Tea Leaf Disease Segmentation by using Color and Region’s Mean Based Segmentation (CRM)
P. Velmurugan1, M. Renukadevi2
1Velmurugan P, Research Scholar, Part Time Ph.D, Bharathair University,Coimbatore.
2Dr.M.Renuka Devi, Professor & Head, Department of BCA, Sri Krishna Arts and Science College, Coimbatore.
Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 109-112 | Volume-8 Issue-4, November 2019. | Retrieval Number: D6494118419/2019©BEIESP | DOI: 10.35940/ijrte.D6494.118419
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In agriculture image processing and Datamining play an important role. Prediction of crop yield prediction is very important in tea production. Image segmentation is used to segment the disease affected region in the leaf. It segment image into various homogeneous region. In this paper color and Region’s mean based segmentation technique is introduced to subtract background and fore ground. This new approach is analyzed and compared based on five performance metrics such as PSNR (Peak Signal to Noise Ratio) Value, Rand Index (RI), precision, recall and accuracy. The proposed method gave better accuracy than other methods.
Keywords: Image Processing, Segmentation, PSNR, Rand Index, Precision, Recall.
Scope of the Article: Signal and Image Processing.