AI-enhanced prediction models to explore reproductive adaptations in cluster-living arthropods: A case study in Haplodiploid spider mites (Tetranychus ludeni)

Abstract

Effective management of Haplodiploid spider mites (Tetranychus ludeni) is a significant agricultural challenge due to their complex, non-linear population dynamics, which are poorly handled by traditional Integrated Pest Management (IPM) strategies based on static thresholds. This research presents the development and evaluation of an integrated AI-driven system for proactive pest management. The system architecture combines three core modules: (1) a computer vision module, using a lightweight YOLOv8 Nano model, for automated mite detection; (2) a forecasting module to predict population trends using machine learning and deep learning; and (3) a web-based advisory system to provide agricultural instructors with actionable insights based on a static Economic Injury Level (EIL) derived from observational data. This research documents the initial exploratory data analysis, synthetic data generation, and a comparative evaluation of several predictive models. The claim for the tuned XGBoost model lies in a very good performance with a value of R2 = 0.8351 for the intermediate count prediction. Long Short-Term Memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) time-series models have been tested to forecast population counts over time. The results explicitly show that, though there is potential in using machine learning for such forecasting tasks, there are considerable challenges involved in applying time-series models that need further refinement. The time series features introduced XGBoost model lies in a very good performance with a value of R2 = 0.9995 for the future count prediction (trained on synthetic data tested on real data). However, this project has amalgamated these components toward building and validating a coherent tool driving enhanced agricultural sustainability practice through minimized crop loss arising from optimised pest control intervention.