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To study the wind field pattern created by unmanned aerial vehicles (UAVs) in agricultural chemical applications, triggering the wind speed sensors distributed in crop canopy along the flight path simultaneously when the UAV passes over each of them is critical in capturing the instantaneous wind field data. However, in many cases the measurements were triggered manually by human vision which reduced the timeliness and validity of the data. The data acquisition triggering method was improved and automated for wind speed sensors by predicting the exact UAV flyover timing with accurate geo-location information from an onboard Beidou positioning system and the modeling of future flight status based on past flight data given that agricultural UAVs usually operate at low speed and low altitude without overload. Since the weed speed sensors used could only record data for five seconds, a flight status prediction model was developed to determine the triggering timing for data acquisition based on the consistency and stability of the flight direction, speed, and altitude within a certain period of time. Extensive field experiments were conducted, and the model predicted and wind speed sensor measured maximum wind speed data were compared. No significant difference was found between them at a 99% confidence interval with a P value of 0.956. With the improved triggering timing, the averaged maximum wind speed in X, Y, Z axes occurred at 3.036s, 2.427s and 3.145s, respectively, of the five-second logging period with standard deviations of 0.79s, 0.87s and 0.98s, respectively. The maximum wind speed, which corresponded to the wind speed when the UAV flew over each sensor, measured by the improved data acquisition system was ensured to be captured now within the five-second optimal logging period of the wind speed sensors by the improved aerial-and-ground-sensor cooperative sensing system.