Name
Abstract | ||
|---|---|---|
The process of detecting plant disease by human naked-eye is difficult and very expensive practice, particularly in developing countries like India. Designing and providing a fast-reliable automated mobile vision based solution for such tasks, is a great realistic contribution to the society. In this paper, a mobile client–server architecture for leaf disease detection and diagnosis using a novel combination of Gabor wavelet transform (GWT) and gray level co-occurrence matrix (GLCM), opens a new dimension in pattern recognition, is proposed. Mobile disease diagnosis system represents a diseased patch in multi-resolution and multi-direction feature vector. Mobile client captures and pre-processes the leaf image, segments diseased patches in it and transmits to the Pathology Server, reducing transmission cost. The Server performs the computational tasks: GWT–GLCM feature extraction and \(k\)-Nearest Neighbor classification. The result is sent back to the users screen via an SMS (short messaging service) with an accuracy rate of 93 %, in best condition. On the other part, paper also focus on design of Human-mobile interface (HMI), which is useful even for the illiterate farmers, to automatically monitor their field at any stage by just a mobile click. Android is currently used to run this system which can be easily extended to other mobile operating systems. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s11760-015-0751-y | Signal, Image and Video Processing |
Keywords | Field | DocType |
Android, Human mobile interaction (HMI), Multi-resolution and multi-directional transform, Mobile disease diagnosis system, Pattern recognition, Plant leaf | Gabor wavelet transform,Computer vision,Feature vector,Mobile client,Android (operating system),Feature extraction,Artificial intelligence,Gray level,Mobile vision,Mathematics | Journal |
Volume | Issue | ISSN |
10 | 2 | 1863-1711 |
Citations | PageRank | References |
9 | 0.72 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shitala Prasad | 1 | 55 | 7.28 |
| Sateesh K. Peddoju | 2 | 72 | 10.60 |
| Debashis Ghosh | 3 | 496 | 49.16 |