Mango leaf disease recognition and classification using novel segmentation and vein pattern technique

Rabia Saleem, Jamal Hussain Shah, Muhammad Sharif, Mussarat Yasmin, Hwan Seung Yong, Jaehyuk Cha

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


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.

Original languageEnglish
Article number1901
JournalApplied Sciences (Switzerland)
Issue number24
StatePublished - 1 Dec 2021

Bibliographical note

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.


  • CCA
  • Cubic SVM
  • Leaf disease
  • Mango leaf
  • Vein pattern


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