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Research on Vegetable Pest Warning System Based on Multidimensional Big Data

机译：基于多维大数据的蔬菜病虫害预警系统研究

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Pest early warning technology is part of the prerequisite for the timely and effective control of pest outbreaks. Traditional pest warning system with artificial mathematical statistics, radar, and remote sensing has some deficiency in many aspects, such as higher cost, weakness of accuracy, low efficiency, and so on. In this study, Pest image data was collected and information about four major vegetable pests (Bemisia tabaci (Gennadius), Phyllotreta striolata (Fabricius), Plutella xylostella (Linnaeus), and Frankliniella occidentalis (Pergande) (Thysanoptera, Thripidae)) in southern China was extracted. A multi-sensor network system was constructed to collect small-scale environmental data on vegetable production sites. The key factors affecting the distribution of pests were discovered by multi-dimensional information, such as soil, environment, eco-climate, and meteorology of vegetable fields, and finally, the vegetable pest warning system that is based on multidimensional big data (VPWS-MBD) was implemented. Pest and environmental data from Guangzhou Dongsheng Bio-Park were collected from June 2017 to February 2018. The number of pests is classified as level I (0–56), level II (57–131), level III (132–299), and level IV (above 300) by K-Means algorithm. The Pearson correlation coefficient and the grey relational analysis algorithm were used to calculate the five key influence factors of rainfall, soil temperature, air temperature, leaf surface humidity, and soil moisture. Finally, Back Propagation (BP) Neural Network was used for classification prediction. The result shows: I-level warning accuracy was 96.14%, recall rate was 97.56%; II-level pest warning accuracy was 95.34%, the recall rate was 96.45%; III-level pest warning accuracy of 100%, the recall rate was 96.28%; IV-level pest warning accuracy of 100%, recall rate was 100%. It proves that the early warning system can effectively predict vegetable pests and achieve the early warning of vegetable pest’s requirements, with high availability.
机译：虫害预警技术是及时有效控制虫害暴发的前提条件之一。传统的带有人工数理统计，雷达和遥感技术的虫害预警系统在很多方面都存在不足，例如成本较高，准确性较弱，效率较低等。在这项研究中，收集了中国南部的害虫图像数据和四种主要蔬菜害虫（烟粉虱（Bemisia tabaci（Gennadius）），Phyllotreta striolata（Fabricius），小菜蛾（Plutella xylostella）（Linnaeus）和西方富兰克氏菌（Frankliniella occidentalis）（Pergande）（Thysanoptera，Thripidae）的信息。被提取。构建了一个多传感器网络系统来收集蔬菜生产现场的小规模环境数据。通过土壤，环境，生态气候和菜地气象等多维信息，发现了影响害虫分布的关键因素，最后，基于多维大数据的蔬菜害虫预警系统（VPWS- MBD）已实施。从2017年6月至2018年2月收集了广州东升生物园的有害生物和环境数据。有害生物的数量分为I级（0-56），II级（57-131），III级（132-299），和K-Means算法的IV级（高于300）。利用皮尔逊相关系数和灰色关联分析算法计算出降雨，土壤温度，气温，叶面湿度和土壤湿度的五个主要影响因素。最后，将反向传播（BP）神经网络用于分类预测。结果表明：I级警告准确率为96.14％，召回率为97.56％；二级虫害预警准确率为95.34％，召回率为96.45％；三级虫害预警准确度为100％，召回率为96.28％； IV级虫害预警准确度为100％，召回率为100％。事实证明，该预警系统可以有效地预测蔬菜病虫害，并达到对蔬菜病虫害需求的预警，并且可用性很高。

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