Inconsistent Cluster Analysis With Disease Feature Enhancement (ICADFE) For American Cotton Leaf Disease Recognition
Kapil Prashar1, Rajneesh Talwar2, Chander Kant3
1Kapil Prashar, Research Scholar, I.K. Gujral Punjab Technical University, Kapurthala Road, Jalandhar (Punjab), India.
2Rajneesh Talwar, Principal, CGC Group of Colleges, Jhanjeri, Mohali (Punjab), India.
3Chander Kant, Assistant Professor, Department of Computer Science & Applications, Kurukshetra University, Kurukshetra (Haryana), India.
Manuscript received on 03 September 2019 | Revised Manuscript received on 13 September 2019 | Manuscript Published on 23 September 2019 | PP: 1497-1505 | Volume-8 Issue-5C, May 2019 | Retrieval Number: E12200585C19/19©BEIESP | DOI: 10.35940/ijeat.E1220.0585C19
Open Access | Editorial and Publishing 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 (

Abstract: The broad leaves of cotton plant carry various visible disease symptoms. The ability of visual analysis by experts motivated the development of the plant disease recognition model. There are several visual feature descriptors, which can be primarily distinguished on the basis of pattern, texture or color. This system has been developed for the convenience of the farmers, who can avail the benefit by submitting the pictures of infected cotton leaves on the interface and the plant disease recognition system will return type of disease. In this paper, a dynamic feature descriptor is designed with inconsistent cluster analysis (ICA) and disease feature enhancement (DFE), which are combined as hybrid descriptor known as ICADFE for the recognition of the cotton plant disease. The ICADFE is found to improve the detection accuracy (approx. 80%), precision (approx 95%) and f1-measure (approx. 88%) on average in comparison with traditional shape and texture based feature descriptors such as scale invariant feature transform (SIFT), speeded up robust features (SURF) and fast retina keypoints (FREAK) with multicategory SVM (mSVM) for disease recognition.
Keywords: Leaf Disease Recognition, Inconsistent Cluster Analysis, Machine Vision.
Scope of the Article: Pattern Recognition and Analysis