Your browser doesn't support javascript.
loading
Moisture content online detection system based on multi-sensor fusion and convolutional neural network.
Yang, Taoqing; Zheng, Xia; Xiao, Hongwei; Shan, Chunhui; Zhang, Jikai.
Afiliación
  • Yang T; College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China.
  • Zheng X; Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi, China.
  • Xiao H; Key Laboratory of Modern Agricultural Machinery Corps, Shihezi, China.
  • Shan C; College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China.
  • Zhang J; Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi, China.
Front Plant Sci ; 15: 1289783, 2024.
Article en En | MEDLINE | ID: mdl-38501134
ABSTRACT
To monitor the moisture content of agricultural products in the drying process in real time, this study applied a model combining multi-sensor fusion and convolutional neural network (CNN) to moisture content online detection. This study built a multi-sensor data acquisition platform and established a CNN prediction model with the raw monitoring data of load sensor, air velocity sensor, temperature sensor, and the tray position as input and the weight of the material as output. The model's predictive performance was compared with that of the linear partial least squares regression (PLSR) and nonlinear support vector machine (SVM) models. A moisture content online detection system was established based on this model. Results of the model performance comparison showed that the CNN prediction model had the optimal prediction effect, with the determination coefficient (R2) and root mean square error (RMSE) of 0.9989 and 6.9, respectively, which were significantly better than those of the other two models. Results of validation experiments showed that the detection system met the requirements of moisture content online detection in the drying process of agricultural products. The R2 and RMSE were 0.9901 and 1.47, respectively, indicating the good performance of the model combining multi-sensor fusion and CNN in moisture content online detection for agricultural products in the drying process. The moisture content online detection system established in this study is of great significance for researching new drying processes and realizing the intelligent development of drying equipment. It also provides a reference for online detection of other indexes in the drying process of agricultural products.
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Plant Sci Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Plant Sci Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza