| Issue |
ITM Web Conf.
Volume 32, 2020
International Conference on Automation, Computing and Communication 2020 (ICACC-2020)
|
|
|---|---|---|
| Article Number | 03050 | |
| Number of page(s) | 6 | |
| Section | Computing | |
| DOI | https://doi.org/10.1051/itmconf/20203203050 | |
| Published online | 29 July 2020 | |
Identification of Anthracnose Disease in Mango Plant Using Clustering and Gray level Co- occurrence Matrix
1 Ramrao Adik Institute of Technology ,Electronics and Telecommunication department, Nerul, India
2 Ramrao Adik Institute of Technology ,Electronics and Telecommunication department, Nerul, India
3 Ramrao Adik Institute of Technology ,Electronics and Telecommunication department, Nerul, India
4 Ramrao Adik Institute of Technology ,Electronics and Telecommunication department, Nerul, India
5 Ramrao Adik Institute of Technology ,Electronics and Telecommunication department, Nerul, India
* Corresponding author: author@e-mail.org
The aim of this paper is to help farmers to identify disease without observing each and every mango plant by eyes. Farmers can use the pesticides according to the leaf disease identified. For identification of this leaf disease K-means clustering and GLCM (Gray level co-occurrence matrix) technique are used. Affected areas of the leaf are identified more accurately by using K-means clustering. GLCM is used to get the texture features which help for identifying the mango leaf disease. The signs of the diseases on the leaf should be identified at the early for the growth of productivity in mango plant, for this leaf disease identification is important.
© The Authors, published by EDP Sciences, 2020
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