Unveiling trends in forecasting models for crop pest and disease outbreaks: A systematic and scientometric analysis

Abstract

The intensifying impact of global climate change has led to a significant rise in crop pests and disease outbreaks, resulting in substantial agricultural losses worldwide. Weather-driven forecasting models are essential tools in predicting these outbreaks, enabling timely and effective intervention strategies. This study presents a comprehensive systematic review of existing literature to assess the comparative strengths and limitations of statistical versus machine learning pest forecasting models. In parallel, we conducted a detailed bibliometric analysis of 1,215 peer-reviewed studies spanning 2000–2023, sourced from the Web of Science Core Collection database. This dataset reveals a marked surge in research activity, particularly after 2019. The most significant growth is evident in key disciplines such as entomology, plant pathology, and agronomy. Geographically, the highest research output originates from Asia, Europe, and North America, where China, the United States, and India are emerging as the foremost contributors in terms of publication volume. Chinese researchers lead the list of top contributing authors, while high-impact journals such as PLOS ONE, Insects, Computers and Electronics in Agriculture, etc., emerge as central publication platforms in this domain. The analysis identifies major thematic focuses in the literature, including deep learning, convolutional neural networks, artificial neural networks, and diverse forecasting models. Frequently occurring keywords such as “regression,” “prediction,” “insects,” and “population dynamics” reflect the evolving research priorities in this field. Collectively, these studies highlight significant progress in developing predictive models for crop pests and disease outbreaks. This study provides useful insights into the field of agriculture & pest management and serves as a framework for future research. © African Association of Insect Scientists 2025.