Non-intrusive flowrate measurement and monitoring system of plant-protection unmanned aircraft systems based on pump voice analysis

Abstract

Application of Unmanned Aircraft Systems (UAS) for plant protection is becoming a common tool in agricultural field management. To avoid shortcomings of intrusive flowrate sensors including poor measurement accuracy and poor anti-vibration ability, a non-intrusive flowrate measurement and monitoring system of plant-protection UAS was developed based on pump voice signal analysis. It is mainly composed of STM32 processor, microphone and signal-conditioning circuit. By collecting and analyzing the voice signal of the pump in the UAS, the monitoring system will output the real-time values of spraying flowrate and amount. An extraction model was developed to determine operation status and primary frequency of the pump based on voice signal analysis. Real-time spray flowrate can be determined from the real-time extracted primary frequency and the fitted correlation formulas of spraying flowrate under outlet area and pump primary frequency. The flowrate correlation equation of one certain pump from 4-rotor UAS 3WQFTX-1011S was obtained, the max deviation rate of fitted spray flowrate was only 2.8%. In primary frequency extraction test, the error rate of primary frequency extraction was less than 1%. In the 4-rotor UAS flight tests: the max deviation of operating starting/end point was only 0.7 s and the max deviation of extracted total operating time was only 0.8 s; the deviation of extracted spray flowrate was less than 2%, and the max deviation rate of total spray amount was 3.2%. This research could be used as a guidance for plant-protection UAS non-intrusive flowrate measurement and monitoring. Keywords: plant protection UAS, voice signal analysis, non-intrusive, flowrate measurement, monitoring system DOI: 10.25165/j.ijabe.20211403.5508 Citation: Xu Y, Xue X Y, Sun Zh, Gu W. Non-intrusive flowrate measurement and monitoring system of plant-protection unmanned aircraft systems based on pump voice analysis. Int J Agric & Biol Eng, 2021; 14(3): 58–65.