Crushing Force Prediction Method of Controlled-Release Fertilizer Based on Particle Phenotype

Linlin Sun, Xiubo Chen, Zixu Chen, Linlong Jing, Jinxing Wang, Xinpeng Cao, Shenghui Fu, Yuanmao Jiang and Hongjian Zhang ()
Additional contact information
Linlin Sun: College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, China
Xiubo Chen: College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, China
Zixu Chen: College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, China
Linlong Jing: College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, China
Jinxing Wang: College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, China
Xinpeng Cao: College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, China
Shenghui Fu: College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, China
Yuanmao Jiang: College of Horticultural Science and Engineering, Shandong Agricultural University, Taian 271018, China
Hongjian Zhang: College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, China

Agriculture, 2024, vol. 14, issue 12, 1-20

Abstract: This study proposed a method to predict the crushing force of controlled-release fertilizer granules based on their phenotypic characteristics to prevent coating damage during production, transport, and fertilization, which could affect nutrient diffusion rates. The phenotypic features, including sphericity, particle size, and texture, of three commonly used controlled-release fertilizers were obtained using machine vision, while the crushing force was measured using a universal testing machine. A principal component analysis was applied for data reduction, and the optimal parameters for the support vector machine (SVM) were selected using particle swarm optimization (PSO) combined with k-fold cross-validation. A particle swarm optimization–support vector machine (PSO-SVM) model was then developed to predict the crushing force based on fertilizer shape features. Compared with the traditional method, the innovation of this paper is that a non-destructive prediction method is proposed, which enables high-precision predictions of the crushing force by integrating multi-dimensional phenotypic features and an intelligent optimization algorithm. Comparative tests with a random forest regression, the K-nearest neighbor, a back propagation (BP) neural network, and a long short-term memory (LSTM) neural network have demonstrated that the PSO-SVM model outperforms these methods in terms of mean absolute error, root mean square error, and correlation coefficient, underscoring its effectiveness. The proportion of predictions within the −10% to +10% error range reached 0.82, 0.82, and 0.86 for the three fertilizers, confirming the high reliability and accuracy of the PSO-SVM method for non-destructive testing.

Keywords: controlled-release fertilizer; crushing force; forecasting method; phenotypic characteristic; support vector machine (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
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/14/12/2235/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/12/2235/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:14:y:2024:i:12:p:2235-:d:1538367

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().