Year: 2025
Type: conference-paper
Abstract: We propose a method to estimate a suitable approach angle for the end-effector of a tomato harvesting robot based on image data.Agricultural harvesting robots often face obstacles such as other fruits or stems around the target crop.Additionally, it is important to approach the target from a direction appropriate for harvesting, considering the shape of the end-effector.The proposed method uses a deep learning-based instance segmentation model to extract regions of fruits and stems, and estimates the suitable approach angle based on their positional relationships.We demonstrated the usefulness of the proposed method using an image dataset acquired in an actual tomato greenhouse.
Authors: Kizuna Yoshinaga, Hikaru Sato, Kazuo Ishii, Shinsuke Yasukawa
Institution: Kyushu Institute of Technology
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: bronze
OpenAlex ID: W4409913550