Modeling the Mechanical Properties of Root–Substrate Interaction with a Transplanter Using Artificial Neural Networks

Zhiwei Tian, Ang Gao, Wei Ma (), Huanyu Jiang (), Dongping Cao, Weizi Wang, Jianping Qian and Lijia Xu
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Zhiwei Tian: Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu 610213, China
Ang Gao: College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an 271017, China
Wei Ma: Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu 610213, China
Huanyu Jiang: Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Dongping Cao: Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu 610213, China
Weizi Wang: Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu 610213, China
Jianping Qian: Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Lijia Xu: College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625014, China

Agriculture, 2024, vol. 14, issue 5, 1-12

Abstract: The mechanical properties of a plug seedling substrate determine whether it will crush during the transplantation, thereby affecting the integrity of the root system and the survival rate of transplanted seedlings. In this study, we measured eight morphological parameters of pepper seedlings using machine vision and physical methods, and the corresponding substrate mechanical parameters of the plug seedlings were tested using a texture analyzer. Based on the experimental data, a BPNN framework was constructed to predict the substrate mechanical properties of plug seedlings at different growth stages. The results indicate that the BPNN with a framework of [8, 15, 15, 1] exhibits higher R 2 and lower errors. The mean absolute error ( MAE ), mean squared error ( MSE ), and mean absolute percentage error ( MAPE ) values are 7.669, 88.842, and 9.076%, respectively, with an R 2 of 0.867. The average prediction accuracy of 20 test data set is 90.472%. Finally, predictions and experimental validations were conducted on the substrate mechanical properties of seedlings grown for 47 days. The results revealed that the BPNN achieved an average prediction accuracy of 93.282%. Additionally, it exhibited faster speed and lower computational costs. This study provides a reference for the non-intrusive estimation of substrate mechanical properties in plug seedlings and the design and optimization of transplanting an end-effector.

Keywords: plug seedling; mechanical properties; BPNN; prediction (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: 2024
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