| 講演抄録／キーワード |
| 講演名 |
2006-10-20 16:15
Lawn weeds detection methods using image processing techniques
○Ukrit Watchareeruetai・Yoshinori Takeuchi・Tetsuya Matsumoto・Hiroaki Kudo・Noboru Ohnishi（Nagoya Univ.） |
| 抄録 |
（和） |
(まだ登録されていません) |
| （英） |
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. |
| キーワード |
（和） |
/ / / / / / / |
| （英） |
lawn / weed detection / Bayesian classifier / morphology operator / gray-scale uniformity analysis / / / |
| 文献情報 |
信学技報, vol. 106, no. 301, PRMU2006-115, pp. 65-70, 2006年10月. |
| 資料番号 |
PRMU2006-115 |
| 発行日 |
2006-10-13 (PRMU) |
| ISSN |
Print edition: ISSN 0913-5685 |
| PDFダウンロード |
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