Lawn Weeds Detection Methods Using Image Processing Techniques

元データ 2006-10-13 社団法人電子情報通信学会

概要

In this work, three methods of lawn weeds detection based on various image processing techniques, Bayesian classifier, morphology operators, and gray-scale uniformity analysis based methods, were evaluated and compared by using four different seasons image datasets. In the evaluations, two types of automatic weeding systems (i.e., chemical and non-chemical based) together with the detection methods were simulated and their performances were compared. From the results, for chemical approach, the Bayesian classifier based method could destroy 80.85%-96.30% of weeds, with more than 80% of accuracy for all datasets. For non-chemical approach, its accuracy was nearly 100% for all datasets. This shows its robustness against changing in season. The morphological operator based method was the best in weeds destruction for the non-chemical based system. However, its accuracy performance ranked as the last. For gray-scale uniformity analysis method, it missed detecting a lot of weeds for winter dataset, only 31.91%-36.17% of total weeds could be destroyed. Among three detection methods, the Bayesian classifier based method can be considered as the most appropriate method for both chemical and non-chemical weeding systems.

著者

松本 哲也 名古屋大学大学院情報科学研究科メディア科学専攻
Matsumoto Tetsuya Department of Cardiovascular and Respiratory Medicine, Shiga University of Medical Science
WATCHAREERUETAI Ukrit Department of Media Science, Graduate School of Information Science, Nagoya University
TAKEUCHI Yoshinori Department of Media Science, Graduate School of Information Science, Nagoya University
KUDO Hiroaki Department of Media Science, Graduate School of Information Science, Nagoya University
OHNISHI Noboru Department of Media Science, Graduate School of Information Science, Nagoya University
大西 昇 名古屋大学 大学院 工学研究科 情報工学専攻
Ohnishi Noboru Department Of Media Science Graduate School Of Information Science Nagoya University
Watchareeruetai Ukrit Department Of Media Science Graduate School Of Information Science Nagoya University
Matsumoto Tetsuya Department Of Cardiovascular And Respiratory Medicine
Matsumoto Tetsuya Department Of Informatics Kyushu University
Takeuchi Yoshinori Department Of Informatics And Mathematical Science Graduate School Of Engineering Science Osaka Univ
Ohnishi Noboru Department Of Information Engineering School Of Engineering Nagoya University
Kudo H Department Of Media Science Graduate School Of Information Science Nagoya University
Watchareeruetai Ukrit Dep. Of Media Sci. Graduate School Of Information Sci. Nagoya Univ.

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