Design and Experiment of Real-Time Grain Yield Monitoring System for Corn Kernel Harvester

Shangkun Cheng, Huayu Han, Jian Qi, Qianglong Ma, Jinghui Liu, Dong An and Yang Yang ()
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Shangkun Cheng: School of Engineering, Anhui Agricultural University, Hefei 230036, China
Huayu Han: School of Engineering, Anhui Agricultural University, Hefei 230036, China
Jian Qi: School of Engineering, Anhui Agricultural University, Hefei 230036, China
Qianglong Ma: School of Engineering, Anhui Agricultural University, Hefei 230036, China
Jinghui Liu: School of Engineering, Anhui Agricultural University, Hefei 230036, China
Dong An: School of Engineering, Anhui Agricultural University, Hefei 230036, China
Yang Yang: School of Engineering, Anhui Agricultural University, Hefei 230036, China

Agriculture, 2023, vol. 13, issue 2, 1-14

Abstract: Real-time crop harvest data acquisition from harvesters during harvesting operations is an important way to understand the distribution of crop harvest in the field. Most real-time monitoring systems for grain yield using sensors are vulnerable to factors such as low accuracy and low real-time performance. To address this phenomenon, a real-time grain yield monitoring system was designed in this study. The real-time monitoring of yield was accomplished by adding three pairs of photoelectric sensors to the elevator of the corn kernel harvester. The system mainly consists of a signal acquisition and processing module, a positioning module and a visualization terminal; the signal acquisition frequency was set to 1 kHz and the response time was 2 ms. When the system operated, the signal acquisition and processing module detected the sensor signal duration of grain blocking the scrapers of the grain elevator in real-time and used the low-potential signal-based corn grain yield calculation model constructed in this study to complete the real-time yield measurement. The results of the bench tests, conducted under several different operating conditions with the simulated elevator test bench built, showed that the error of the system measurement was less than 5%. Field tests were conducted on a Zoomlion 4YZL-5BZH combined corn kernel harvester and the results showed that the average error of measured yield was 3.72%. Compared to the yield measurement method using the weighing method, the average error of the bench test yield measurement was 7.6% and the average error of yield measurement in field trials with a mass flow sensor yield measurement system was 16.38%. It was verified that the system designed in this study has high yield measurement accuracy and real-time yield measurement, and can provide reference for precision agriculture and high yield management.

Keywords: yield measurement; photoelectric sensors; yield calculation model; real-time performance (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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