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日光温室作物冠层夏季光照强度与空气温湿度
引用本文:吕欢欢,牛源艺,韩雨晓,李顺达,张漫,李寒. 日光温室作物冠层夏季光照强度与空气温湿度[J]. 农业机械学报, 2024, 55(5): 368-378
作者姓名:吕欢欢  牛源艺  韩雨晓  李顺达  张漫  李寒
作者单位:中国农业大学
基金项目:国家自然科学基金项目(31971786)
摘    要:针对温室内不同时间、位置的环境参数存在变异性,且随天气与季节变化,日光温室冠层光照强度、空气温度和空气相对湿度的分布差异性问题,构建了基于无线传感器网络的环境监测系统。环境感知节点部署在作物冠层位置,集成了光照强度、空气温湿度等传感器。首先,基于实时采集的温室环境数据,采用反距离加权算法进行插值分析,得到环境参数的离散曲面;其次,通过基于质心坐标的K-means聚类算法,得到了温室内连通及非连通区域的代表性特征点;最后,采用半变异函数与变异系数方法对温室环境的空间变异性与时间变异性进行分析。实验结果表明,夏季日光温室在下午表现为高温与高光照,08:00、16:00的光照强度分别为12:00的24.2%、72.9%,08:00的空气温度(27.7℃)较12:00、16:00低约6.0℃,对应的空气湿度(90%)高约30%。晴天最大光照强度分别为阴天、雨天的1.4倍和4.6倍,晴天、阴天最高空气温度高于雨天(29.5℃)约6℃,最小空气相对湿度远低于雨天(84%)。夏季日光温室晴天与阴天表现为高温和低湿,雨天表现为高湿和低光照。各环境参数中,光照强度的空间变异性最强,变程为10.34m。空气温湿度的空间变异性较弱,整体分布均匀。光照强度、空气温度和空气相对湿度的时间变异性均为中等变异程度。环境参数的特征点及时空变化规律有助于日光温室传感器的高效部署,为揭示作物与环境的交互作用提供了基础。

关 键 词:日光温室;光照强度;空气温湿度;时空变化;K-means;无线传感器网络
收稿时间:2023-09-21

Spatiotemporal Variation of Crop-canopy Light Intensity and Air Temperature and Humidity in Summer Solar Greenhouse
Lü Huanhuan,NIU Yuanyi,HAN Yuxiao,LI Shund,ZHANG Man,LI Han. Spatiotemporal Variation of Crop-canopy Light Intensity and Air Temperature and Humidity in Summer Solar Greenhouse[J]. Transactions of the Chinese Society for Agricultural Machinery, 2024, 55(5): 368-378
Authors:Lü Huanhuan  NIU Yuanyi  HAN Yuxiao  LI Shund  ZHANG Man  LI Han
Affiliation:China Agricultural University
Abstract:The solar greenhouse structure helps indoor lighting and heat storage, ensuring the normal growth of crops. However, there is variability in environmental parameters at different times and locations in the greenhouse, and they vary with weather and seasons. To reveal the spatiotemporal variation patterns of light intensity, air temperature and humidity in the canopy, an environmental monitoring system based on wireless sensor networks was built. Nodes with environmental information sensing functions such as light intensity, air temperature and humidity were deployed in the crop canopy to analyze the temporal and spatial variation of environmental parameters. Firstly, the inverse distance weighted algorithm was used to construct discrete data surface of canopy light intensity, air temperature and humidity. Secondly, the K-means clustering based on centroid coordinates of the interpolation results was carried out to calculate the position of the feature points of connected and non-connected areas in the greenhouse. Finally, the semi-variogram method was used to analyze the spatiotemporal variability of the monitoring parameters of the interpolation nodes. The experimental results showed that the solar greenhouse in summer presented high temperature and high light in the afternoon. The light intensity at 08:00 and 16:00 was 24.2% and 72.9% of that at 12:00, respectively. The air temperature at 08:00 (27.7℃) was about 6℃ lower than that at 12:00 and 16:00, and the air humidity (90%) was about 30% higher. The maximum light intensity in sunny day was 1.4 times of that in cloudy day and 4.6 times of that in rainy day. The maximum air temperature in sunny day and cloudy day was about 6℃ higher than that in rainy day (29.5℃), and the minimum air humidity were lower than that in rainy day (84%). The solar greenhouse presented high temperature and low humidity in both sunny day and cloudy day, and high humidity and low light in rainy day. The range of light intensity was 10.34m, and the spatial variability was strong. The spatial variability of air temperature and humidity was weak, and the overall distribution was relatively uniform. The temporal variability of light intensity, air temperature, and air humidity were moderate. The characteristic points and spatial and temporal variation patterns of environmental parameters contributed to the efficient deployment of solar greenhouse sensors and provided a basis for revealing the interaction between crops and the environment.
Keywords:solar greenhouse   light intensity   air temperature and humidity   spatiotemporal variation   K-means   WSN
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