Mechanical Control with a Deep Learning Method for Precise Weeding on a Farm

Chung-Liang Chang, Bo-Xuan Xie and Sheng-Cheng Chung
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Chung-Liang Chang: Department of Biomechatronics Engineering, National Pingtung University of Science and Technology, Neipu 91201, Taiwan
Bo-Xuan Xie: Department of Biomechatronics Engineering, National Pingtung University of Science and Technology, Neipu 91201, Taiwan
Sheng-Cheng Chung: Department of Biomechatronics Engineering, National Pingtung University of Science and Technology, Neipu 91201, Taiwan

Agriculture, 2021, vol. 11, issue 11, 1-21

Abstract: This paper presents a mechanical control method for precise weeding based on deep learning. Deep convolutional neural network was used to identify and locate weeds. A special modular weeder was designed, which can be installed on the rear of a mobile platform. An inverted pyramid-shaped weeding tool equipped in the modular weeder can shovel out weeds without being contaminated by soil. The weed detection and control method was implemented on an embedded system with a high-speed graphics processing unit and integrated with the weeder. The experimental results showed that even if the speed of the mobile platform reaches 20 cm/s, the weeds can still be accurately detected and the position of the weeds can be located by the system. Moreover, the weeding mechanism can successfully shovel out the roots of the weeds. The proposed weeder has been tested in the field, and its performance and weed coverage have been verified to be precise for weeding.

Keywords: deep learning; machine vision; weeder; smart agriculture; mechanical control (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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