Leaf lesion classification (LLC) algorithm based on artificial bee colony (ABC)
In this paper, an algorithm to classify leaf disease severity based on lesions is presented. The algorithm involved three main steps, filtration, recognition and detection.Artificial Bee Colony, Fuzzy Logic, Otsu and Geometry formula were incorporated to achieve the goal.Ninety-four leaf images wer...
保存先:
| 主要な著者: | , , , |
|---|---|
| フォーマット: | 論文 |
| 言語: | English |
| 出版事項: |
Asian Research Publishing Network (ARPN)
2015
|
| 主題: | |
| オンライン･アクセス: | http://repo.uum.edu.my/14839/1/jeas_RG.pdf http://repo.uum.edu.my/14839/ http://www.arpnjournals.com/jeas/research_papers/rp_2015/jeas_0215_1595.pdf |
| タグ: |
タグ追加
タグなし, このレコードへの初めてのタグを付けませんか!
|
| 要約: | In this paper, an algorithm to classify leaf disease severity based on lesions is presented. The algorithm involved three main steps, filtration, recognition and detection.Artificial Bee Colony, Fuzzy Logic, Otsu and Geometry formula
were incorporated to achieve the goal.Ninety-four leaf images were used in this algorithm combination experiment.The study was conducted in four phases, filtration, recognition, detection and evaluation.Comparison was made with four
other algorithms, Otsu, Canny, Robert and Sobel. Results showed that the Leaf Lesion Classification (LLC) algorithm based on Artificial Bee colony (ABC) produced an average 96.83% of accuracy and average 1.66 milliseconds of processing time, indicating that LLC algorithm is better than algorithm such as Otsu, Canny, Roberts and Sobel. The study makes a substantial contribution to the body of knowledge in image processing. |
|---|