Name
Title | ||
|---|---|---|
Visual features based boosted classification of weeds for real-time selective herbicide sprayer systems. |
Abstract | ||
|---|---|---|
•Early fusion-based framework ensures effective weed classification for selective herbicide sprayer systems.•The proposed method uses texture and shape features for effectively representing the two weed species.•Adaptive segmentation algorithmim proves robustness to illumination, noise, and motion blur, prior to features extraction.•A hybrid classifier “AdaBoost ensemble of Naïve Bayes” yields high classification accuracy compared to state-of-the-art. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.compind.2018.02.005 | Computers in Industry |
Keywords | Field | DocType |
Weed classification,Machine learning,Computer vision,Image segmentation,Selective herbicide sprayer systems,Boosted classifier for weed detection | False positive rate,Sprayer,Weed,AdaBoost,Pattern recognition,Naive Bayes classifier,Segmentation,Control engineering,Feature extraction,Artificial intelligence,Engineering,Classifier (linguistics) | Journal |
Volume | ISSN | Citations |
98 | 0166-3615 | 5 |
PageRank | References | Authors |
0.40 | 19 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jamil Ahmad | 1 | 381 | 35.12 |
| Khan Muhammad | 2 | 986 | 67.67 |
| Imran Ahmed | 3 | 10 | 3.25 |
| Wakeel Ahmad | 4 | 5 | 0.40 |
| Melvyn L. Smith | 5 | 194 | 22.20 |
| Lyndon N. Smith | 6 | 117 | 12.58 |
| Deepak Kumar Jain | 7 | 32 | 4.99 |
| Haoxiang Wang | 8 | 276 | 15.25 |
| Irfan Mehmood | 9 | 522 | 30.84 |