Design of Wheat Variable Spray System Based on Machine Vision and Beidou Positioning
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    Abstract:

    Aiming at the problems of waste of pesticide, low utilization rate and environmental pollution caused by spraying the same amount of pesticide in the traditional plant protection spray rod sprayer, a wheat variable spray operation system based on positioning system and machine vision was designed. The region of interest (ROI) was obtained by double-plane height projection method. The effects of velocity and plant density on spray volume were studied, and the control method of variable spray volume was proposed. In the target area planned by the positioning system, variable spray was realized by machine vision processing. The test results showed that at the same unit speed, the average fog drop coverage in the sparse area of plant density was reduced by 12.06% compared with that in the normal area of plant density. At the same plant density, the average fog drop coverage rate at unit advancing speed of 0.75m/s was 3.94% higher than that at speed of 1.50m/s. Under the condition that the spray standard was satisfied, variable spray can be realized at different speeds and different plant densities. In order to verify the grade transformation accuracy of the driving speed at the boundary of the target area, the positioning sensor was used to judge the relative position of the nozzle at the boundary of the target area in real time and control the opening and closing. The test results showed that the accuracy was the highest when the driving speed was 0.50m/s, and the average exceeding error at the boundary of the region was 48.72cm. In order to verify the influence of driving mode on the accuracy of spray level transformation, a test on the influence of driving mode on the accuracy of spray level transformation was carried out at the boundary of the target area by the positioning system. The test results showed that the average value of the minimum outsize error of driving into the target area was 7.20cm.

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History
  • Received:July 14,2021
  • Revised:
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  • Online: July 10,2022
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