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This chapter explores the transformative role of artificial intelligence (AI) in predictive modeling of pest dynamics, offering a proactive approach to agricultural pest management systems. Traditional pest control methods are often reactive, chemically intensive, and pose significant environmental and economic challenges. In contrast, AI-driven predictive analytics leverages historical data, real-time sensor inputs, and satellite imagery to forecast pest outbreaks before they occur. The integration of AI with ecological and statistical models enables precise identification, monitoring, and intervention strategies, thereby minimizing crop damage and reducing pesticide usage. Smart farming technologies, including robotics and drones, enhance decision-making, computer use, and operational efficiency. This chapter also examines the economic, environmental, and social implications of AI adoption in agriculture, highlighting both its potential benefits and challenges. Case studies and real-world applications have illustrated how AI can support Integrated Pest Management (IPM) systems, improve sustainability, and foster resilience in agroecosystems. Finally, this chapter describes the shift from reactive to predictive pest management systems, highlighting the necessity for intelligent data-driven solutions to confirm food security and ecological balance in the face of global agricultural demands.

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Predictive Modeling in Pest Dynamics: Forecasting Infestations with AI

  • 章节

  • 发表日期：

    2026年

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  • Kamali Joshna Thayalan,
  • Manivannan Govindasamy,
  • Sathyakala Shanmugam,
  • Ponmurugan Karuppiah,
  • Uma Eswaranpillai

摘要

This chapter explores the transformative role of artificial intelligence (AI) in predictive modeling of pest dynamics, offering a proactive approach to agricultural pest management systems. Traditional pest control methods are often reactive, chemically intensive, and pose significant environmental and economic challenges. In contrast, AI-driven predictive analytics leverages historical data, real-time sensor inputs, and satellite imagery to forecast pest outbreaks before they occur. The integration of AI with ecological and statistical models enables precise identification, monitoring, and intervention strategies, thereby minimizing crop damage and reducing pesticide usage. Smart farming technologies, including robotics and drones, enhance decision-making, computer use, and operational efficiency. This chapter also examines the economic, environmental, and social implications of AI adoption in agriculture, highlighting both its potential benefits and challenges. Case studies and real-world applications have illustrated how AI can support Integrated Pest Management (IPM) systems, improve sustainability, and foster resilience in agroecosystems. Finally, this chapter describes the shift from reactive to predictive pest management systems, highlighting the necessity for intelligent data-driven solutions to confirm food security and ecological balance in the face of global agricultural demands.