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

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 R2 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 R2 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.