Toward adaptive and high‑precision integrated pest management in the big data era

Area of Science:

• Agricultural Science
• Computer Science
• Environmental Science

Background:

• Traditional Integrated Pest Management (IPM) models have limitations in predictability, cost, and specificity.
• Advancements in machine learning (ML) offer improved predictive capabilities for pest forecasting.
• Internet of Things (IoT) technologies facilitate continuous data acquisition for ML models.

Purpose of the Study:

• To explore the integration of ML and IoT for enhanced IPM strategies.
• To leverage multimodal modeling for diverse data integration in pest management.
• To develop adaptive, high-precision IPM solutions for the big data era.

Main Methods:

• Utilizing ML for short-term pest forecasting based on IoT-acquired data.
• Employing multimodal modeling to integrate diverse data sources, including text.
• Implementing adaptive optimization for feedback-driven IPM strategies.

Main Results:

• ML and IoT enable reliable short-term pest forecasting.
• Continuous monitoring and outcome metrics support feedback-driven IPM optimization.
• Multimodal approaches facilitate targeted, minimally chemical-dependent interventions.

Conclusions:

• The convergence of IoT, ML, and multimodal modeling creates adaptive, high-precision IPM.
• Actionable IPM insights can be delivered to diverse stakeholders, from large enterprises to smallholder farms.
• This technological integration advances sustainable and economically viable pest management practices.