基于多传感器融合和卷积神经网络的水分含量在线检测系统.
Taoqing Yang1,2,3, Xia Zheng1,2,3, Hongwei Xiao4
1College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China.
Frontiers in plant science
|March 19, 2024
概括
这项研究开发了一种使用多传感器融合和卷积神经网络 (CNN) 的农产品实时水分含量检测系统. 该CNN模型显著优于其他方法,使智能干燥设备的开发.
科学领域:
- 农业工程 农业工程
- 机器学习 机器学习
- 传感器和仪器仪表的使用
背景情况:
- 在干燥过程中实时监测农产品的水分含量对于质量控制和工艺优化至关重要.
- 传统方法往往缺乏动态干燥过程所需的精度和速度.
- 干燥设备的智能开发需要先进的在线检测能力.
研究的目的:
- 开发和验证一套用于在干燥过程中在农产品中在线检测水分的新系统.
- 应用多传感器融合与卷积神经网络 (CNN) 结合,以提高预测准确度.
- 将CNN模型的性能与PLSR和SVM等传统回归模型进行比较.
主要方法:
- 构建了一个多传感器数据采集平台,从负载,空气速度和温度传感器以及托盘位置收集数据.
- 建立了一个卷积神经网络 (CNN) 预测模型,使用传感器数据作为输入来预测材料重量,即湿度含量的代理.
- 严格比较CNN模型的预测性能与线性部分最小平方回归 (PLSR) 和支持向量机 (SVM) 模型.
主要成果:
- CNN预测模型表现出优异的性能,确定系数 (R) 为0.9989和根平均平方误差 (RMSE) 为6.9.
- 验证实验证实了该系统的有效性,在线检测水分含量时获得了0.9901的R和1.47的RMSE.
- 开发的系统在农产品干燥监测的准确性和可靠性方面明显优于PLSR和SVM模型.
结论:
- 多传感器融合和CNN的整合提供了一个高度准确和可靠的方法,用于在线检测农产品中的水分含量.
- 开发的系统满足了在干燥过程中实时监控的严格要求.
- 这项研究对推进干燥过程研究,智能干燥设备以及在线检测其他农产品参数具有重大意义.
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