Weed recognition framework for robotic precision farming
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.
2016 IEEE International Conference on Imaging Systems and Techniques (IST)