Zobrazeno 1 - 10
of 2 222
pro vyhledávání: '"Henten, A."'
Controlling greenhouse crop production systems is a complex task due to uncertain and non-linear dynamics between crops, indoor and outdoor climate, and economics. The declining number of skilled growers necessitates the development of autonomous gre
Externí odkaz:
http://arxiv.org/abs/2410.05336
The 3D reconstruction of plants is challenging due to their complex shape causing many occlusions. Next-Best-View (NBV) methods address this by iteratively selecting new viewpoints to maximize information gain (IG). Deep-learning-based NBV (DL-NBV) m
Externí odkaz:
http://arxiv.org/abs/2410.14790
Learning from Demonstration offers great potential for robots to learn to perform agricultural tasks, specifically selective harvesting. One of the challenges is that the target fruit can be oscillating while approaching. Grasping oscillating targets
Externí odkaz:
http://arxiv.org/abs/2409.16957
This paper addresses the safe stabilization problem, focusing on controlling the system state to the origin while avoiding entry into unsafe state sets. The current methods for solving this issue rely on smooth Lyapunov and barrier functions, which d
Externí odkaz:
http://arxiv.org/abs/2409.13624
With the current demand for automation in the agro-food industry, accurately detecting and localizing relevant objects in 3D is essential for successful robotic operations. However, this is a challenge due the presence of occlusions. Multi-view perce
Externí odkaz:
http://arxiv.org/abs/2404.12963
This study presents an automated lameness detection system that uses deep-learning image processing techniques to extract multiple locomotion traits associated with lameness. Using the T-LEAP pose estimation model, the motion of nine keypoints was ex
Externí odkaz:
http://arxiv.org/abs/2401.05202
Robots are increasingly used in tomato greenhouses to automate labour-intensive tasks such as selective harvesting and de-leafing. To perform these tasks, robots must be able to accurately and efficiently perceive the plant nodes that need to be cut,
Externí odkaz:
http://arxiv.org/abs/2311.16759
In the current demand for automation in the agro-food industry, accurately detecting and localizing relevant objects in 3D is essential for successful robotic operations. However, this is a challenge due the presence of occlusions. Multi-view percept
Externí odkaz:
http://arxiv.org/abs/2311.15674
Autor:
Behailu Taye, Roma Melkamu, Fitsumbrhan Tajebe, Ana Victoria Ibarra-Meneses, Desalegn Adane, Saba Atnafu, Mohammed Adem, Gashaw Adane, Mekibib Kassa, Mezgebu Silamsaw Asres, Johan van Griensven, Saskia van Henten, Myrthe Pareyn
Publikováno v:
Parasites & Vectors, Vol 17, Iss 1, Pp 1-8 (2024)
Abstract Background Cutaneous leishmaniasis (CL) in Ethiopia and some parts of Kenya is predominantly caused by Leishmania aethiopica. While skin-slit (SS) microscopy is routinely used for CL diagnosis, more sensitive molecular tests are available. T
Externí odkaz:
https://doaj.org/article/784215cff2c84579b62ab91da5bf52a3
The agro-food industry is turning to robots to address the challenge of labour shortage. However, agro-food environments pose difficulties for robots due to high variation and occlusions. In the presence of these challenges, accurate world models, wi
Externí odkaz:
http://arxiv.org/abs/2307.05219