College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China.
College of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
Sensors (Basel). 2022 Aug 21;22(16):6287. doi: 10.3390/s22166287.
Moisture content (MC) detection plays a vital role in the monitoring and management of living trees. Its measurement accuracy is of great significance to the progress of the forestry informatization industry. Targeting the drawbacks of high energy consumption, low practicability, and poor sustainability in the current field of living tree MC detection, this work designs and implements an ultra-high-frequency radio frequency identification (UHF RFID) sensor system based on a deep learning model, with the main goals of non-destructive testing and high-efficiency recognition. The proposed MC diagnostic system includes two passive tags which should be mounted on the trunk and one remote data processing terminal. First, the UHF reader collects information from the living trees in the forest; then, an improved online sequential parallel extreme learning machine algorithm (OS-PELM) is proposed and trained to establish a specific MC prediction model. This mechanism could self-adjust its neuron network structure according to the features of the data input. The experimental results show that, for the entire living tree dataset, the MC prediction model based on the OS-PELM algorithm can identify the MC level with a root-mean-square error (RMSE) of no more than 0.055 within a measurement range of 1.2 m. Compared with the results predicted by other algorithms, the mean absolute error (MAE) and RMSE are 0.0225 and 0.0254, respectively, which are better than the ELM and OS-ELM algorithms. Comparisons also prove that the prediction model has the advantages of high precision, strong robustness, and broad applicability. Therefore, the designed MC detection system fully meets the demand of forestry Artificial Intelligence of Things.
含水率（MC）检测在监测和管理活立木中起着至关重要的作用。其测量精度对林业信息化产业的发展具有重要意义。针对当前活立木 MC 检测领域存在的能耗高、实用性低、可持续性差等问题，本工作设计并实现了一种基于深度学习模型的超高频射频识别（UHF RFID）传感器系统，主要目标是实现无损检测和高效识别。所提出的 MC 诊断系统包括两个应安装在树干上的无源标签和一个远程数据处理终端。首先，UHF 读取器从森林中的活立木中收集信息；然后，提出并训练了一种改进的在线顺序并行极端学习机算法（OS-PELM），以建立特定的 MC 预测模型。该机制可以根据输入数据的特征自动调整其神经元网络结构。实验结果表明，对于整个活立木数据集，基于 OS-PELM 算法的 MC 预测模型可以在 1.2 m 的测量范围内以不超过 0.055 的均方根误差（RMSE）识别 MC 水平。与其他算法的预测结果相比，平均绝对误差（MAE）和 RMSE 分别为 0.0225 和 0.0254，优于 ELM 和 OS-ELM 算法。比较还证明了预测模型具有高精度、强鲁棒性和广泛适用性。因此，设计的 MC 检测系统完全满足林业物联网的需求。
