Skip to content
Research Article Open access CC BY 4.0

Weather Based Pest Forewarning Model for Major Insect Pests of Rice – An Effective Way for Insect Pest Prediction

Manikandan Narayanasamy, J. S. Kennedy, V. Geethalakshmi

Annual Research & Review in Biology · pp. 1–13 · Published 28 Dec 2017

10.9734/ARRB/2017/37365

Abstract

Weather parameters viz., Temperature, rainfall, relative humidity, sunshine hours and wind speed are the major weather elements determining the insect pests’ occurrence. Weather based forewarning models are widely utilized in the integrated pest management system as a tool which do not cause any harm to the predators and also cuts down environmental pollution. Considering this, an attempt was made to predict the population occurrence of Yellow Stem Borer (YSB), Brown Planthopper (BPH) and Rice Leaffolder (RLF). Generalized Linear Model (GLiM) was developed for YSB, BPH and RLF for predicting the population at a given time. The results of chi square test revealed that, there are many other factors which affect the amount of light trap catches of the insects apart from weather parameter. The predictability of the equation can be increased if the weather factors are combined with the other factors (variety, soil, fertilizer application, etc.,) in developing the model.

Forewarning forecast model PCA prediction weather

Cited by 6

Crop Weather Pest Relationship and Forewarning Model for Spodoptera frugiperda on Maize and Sorghum

T. Prathima, K.V.S. Sudheer, K. Devaki · Agricultural Science Digest - A Research Journal · 2023

Exploring weather-disease-mustard yield relationship using predictive analytics

Manjari Singh, Subash Nataraja Pillai, Ajeet Singh Nain · Theoretical and Applied Climatology · 2026

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

Abha Goyal, Abhishek Singh, Mahadevan Raghuraman · International Journal of Tropical Insect Science · 2025

Dynamic Prediction of Chilo suppressalis Occurrence in Rice Based on Deep Learning

Siqiao Tan, Yu Liang, Ruowen Zheng · Processes · 2021

Article metrics

Real usage data collected on this platform.

0

Page views

0

PDF downloads

0

Outbound clicks

6

Citations

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

No views recorded yet.

Traffic sources

Referring site, by host.

No traffic recorded yet.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.