Mango Leaf Disease Recognition and Classification Using Novel Segmentation and Vein Pattern Technique
- 주제(키워드) mango leaf , CCA , vein pattern , leaf disease , cubic SVM
- 주제(기타) Chemistry, Multidisciplinary
- 주제(기타) Engineering, Multidisciplinary
- 주제(기타) Materials Science, Multidisciplinary
- 주제(기타) Physics, Applied
- 설명문(일반) [Saleem, Rabia; Shah, Jamal Hussain; Sharif, Muhammad; Yasmin, Mussarat] COMSATS Univ Islamabad, Dept Comp Sci, Wah Campus, Islamabad 45550, Pakistan; [Yong, Hwan-Seung] Ewha Womans Univ, Dept Comp Sci & Engn, Seoul 120750, South Korea; [Cha, Jaehyuk] Hanyang Univ, Dept Comp Sci, Seoul 04763, South Korea
- 관리정보기술 faculty
- 등재 SCIE, SCOPUS
- OA유형 Green Published, gold
- 발행기관 MDPI
- 발행년도 2021
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000191060
- 본문언어 영어
- Published As https://doi.org/10.3390/app112411901
초록/요약
Mango fruit is in high demand. So, the timely control of mango plant diseases is necessary to gain high returns. Automated recognition of mango plant leaf diseases is still a challenge as manual disease detection is not a feasible choice in this computerized era due to its high cost and the non-availability of mango experts and the variations in the symptoms. Amongst all the challenges, the segmentation of diseased parts is a big issue, being the pre-requisite for correct recognition and identification. For this purpose, a novel segmentation approach is proposed in this study to segment the diseased part by considering the vein pattern of the leaf. This leaf vein-seg approach segments the vein pattern of the leaf. Afterward, features are extracted and fused using canonical correlation analysis (CCA)-based fusion. As a final identification step, a cubic support vector machine (SVM) is implemented to validate the results. The highest accuracy achieved by this proposed model is 95.5%, which proves that the proposed model is very helpful to mango plant growers for the timely recognition and identification of diseases.
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