Yang Taoqing, Zheng Xia, Xiao Hongwei, Shan Chunhui, Zhang Jikai
College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China.
Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi, China.
Front Plant Sci. 2024 Mar 4;15:1289783. doi: 10.3389/fpls.2024.1289783. eCollection 2024.
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 () 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 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.
为实时监测农产品干燥过程中的水分含量，本研究应用了一种将多传感器融合与卷积神经网络（CNN）相结合的模型进行水分含量在线检测。本研究构建了一个多传感器数据采集平台，并建立了一个以负载传感器、风速传感器、温度传感器的原始监测数据以及托盘位置为输入，物料重量为输出的CNN预测模型。将该模型的预测性能与线性偏最小二乘回归（PLSR）模型和非线性支持向量机（SVM）模型的预测性能进行了比较。基于该模型建立了一个水分含量在线检测系统。模型性能比较结果表明，CNN预测模型具有最优的预测效果，决定系数（）和均方根误差（RMSE）分别为0.9989和6.9，显著优于其他两个模型。验证实验结果表明，该检测系统满足农产品干燥过程中水分含量在线检测的要求。决定系数和RMSE分别为0.9901和1.47，表明多传感器融合与CNN相结合的模型在农产品干燥过程水分含量在线检测中性能良好。本研究建立的水分含量在线检测系统对于研究新型干燥工艺和实现干燥设备的智能化发展具有重要意义。它也为农产品干燥过程中其他指标的在线检测提供了参考。
