Department of Electrical Engineering, City University of Hong Kong, Hong Kong 999077, China.
Sensors (Basel). 2019 Jul 15;19(14):3115. doi: 10.3390/s19143115.
Improperly grown trees may cause huge hazards to the environment and to humans, through e.g., climate change, soil erosion, etc. A proximity environmental feature-based tree health assessment (PTA) scheme is proposed to prevent these hazards by providing guidance for early warning methods of potential poor tree health. In PTA development, tree health is defined and evaluated based on proximity environmental features (PEFs). The PEF takes into consideration the seven surrounding ambient features that strongly impact tree health. The PEFs were measured by the deployed smart sensors surrounding trees. A database composed of tree health and relative PEFs was established for further analysis. An adaptive data identifying (ADI) algorithm is applied to exclude the influence of interference factors in the database. Finally, the radial basis function (RBF) neural network (NN), a machine leaning algorithm, has been identified as the appropriate tool with which to correlate tree health and PEFs to establish the PTA algorithm. One of the salient features of PTA is that the algorithm can evaluate, and thus monitor, tree health remotely and automatically from smart sensor data by taking advantage of the well-established internet of things (IoT) network and machine learning algorithm.
生长不当的树木可能会通过气候变化、土壤侵蚀等给环境和人类带来巨大危害。本研究提出了一种基于邻近环境特征的树木健康评估（PTA）方案，通过为潜在不良树木健康的预警方法提供指导，来预防这些危害。在 PTA 的开发中，基于邻近环境特征（PEFs）来定义和评估树木健康。PEF 考虑了对树木健康有强烈影响的七个周围环境特征。PEFs 通过部署在树木周围的智能传感器进行测量。为了进一步分析，建立了一个包含树木健康和相关 PEFs 的数据库。应用自适应数据识别（ADI）算法排除数据库中干扰因素的影响。最后，径向基函数（RBF）神经网络（NN），一种机器学习算法，被确定为合适的工具，用于关联树木健康和 PEFs ，从而建立 PTA 算法。PTA 的一个显著特点是，该算法可以利用成熟的物联网（IoT）网络和机器学习算法，从智能传感器数据中远程和自动评估树木健康并进行监测。
