Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Climate-Based Modeling and Prediction of Rice Gall Midge Populations Using Count Time Series and Machine Learning Approaches

Domaine:

agricultureclimate
Créateur:
SanSriPraGur
Éditeur:
MDP
Hôte:
The Asian rice gall midge (Orseolia oryzae (Wood-Mason)) is a major insect pest in rice cultivation. Therefore, development of a reliable system for the timely prediction of this insect would be a valuable tool in pest management. In this study, occurring between the period from 2013–2018: (i) gall midge populations were recorded using a light trap with an incandescent bulb, and (ii) climatological parameters (air temperature, air relative humidity, rainfall and insulations) were measured at four intensive rice cropping agroecosystems that are endemic for gall midge incidence in India. In addition, weekly cumulative trapped gall midge populations and weekly averages of climatological data were subjected to count time series (Integer-valued Generalized Autoregressive Conditional Heteroscedastic—INGARCH) and machine learning (Artificial Neural Network—ANN, and Support Vector Regression—SVR) models. The empirical results revealed that the ANN with exogenous variable (ANNX) model outperformed INGRACH with exogenous variable (INGRCHX) and SVR with exogenous variable (SVRX) models in the prediction of gall midge populations in both training and testing data sets. Moreover, the Diebold–Mariano (DM) test confirmed the significant superiority of the ANNX model over INGARCHX and SVRX models in modeling and predicting rice gall midge populations. Utilizing the presented efficient early warning system based on a robust statistical model to predict the build-up of gall midge population could greatly contribute to the design and implementation of both proactive and more sustainable site-specific pest management strategies to avoid significant rice yield losses.

Visit

doi.org

Licenses

https://creativecommons.org/licenses/by/4.0/

Similaires

Climate-based predictive modeling of malaria incidence using statistical and machine learning approachesAntixenotic and Antibiotic Mechanisms of Resistance to African Rice Gall Midge in NigeriaA South African Power Supply Reliability Dataset, Structured for Count Time Series and Machine Learning ApplicationsEnsemble Methods for Time Series Forecasting in Nigeria: Predicting Agricultural Yields Using Advanced Machine Learning ApproachesFarhanilig/Machine-Learning-Based-Forecasting-of-Fuel-Prices-in-Somalia-Using-Time-Series-ModelsMachine learning and remote sensing based time series analysis for drought risk prediction in Borena Zone, Southwest Ethiopia

Climate-based predictive modeling of malaria incidence using statistical and machine learning approaches

Malaria remains a major public health burden in Nigeria, where climatic variability plays a critical

Antixenotic and Antibiotic Mechanisms of Resistance to African Rice Gall Midge in Nigeria

A South African Power Supply Reliability Dataset, Structured for Count Time Series and Machine Learning Applications

Recurring load-shedding and persistent power system disruptions in South Africa have intensified the

Ensemble Methods for Time Series Forecasting in Nigeria: Predicting Agricultural Yields Using Advanced Machine Learning Approaches

International audience Accurate forecasting of agricultural yields is essential for m

Farhanilig/Machine-Learning-Based-Forecasting-of-Fuel-Prices-in-Somalia-Using-Time-Series-Models

Machine Learning-Based Forecasting of Fuel Prices in Somalia Using Time-Series Models # Machine-Lea

Machine learning and remote sensing based time series analysis for drought risk prediction in Borena Zone, Southwest Ethiopia