Research on the self-feedback algorithm of grain yield monitor model based on combine harvester

Background: In order to obtain real time grain yield information, a kind of grain yield monitor system based on photoelectric principle was developed. Methods: It was consisted of sensor module, data collection module, GPS module and grain yield calculation terminal. After analyzing the working status of combine harvester and the simulation of scraper heap shape, a subsection type grain yield monitor model was proposed. For the accuracy of grain yield monitor system was affected by the elevator speed seriously, the model had considered the elevator speed as an input parameter. Results: When the combine harvester worked at the normal status, grain volume had linear relationship with scraper grain thickness. In order to further optimize the quality of yield data, a new preprocessing method was also proposed based on elevator speed dynamic threshold value filter. Once the data was below 10% of the real time calculated scraper thickness, it was removed. Once it was above 10 times of the real time calculated scraper thickness, it was replaced by the normal value one second before. Discussions: In order to evaluate this new preprocessing method, original data, average filter data and dual threshold filter data were used to validate the model. The test results show that the proposed data preprocessing method could eliminate the singularity and improve the smooth of yield data, obviously. Conclusion: The field experiment showed that validation error of the grain yield monitor model was less than 3%, which could satisfy the practical need