Computer vision based methods for detecting weeds in lawns

In this paper, two methods for detecting weeds in lawns using computer vision techniques are proposed. The first is based on an assumption about the differences in statistical values between the weed and grass areas in edge images and using Bayes classifier to discriminate them. The second also uses the differences in texture between both areas in edge images but instead applies only simple morphology operators. Correct weed detection rates range from 77.70 to 82.60% for the first method and from 89.83 to 91.11% for the second method. From the results, the methods show the robustness against lawn color change. In addition, the proposed methods together with a chemical weeding system as well as a non-chemical weeding system based on pulse high voltage discharge are simulated and the efficiency of the overall systems are evaluated theoretically. With a chemical based system, more than 72% of the weeds can be destroyed with a herbicide reduction rate of 90–94% for both methods. For the latter weeding system, killed weed rate varies from 58 to 85%.