parameters. There was obvious time delay in the change of Pm compared with P. Therefore, a grey system model (GM) was adopted to predict the change of Pm, and two back-propagation (BP) neural networks which take GM prediction results as input parameters were developed respectively. Then, according to the BP neural networks outputs, a prediction plane model Pp was proposed to predict the seed distribution. Experiments were carried out on a test-rig to validate these predictions. The seed distribution plane P was measured manually, the monitoring plane Pm was established using seed layer thickness measurement results and the prediction plane Pp was established using the GM and BP neural networks. The results indicated that the proposed method had good precision and stability and provides the basis for the design of an automatic control system for the vibrating tray to promote a uniform seed distribution.