Skip to content
Research Article Open access CC BY 4.0

Predicting the Major Organism Species in Tobacco in Cao Bang Province, Vietnam Based on the Weather Conditions

Nguyen Van Chin, Nguyen Van Van, Tao Ngoc Tuan, Phạm Hà Thành

Annual Research & Review in Biology · pp. 42–54 · Published 19 Aug 2022

10.9734/arrb/2022/v37i930531

Abstract

In recent years, some organism species are appearing popularly in the tobacco in Cao Bang province, Vietnam, and caused severe damage to the tobacco yield and quality such as budworm (Helicoverpa assulta Guene), aphid (Myzus persicea Sulzer), and powdery mildew (Erysiphe cichoracearum D.C). To manage them effectively, forecasting and controlling insect pests play an important role in tobacco cultivation. The predictive model was built base on the Skybit, Fuzzy, and Degree-days model to forecast and give suitable control methods for major insect pests in tobacco. This model is run on Excel software and calculated by an IF function for the growth of the organism. Result of the model predicted accurately the tobacco budworm, aphids, and powdery mildew damaging tobacco in Cao Bang in April 2022. Based on the results of prediction, we give proper control methods for each insect pests, preventing the quick growth and development of the organism species in the field, reducing the use of pesticides, and increasing the income of the growers. This model has also applied to forecast other pests in the tobacco in Vietnam. To increase the quality of the prediction, the model will continue to be perfected and completed in the coming years based on the practice field.

Tobacco budworm aphid powdery mildew and forecast model

Cited by 2

PHTFNet-RPM: a probabilistic hybrid network with RPM for tobacco root disease forecasting

Yun-Hong Bu, Tingshan Yao, Shao-Wu Geng · Frontiers Big Data · 2025

Ensemble models based on radial basis function network for landslide susceptibility mapping

Minh Lê Nguyễn, Truyền Thế Phạm, P. Tran · Environmental science and pollution research international · 2023

Article metrics

Real usage data collected on this platform.

0

Page views

0

PDF downloads

0

Outbound clicks

2

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.