caa a22 4500 46793696X CHVBK 20180406153000.0 cr unu---uuuuu 170328e20061001xx s 000 0 eng 10.1007/s00138-006-0039-x doi (NATIONALLICENCE)springer-10.1007/s00138-006-0039-x Computer vision based methods for detecting weeds in lawns [Elektronische Daten] [Ukrit Watchareeruetai, Yoshinori Takeuchi, Tetsuya Matsumoto, Hiroaki Kudo, Noboru Ohnishi] 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%. Springer-Verlag, 2006 Weeding nationallicence Lawn nationallicence Computer vision nationallicence Bayes classifier nationallicence Morphology nationallicence Watchareeruetai Ukrit Department of Media Science, Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, 464-8603, Nagoya, Japan aut Takeuchi Yoshinori Department of Media Science, Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, 464-8603, Nagoya, Japan aut Matsumoto Tetsuya Department of Media Science, Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, 464-8603, Nagoya, Japan aut Kudo Hiroaki Department of Media Science, Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, 464-8603, Nagoya, Japan aut Ohnishi Noboru Department of Media Science, Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, 464-8603, Nagoya, Japan aut Machine Vision and Applications Springer-Verlag 17/5(2006-10-01), 287-296 0932-8092 17:5<287 2006 17 138 https://doi.org/10.1007/s00138-006-0039-x text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s00138-006-0039-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Watchareeruetai Ukrit Department of Media Science, Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, 464-8603, Nagoya, Japan aut NATIONALLICENCE 700 1- Takeuchi Yoshinori Department of Media Science, Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, 464-8603, Nagoya, Japan aut NATIONALLICENCE 700 1- Matsumoto Tetsuya Department of Media Science, Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, 464-8603, Nagoya, Japan aut NATIONALLICENCE 700 1- Kudo Hiroaki Department of Media Science, Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, 464-8603, Nagoya, Japan aut NATIONALLICENCE 700 1- Ohnishi Noboru Department of Media Science, Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, 464-8603, Nagoya, Japan aut NATIONALLICENCE 773 0- Machine Vision and Applications Springer-Verlag 17/5(2006-10-01), 287-296 0932-8092 17:5<287 2006 17 138 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer