Turkish Journal of Agriculture and Forestry




The objective of the study was to develop and test an automatic machine vision-based spraying robot for the detection, tracking, and spraying of artificial weeds by using LabVIEW programming language. The greenness method was used to distinguish green objects in the image. A time-controlled spray nozzle was run according to the presence of an artificial weed and its coordinates. A mobile test bench was built and the spraying system with a webcam was operated at speeds of 0.42, 0.54, 0.66, 0.78, and 0.90 km h-1, so as to be able to see the performance of the system. The amount of deposits on the ground in the spray pattern was evaluated on the test area and used in comparisons for site specific and broadcast spraying methods. A spraying solution containing brilliant sulpho-flavin (BSF) tracer (0.4 g L-1) and filter papers were used to compare the deposition of spray pattern achieved on the ground with both methods. According to the results, site-specific spraying application saved on average 89.48%, 79.98%, and 73.93% application volumes for 500 ms, 1000 ms, and 1500 ms spraying durations, respectively, at all spraying speeds is compared to broadcast spraying application. As one would expect, deposits on the filter papers decreased with increasing spraying speed. In addition, operating the system with 1000 ms nozzle controlled site specific spraying at different speeds did not cause a significant difference in the amount of deposits in the spray pattern and spraying accuracy as compared to the broadcast spraying method.


Image processing, LabVIEW, machine vision, patch spraying, weed detection

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