基于农业物联网的火龙果生长环境大数据分析

Big Data Analysis of Pitaya Growing Environment Based on Agricultural Internet of Things

针对农业物联网信息应用层及数据处理部分进行研究,通过对数据进行处理、挖掘、融合和应用来制定科学的管理决策,实现对农业生产过程的控制。通过在南宁市灌溉实验站火龙果实验田安装农田物联网系统,长期对实验田空气温湿度,CO_2浓度,光照强度,土壤水分,土壤温度进行实时监测。首先对试验田获取的数据进行清洗,然后根据火龙果生长环境需求,将每组环境数据进行类别标记,分为"适宜"和"不适宜"两种类别,最后通过决策树方法对数据进行自动分类,并采用正确率评价模型性能。实验结果表明,决策树分类模型测试集的正确率为99.04%,验证集的正确率为100%。表明决策树在数据分类方面具有很好的效果。

This paper mainly studied on the information application layer and data processing part of agricultural lnternet of Things （IoT）, and was aimed on scientific management decision-making through data processing, mining, integration and application to realize the control of agricultural production process. The temperature and humidity, CO2 concentration, light intensity, soil moisture and soil temperature of the experiment field were monitored in real time by an IoT system in a Pitaya experimental field located in Namiing Irrigation Experiment Station. First, the data obtained from the experimental field were cleaned, and then the environmental data of each group were marked as ＂suitable＂ and ＂unsuitable＂ according to the requirements of the growth environment of pitaya. Finally, the data were automatically classified by the decision tree method, and the correct rate was used as an indicator to evaluate the model performance. The experimental results showed that the correct rate of the testing set of decision tree was 99.04%, and the correct rate of the verification set was 100%. It showed that decision tree had a good performance in data classification.