Wu Yin, Yang Zenan, Liu Yanyi
College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China.
Micromachines (Basel). 2023 Jul 8;14(7):1395. doi: 10.3390/mi14071395.
The rise of Internet of Things (IoT) technology has moved the digital world in a new direction and is considered the third wave of the information industry. To meet the current growing demand for food, the agricultural industry should adopt updated technologies and smart agriculture based on the IoT which will strongly enable farmers to reduce waste and increase productivity. This research presents a novel system for the application of IoT technology in agricultural soil measurements, which consists of multiple sensors (temperature and moisture), a micro-processor, a microcomputer, a cloud platform, and a mobile phone application. The wireless sensors can collect and transmit soil information in real time with a high speed, while the mobile phone app uses the cloud platform as a monitoring center. A low power consumption is specified in the hardware and software, and a modular power supply and time-saving algorithm are adopted to improve the energy effectiveness of the nodes. Meanwhile, a novel soil information prediction strategy was explored based on the deep Q network (DQN) reinforcement learning algorithm. Following the weighted combination of a bidirectional long short-term memory, online sequential extreme learning machine, and parallel extreme machine learning, the DQN Bi-OS-P prediction model was obtained. The proposed data acquisition system achieved a long-term stable and reliable collection of time-series soil data with equal intervals and provided an accurate dataset for the precise diagnosis of soil information. The RMSE, MAE, and MAPE of the DQN Bi-OS-P were all reduced, and the R2 was improved by 0.1% when compared to other methods. This research successfully implemented the smart soil system and experimentally showed that the time error between the value displayed on the mobile phone app and its exact acquisition moment was no more than 3 s, proving that mobile applications can be effectively used for the real-time monitoring of soil quality and conditions in wireless multi-sensing based on the Internet of Things.
物联网（IoT）技术的兴起将数字世界推向了一个新的方向，被视为信息产业的第三次浪潮。为了满足当前对粮食不断增长的需求，农业产业应采用基于物联网的更新技术和智慧农业，这将有力地帮助农民减少浪费并提高生产力。本研究提出了一种将物联网技术应用于农业土壤测量的新型系统，该系统由多个传感器（温度和湿度）、一个微处理器、一台微型计算机、一个云平台和一个手机应用程序组成。无线传感器能够高速实时收集和传输土壤信息，而手机应用程序则将云平台用作监测中心。在硬件和软件方面都规定了低功耗，并采用模块化电源和省时算法来提高节点的能源效率。同时，基于深度Q网络（DQN）强化学习算法探索了一种新型的土壤信息预测策略。经过双向长短期记忆、在线序列极限学习机和平行极限机器学习的加权组合，得到了DQN Bi-OS-P预测模型。所提出的数据采集系统实现了等间隔时间序列土壤数据的长期稳定可靠采集，并为土壤信息的精确诊断提供了准确的数据集。与其他方法相比，DQN Bi-OS-P的均方根误差（RMSE）、平均绝对误差（MAE）和平均绝对百分比误差（MAPE）均有所降低，决定系数（R2）提高了0.1%。本研究成功实现了智能土壤系统，并通过实验表明手机应用程序显示的值与其精确采集时刻之间的时间误差不超过3秒，证明了移动应用程序可有效地用于基于物联网的无线多传感土壤质量和状况的实时监测。
