Mango Leaf Disease Recognition and Classification Using Novel Segmentation and Vein Pattern Techniqueopen access
- Authors
- Saleem, Rabia; Shah, Jamal Hussain; Sharif, Muhammad; Yasmin, Mussarat; Yong, Hwan-Seung; Cha, Jaehyuk
- Issue Date
- Dec-2021
- Publisher
- MDPI
- Keywords
- mango leaf; CCA; vein pattern; leaf disease; cubic SVM
- Citation
- Applied Sciences, v.11, no.24, pp.1 - 12
- Indexed
- SCIE
SCOPUS
- Journal Title
- Applied Sciences
- Volume
- 11
- Number
- 24
- Start Page
- 1
- End Page
- 12
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/138480
- DOI
- 10.3390/app112411901
- Abstract
- 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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