Phased dynamic analysis and prediction of rice Chilo suppressalis integrating remote sensing physiological indices and environmental factors

doi:10.1016/j.jia.2026.01.043

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Phased dynamic analysis and prediction of rice Chilo suppressalis integrating remote sensing physiological indices and environmental factors

Yuchen Wu¹, Lihua Wang^1,^2#, Yanxiao Bao¹, Weiwei Sun^1,², Zhiyuan Yao³, Gang Yang^1,², Yumiao Wang^1,²

¹ School of Geography and Remote sensing, Ningbo University, Ningbo 315211, China

² Ningbo Key Laboratory of Remote Sensing and Ecological Security of Coastal Zone, Zhejiang-Germany Joint Laboratory on Remote Sensing of Coastal Ecosystem, Ningbo University, Ningbo 315211, China

³ Institute of One Health Science, School of Civil & Environmental Engineering and Geography Science, State Key laboratory for Quality and Safety of Agro-proucts Ningbo University. Ningbo 315211, China

Highlights

l Introduced LOESS smoothing and first-derivative analysis to identify the key developmental stages of Chilo suppressalis.

l Determined the screening scheme for sensitivity parameters specific to each pest development stage.

l Constructed a stage-specific daily dynamic prediction framework to achieve spatiotemporally explicit extrapolation of C. suppressalis population dynamics at the regional scale.

Abstract
References

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摘要

二化螟（Chilo suppressalis，C. suppressalis）是水稻主产区的重要害虫之一，对水稻产量和品质构成严重威胁。现有虫害预测研究普遍忽视二化螟种群动态阶段性异质特征，且未能充分考虑气象、土壤与水稻生理信息之间的协同作用，难以准确刻画害虫种群数量的全动态变化过程。针对上述问题，本文明确提出了三项方法学创新：（1）引入基于LOESS平滑曲线拟合与斜率变化分析的方法，客观地将二化螟种群动态划分为基数建立期、种群扩张期和暴发高峰期三个阶段；（2）融合性诱监测数据、遥感反演的水稻生理指数以及 ERA5 再分析气象与土壤变量，构建分阶段虫害预测模型；（3）在分阶段框架基础上，设计了针对不同发生阶段的敏感参数筛选方案，并基于随机森林算法构建逐日动态预测模型，综合考虑气象、土壤与作物生理因子。研究结果表明，所提出的分阶段预测模型具有优异的预测性能。在暴发高峰期，三个实验田的决定系数（R²）均超过0.9，平均绝对误差（MAE）低于23.16，均方根误差（RMSE）小于32.86；其中，九龙试验田在各阶段的预测 R²均高于0.89。与长短期记忆网络（LSTM）和Prophet模型相比，随机森林模型表现出更高的稳定性与泛化能力，其测试集 R²均稳定在0.69以上，凸显了其在二化螟分阶段种群动态预测中的稳健性与可靠性。综上所述，本研究为提升虫害综合预测能力及支持精准防控决策提供了具有重要实践价值的技术路径。

Abstract

Chilo suppressalis is a major pest in rice-producing regions, posing serious threats to rice yield and quality. Existing pest prediction research generally ignore the stage-specific heterogeneity in population dynamics and neglect the synergistic effects among meteorological, soil, and rice physiological information. This makes it challenging to accurately characterize the complete dynamic changes in pest populations. In this study, we explicitly highlight three methodological innovations: (1) the use of a LOESS-based curve fitting and slope-change detection framework to objectively partition C. suppressalis population dynamics into three stages—population establishment, expansion, and outbreak; and (2) the integration of multi-source data, including trap monitoring, rice physiological indices derived from remote sensing, and ERA5 meteorological and soil variables, to construct stage-specific prediction models.; and (3) building upon this stage-based framework, we designed a targeted sensitivity-parameter screening scheme and developed daily dynamic prediction models using the Random Forest (RF) algorithm, which incorporate meteorological, soil, and crop physiological indicators. The results demonstrate that the proposed stage-specific prediction model achieves excellent performance. During the outbreak stage, the R² for all three experimental fields exceeds 0.9, with MAE below 23.16 and RMSE under 32.86. In the Jiulong field, stage-specific predictions show R² values above 0.89. Compared with Long Short Term Memory (LSTM) and Prophet models, RF exhibits superior stability and generalization, with test set R² consistently above 0.69, highlighting its robustness and reliability for stage-specific prediction of C. suppressalis population dynamics. These findings highlight the practical value of our approach for enhancing comprehensive pest forecasting and supporting targeted pest management.

Keywords: rice Chilo suppressalis spatiotemporal prediction random forest stage-specific response

Online: 29 January 2026

Fund:

This work was supported by the Ningbo University High-Level Scientific Research Project Cultivation, China (GJPY2025018), the Key Technology Breakthrough Plan of Ningbo Science and Technology Innovation 2035, China (2024Z262), the Yongjiang Talent Introduction Programme, China (2021 A-136-G), and the Zhejiang Province “Pioneering Soldier” and “Leading Goose” R&D Project, China (2023C01027).

About author: Yuchen Wu, E-mail:2014856003@qq.com; # Corresponding author, E-mail: wanglihua1@nbu.edu.cn

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Yuchen Wu, Lihua Wang, Yanxiao Bao, Weiwei Sun, Zhiyuan Yao, Gang Yang, Yumiao Wang. 2026. Phased dynamic analysis and prediction of rice Chilo suppressalis integrating remote sensing physiological indices and environmental factors. Journal of Integrative Agriculture, Doi:10.1016/j.jia.2026.01.043

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