In this paper, we introduce a novel framework which applies known image features combined with advanced linear image representations for weed recognition. Our proposed weed recognition framework, is based on state-of-the-the art object/image categorization methods exploiting enhanced performance using advanced encoding and machine learning algorithms. The resulting system can be applied in a variety of environments, plantation or weed types. This results in a novel and generic weed control approach, that in our knowledge is unique among weed recognition methods and systems. For the experimental evaluation of our system, we introduce a challenging image dataset for weed recognition. We experimentally show that the proposed system achieves significant performance improvements in weed recognition in comparison with other known methods.
|Titel||2016 IEEE International Conference on Imaging Systems and Techniques (IST)|
|Publikationsdato||6 okt. 2016|
|Status||Udgivet - 6 okt. 2016|
|Begivenhed||IEEE International Conference on Imaging Systems and Techniques - Chania, Grækenland|
Varighed: 4 okt. 2016 → 6 okt. 2016
|Konference||IEEE International Conference on Imaging Systems and Techniques|
|Periode||04/10/2016 → 06/10/2016|