Autonomous Robotic System to Prune Sweet Pepper Leaves Using Semantic Segmentation with Deep Learning and Articulated Manipulator

Author:

Giang Truong Thi Huong1ORCID,Ryoo Young-Jae2ORCID

Affiliation:

1. Department of Information Technology, Tay Nguyen University, Buonmathuot 63161, Vietnam

2. Department of Electrical and Control Engineering, Mokpo National University, Muan-gun 58554, Republic of Korea

Abstract

This paper proposes an autonomous robotic system to prune sweet pepper leaves using semantic segmentation with deep learning and an articulated manipulator. This system involves three main tasks: the perception of crop parts, the detection of pruning position, and the control of the articulated manipulator. A semantic segmentation neural network is employed to recognize the different parts of the sweet pepper plant, which is then used to create 3D point clouds for detecting the pruning position and the manipulator pose. Eventually, a manipulator robot is controlled to prune the crop part. This article provides a detailed description of the three tasks involved in building the sweet pepper pruning system and how to integrate them. In the experiments, we used a robot arm to manipulate the pruning leaf actions within a certain height range and a depth camera to obtain 3D point clouds. The control program was developed in different modules using various programming languages running on the ROS (Robot Operating System).

Funder

Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry

Korea Smart Farm R&D Foundation (KosFarm) via the Smart Farm Innovation Technology Development Program

Ministry of Agriculture, Food and Rural Affairs (MAFRA) and Ministry of Science and ICT (MSIT), Rural Development Administration

Publisher

MDPI AG

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