A signal output quantity (SOQ) judgment algorithm for improving seeding quantity accuracy

• An analog signal seed sensor has been designed to monitor the seed signals, both large and small size seeds (maize and mung bean) can be monitored. • An SOQ judgment algorithm and calculation model based on ADC signal acquisition have been established. • Under the condition that the recognition accuracy of single maize and mung bean seed was 100%, the average recognition accuracy of the double overlapping maize seeds was 91%, and the accuracy of mung bean seeds was 83%. It is very important to monitor the seeding quantity in the precision seeding process. However, there is still room for improvement in monitoring accuracy of seeding quantity. One of the important questions is that double overlapping seeds cannot be accurately identified. They are usually counted as single seeds. The sensor signal acquisition and judgment algorithm play a key role in seed recognition and counting. This study mainly focused on the discussion and analysis of signal acquisition and judgment algorithms. Based on the ADC (Analog to Digital Converter) voltage signal acquisition, an analog signal seed sensor has been designed. The peak value judgment algorithm and the mean value judgment algorithm are compared and analyzed. At the same time, a signal output quantity (SOQ) judgment algorithm and calculation model based on ADC signal acquisition have been established. Different from the peak value judgment algorithm and the mean value judgment algorithm, the SOQ judgment algorithm did not focus on the magnitude of the acquired voltage values, but on the quantity of acquired voltage values. The test results showed that the monitoring accuracy of the judgment algorithm and calculation model for single and double overlapping model seeds had reached 100%. In order to test the monitoring accuracy of the judgment algorithm and calculation model for real seeds, maize seeds and mung bean seeds were selected for testing. The results showed that under the condition that the recognition accuracy of single maize seed was 100%, the average recognition accuracy of the double overlapping maize seeds was 91%, and the average monitoring accuracy was 95.5%. And under the condition that the recognition accuracy of single mung bean seed was 100%, the average recognition accuracy of the double overlapping mung bean seeds was 83%, and the average monitoring accuracy was 91.5%. In terms of the monitoring accuracy of double overlapping seeds, the SOQ judgment algorithm has been greatly improved compared with the peak judgment algorithm and the mean judgment algorithm. This provided a new idea for the monitoring of the seeding quantity, and it was also of great help to the improvement of the statistical accuracy of the seeding quantity.