The Evolution of Modeling Approaches: From Statistical Models to Deep Learning for Locust and Grasshopper Forecasting

Area of Science:

• Ecology
• Computational Biology
• Data Science

Background:

• Locust and grasshopper outbreaks threaten global food security and ecosystem stability, especially in grasslands.
• Forecasting these pest outbreaks is challenging due to complex spatiotemporal interactions between environmental factors.
• Locusts exhibit larger spatial scales and connectivity than grasshoppers.

Purpose of the Study:

• To review the evolution of prediction methodologies for locust and grasshopper outbreaks.
• To assess the application and limitations of deep learning (DL) methods in ecological forecasting.
• To highlight the adaptation of DL models for grassland ecosystems and overlooked species like grasshoppers.

Main Methods:

• Comparison of traditional statistical models, classical machine learning, and deep learning architectures (DNNs, CNNs, RNNs, LSTMs, GRUs).
• Analysis of DL model performance in modeling locust population dynamics.
• Emphasis on adapting DL models for grassland ecosystems and grasshopper outbreaks.

Main Results:

• Deep learning methods show promise for ecological forecasting of insect outbreaks.
• Most research focuses on locusts, with less attention on grasshoppers in specific regions like Inner Mongolia.
• Challenges include data scarcity, limited generalizability, and low interpretability of DL models.

Conclusions:

• Explainable AI (XAI), transfer learning, and generative models (GANs) are proposed to enhance forecasting tools.
• Future research should focus on developing robust, transparent, and ecologically grounded early warning systems.
• Efficient architectures like Gated Recurrent Units (GRUs) can aid sustainable pest management in vulnerable ecosystems.