Author
Listed:
- Linlin Sun
(College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, China
Shandong Provincial Engineering Laboratory of Agricultural Equipment Intelligence, 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
Shandong Provincial Engineering Laboratory of Agricultural Equipment Intelligence, Taian 271018, China)
- Jinxing Wang
(College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, China
Shandong Provincial Key Laboratory of Facility Horticulture Intelligent Production Technology and Equipment, 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
College of Horticultural Science and Engineering, Shandong Agricultural University, Taian 271018, China)
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.
Suggested Citation
Linlin Sun & Xiubo Chen & Zixu Chen & Linlong Jing & Jinxing Wang & Xinpeng Cao & Shenghui Fu & Yuanmao Jiang & Hongjian Zhang, 2024.
"Crushing Force Prediction Method of Controlled-Release Fertilizer Based on Particle Phenotype,"
Agriculture, MDPI, vol. 14(12), pages 1-20, December.
Handle:
RePEc:gam:jagris:v:14:y:2024:i:12:p:2235-:d:1538367
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