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Pest management is evolving through the integration of modeling, remote sensing, and machine learning (ML). Traditional methods have been labor-intensive and less precise, but recent advancements in ML and computational modeling are providing more efficient and sustainable solutions. Despite these innovations, gaps remain in understanding how to effectively utilize these technologies for pest control.
The focus is on enhancing detection accuracy of pest populations and optimizing real-time management applications. Significant studies highlight the potential of remote sensing and ML models in predicting pest outbreaks and improving pesticide use. Ongoing research aims to refine these technologies to address existing limitations and ethical concerns.
• Machine learning enhances pest detection and management efficiency.
• Remote sensing technology aids in identifying pest damage and outbreaks.
Machine learning is utilized to improve the accuracy of pest population predictions and optimize management strategies.
Remote sensing technologies are applied to detect damage caused by pests and monitor outbreaks.
Modeling techniques are essential for simulating pest behavior and predicting future outbreaks.
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