Name
Abstract | ||
|---|---|---|
The primary focus of this paper is to detect disease and estimate its stage for a cotton plant using images. Most disease symptoms are reflected on the cotton leaf. Unlike earlier approaches, the novelty of the proposal lies in processing images captured under uncontrolled conditions in the field using normal or a mobile phone camera by an untrained person. Such field images have a cluttered background making leaf segmentation very challenging. The proposed work use two cascaded classifiers. Using local statistical features, first classifier segments leaf from the background. Then using hue and luminance from HSV colour space another classifier is trained to detect disease and find its stage. The developed algorithm is a generalised as it can be applied for any disease. However as a showcase, we detect Grey Mildew, widely prevalent fungal disease in North Gujarat, India. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/DSAA.2016.81 | 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA) |
Keywords | Field | DocType |
Cotton,Agricultural,Disease detection,Severity,Natural Images | HSL and HSV,Computer vision,Segmentation,Computer science,Hue,Image segmentation,Feature extraction,Artificial intelligence,Novelty,Classifier (linguistics),Luminance | Conference |
ISBN | Citations | PageRank |
978-1-5090-5207-3 | 0 | 0.34 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aditya Parikh | 1 | 0 | 0.34 |
| Mehul S. Raval | 2 | 10 | 6.47 |
| Chandrasinh Parmar | 3 | 0 | 0.34 |
| Sanjay Chaudhary | 4 | 223 | 24.16 |