11 citations · 21 across the 8 of their papers we have counts for
4 papers · 1 filter
Transferring learned patterns from ground-based field imagery to predict UAV-based imagery for crop and weed semantic segmentation in precision crop farming
Junfeng Gao, Wenzhi Liao, David Nuyttens +3
Weed and crop segmentation is becoming an increasingly integral part of precision farming that leverages the current computer vision and deep learning technologies. Research has be…
Vision based Crop Row Navigation under Varying Field Conditions in Arable Fields
Rajitha de Silva, Grzegorz Cielniak, Junfeng Gao
Accurate crop row detection is often challenged by the varying field conditions present in real-world arable fields. Traditional colour based segmentation is unable to cater for al…
Deep learning-based Crop Row Detection for Infield Navigation of Agri-Robots
Rajitha de Silva, Grzegorz Cielniak, Gang Wang +1
Autonomous navigation in agricultural environments is challenged by varying field conditions that arise in arable fields. State-of-the-art solutions for autonomous navigation in su…
Towards Infield Navigation: leveraging simulated data for crop row detection
Rajitha de Silva, Grzegorz Cielniak, Junfeng Gao
Agricultural datasets for crop row detection are often bound by their limited number of images. This restricts the researchers from developing deep learning based models for precis…