Zobrazeno 1 - 10
of 18
pro vyhledávání: '"de Silva, Rajitha"'
Autor:
Balasingham, Dhanushka, Samarathunga, Sadeesha, Arachchige, Gayantha Godakanda, Bandara, Anuththara, Wellalage, Sasini, Pandithage, Dinithi, Hansika, Mahaadikara M. D. J. T, de Silva, Rajitha
The advancements in precision agriculture are vital to support the increasing demand for global food supply. Precision spot spraying is a major step towards reducing chemical usage for pest and weed control in agriculture. A novel spot spraying algor
Externí odkaz:
http://arxiv.org/abs/2405.20896
Vision-based navigation systems in arable fields are an underexplored area in agricultural robot navigation. Vision systems deployed in arable fields face challenges such as fluctuating weed density, varying illumination levels, growth stages and cro
Externí odkaz:
http://arxiv.org/abs/2309.11989
Usage of purely vision based solutions for row switching is not well explored in existing vision based crop row navigation frameworks. This method only uses RGB images for local feature matching based visual feedback to exit crop row. Depth images we
Externí odkaz:
http://arxiv.org/abs/2306.05869
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 all such variations. The lack of comprehensive datasets in agricultural
Externí odkaz:
http://arxiv.org/abs/2209.14003
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 such environments require expensive hardware such as RTK-GNSS. This pape
Externí odkaz:
http://arxiv.org/abs/2209.04278
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 precision agricultural tasks involving crop row detection. We suggest the ut
Externí odkaz:
http://arxiv.org/abs/2204.01811
Towards agricultural autonomy: crop row detection under varying field conditions using deep learning
This paper presents a novel metric to evaluate the robustness of deep learning based semantic segmentation approaches for crop row detection under different field conditions encountered by a field robot. A dataset with ten main categories encountered
Externí odkaz:
http://arxiv.org/abs/2109.08247
Publikováno v:
In Computers and Electronics in Agriculture February 2024 217
Publikováno v:
Journal of Field Robotics; Oct2024, Vol. 41 Issue 7, p2299-2321, 23p
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.