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.

Deqin Xiao

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