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Research on Vegetable Pest Warning System Based on Multidimensional Big Data.

Changzhen Zhang1, Jiahao Cai2, Deqin Xiao3

  • 1Mathematics and Informatics College of South China Agricultural University, Guangzhou 510642, China. linzcz333@Gmail.com.

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A new big data system effectively predicts vegetable pests using multi-sensor data and AI. This advanced vegetable pest warning system (VPWS-MBD) improves accuracy and efficiency over traditional methods.

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Area of Science:

  • Agricultural Entomology
  • Big Data Analytics
  • Artificial Intelligence in Agriculture

Background:

  • Traditional pest warning systems face limitations in accuracy, efficiency, and cost.
  • Accurate pest prediction is crucial for timely control of agricultural outbreaks.
  • Existing methods struggle with the complexity of pest distribution factors.

Purpose of the Study:

  • To develop and implement an advanced vegetable pest warning system based on multidimensional big data (VPWS-MBD).
  • To identify key environmental factors influencing the distribution of four major vegetable pests.
  • To enhance the accuracy and efficiency of pest early warning technology.

Main Methods:

  • Collected pest image data and environmental information (soil, climate, meteorology) using a multi-sensor network.
  • Utilized K-Means algorithm for pest population classification and Pearson correlation/grey relational analysis for factor identification.
  • Employed Back Propagation (BP) Neural Network for classification prediction of pest warning levels.

Main Results:

  • The VPWS-MBD system achieved high accuracy and recall rates across all four pest warning levels (I-IV).
  • Accuracy ranged from 95.34% to 100%, with recall rates from 96.28% to 100%.
  • Identified key factors influencing pest distribution including rainfall, soil temperature, air temperature, leaf surface humidity, and soil moisture.

Conclusions:

  • The developed VPWS-MBD system demonstrates high availability and effectiveness in predicting vegetable pests.
  • This big data-driven approach significantly improves upon traditional pest warning systems.
  • The system provides a reliable tool for achieving timely and effective vegetable pest management.