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Sensor Location Optimisation Design Based on IoT and Geostatistics in Greenhouse

文献类型： 外文期刊

作者： Liu, Yang 1 ; Liu, Xiaoyu 2 ; Dai, Xiu 1 ; Xun, Guanglian 1 ; Ren, Ni 1 ; Kang, Rui 1 ; Mao, Xiaojuan 1 ;

作者机构： 1.Jiangsu Acad Agr Sci, Informat Ctr, Nanjing 210014, Peoples R China

2.Jiangsu Vocat Coll Agr & Forestry, Jurong 212400, Peoples R China

3.Jiangsu Acad Agr Sci, Inst Agr Resources & Environm, Minist Agr & Rural Affairs, Key Lab Agroenvironm Downstream Yangtze Plain, Nanjing 210014, Peoples R China

4.ARS, US Natl Poultry Res Ctr, USDA, Athens, GA 30605 USA

关键词： Vegetable greenhouse; environmental monitoring; internet of things; sensor location design; ordinary kriging

期刊名称：INTELLIGENT AUTOMATION AND SOFT COMPUTING （ 影响因子：3.401； 五年影响因子：2.132 ）

ISSN： 1079-8587

年卷期： 2022 年 33 卷 3 期

页码：

收录情况： SCI

摘要： Environmental parameters such as air temperature (T) and air relative humidity (RH) should be intensively monitored in a greenhouse in real time. In most cases, one set of sensors is installed in the centre of a greenhouse. However, as the microclimate of a greenhouse is always heterogeneous, the sensor installation location is crucial for practical cultivation. In this study, the T and RH monitoring performance of different sensors were compared. Two types of real-time environmental sensors (Air Temperature and Humidity sensor and Activity Monitoring sensor, referred as ATH and AM) were selected and calibrated by reliable non-real-time sensors (Honest Observer By Onset sensor, referred as HOBO). The results showed that T and RH were variable in a small greenhouse area (128 m(2)). ATH had better T and RH monitoring performance than AM using HOBO as a reference (R-2 = 0.968 and 0.938 for T; 0.594 and 0.538 for RH, respectively, for ATH and AM). In terms of cost, it is more efficient to use more sets of AMs (15 sets were used in this case study) to establish an intensive monitoring system based on the Internet of Things (IoT) compared with that of ATH. Then, the optimal sensor installation location was decided using geostatistics. Based on a simulated monitoring data set, the optimal sensor installation location was determined to be 1.57 m away from the physical centre of the monitoring area. By combining IoT with geostatistics, this study offers a method for effective monitoring of environmental parameters in a practical greenhouse system with a three-step procedure.

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