Designing an Interactively Cognitive Humanoid Field-Phenotyping Robot for In-Field Rice Tiller Counting

Yixiang Huang, Pengcheng Xia, Liang Gong (), Binhao Chen, Yanming Li and Chengliang Liu
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Yixiang Huang: School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Pengcheng Xia: School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Liang Gong: School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Binhao Chen: School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Yanming Li: School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Chengliang Liu: School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Agriculture, 2022, vol. 12, issue 11, 1-15

Abstract: Field phenotyping is a crucial process in crop breeding, and traditional manual phenotyping is labor-intensive and time-consuming. Therefore, many automatic high-throughput phenotyping platforms (HTPPs) have been studied. However, existing automatic phenotyping methods encounter occlusion problems in fields. This paper presents a new in-field interactive cognition phenotyping paradigm. An active interactive cognition method is proposed to remove occlusion and overlap for better detectable quasi-structured environment construction with a field phenotyping robot. First, a humanoid robot equipped with image acquiring sensory devices is designed to contain an intuitive remote control for field phenotyping manipulations. Second, a bio-inspired solution is introduced to allow the phenotyping robot to mimic the manual phenotyping operations. In this way, automatic high-throughput phenotyping of the full growth period is realized and a large volume of tiller counting data is availed. Third, an attentional residual network (AtResNet) is proposed for rice tiller number recognition. The in-field experiment shows that the proposed method achieves approximately 95% recognition accuracy with the interactive cognition phenotyping platform. This paper opens new possibilities to solve the common technical problems of occlusion and observation pose in field phenotyping.

Keywords: phenotyping; agricultural robot; tiller counting; deep learning; residual network (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: 2022
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