Modeling, Remote Sensing, and Machine Learning in Pest Management

Pest management has seen significant advancements in integrating modeling, remote sensing, and machine learning (ML). Traditional pest management strategies often relied on empirical methods and manual monitoring, which could be labor-intensive and less precise. Recent ML and computational modeling developments have revolutionized this field, offering more efficient, accurate, and sustainable solutions. Despite these advancements, there remain critical gaps in our understanding of how to best leverage these technologies for pest control. Ongoing debates focus on the scalability of ML models, the accuracy of simulations in diverse ecological settings, and the ethical implications of automated pest management systems. Significant studies have demonstrated the potential of remote sensing to detect damage caused by insects and ML-based models in predicting pest outbreaks and optimizing pesticide use. Yet, there is a pressing need for more comprehensive investigations to refine these models and address existing limitations.

This Research Topic aims to present original research concerning computer models and simulation results, as well as the use of remote sensing with RGB, Multispectral, and Hyperspectral, as well as machine learning research based on entomological studies. The primary objective is to explore how these advanced technologies can be harnessed to develop more effective and sustainable pest management strategies. Specific questions to be addressed include: How can ML improve the detection accuracy of pest populations? What are the most effective modeling techniques for simulating pest behavior? How can remote sensing and/or machine learning algorithms be optimized for real-time pest management applications?

To gather further insights into the application of modeling, remote sensing, and ML in pest management, we welcome articles addressing, but not limited to, the following themes:
- Modeling and simulation
- Computer vision
- Optimization methods
- Computational processes
- Automatic programming
- RGB, multispectral and hyperspectral imaging
- Machine learning
This Research Topic aims to present original research concerning computer models and simulation results, as well as artificial intelligence research based on entomological studies. The primary objective is to explore how these advanced technologies can be harnessed to develop more effective and sustainable pest management strategies. Specific questions to be addressed include: How can AI improve the accuracy of pest population predictions? What are the most effective modeling techniques for simulating pest behavior? How can machine learning algorithms be optimized for real-time pest management applications?

To gather further insights in the application of modeling and AI in pest management, we welcome articles addressing, but not limited to, the following themes:
- Modeling and simulation
- Computer vision
- Optimization methods
- Computational processes
- Automatic programming
- Machine learning

Keywords: Modeling, AI, Computer models, Simulations, Entomology, Machine learning, Programming, Artificial Intelligence

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.