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A machine learning model for studying the seasonality of aphids in wheat-based cropping systems of the terai zone of Darjeeling, West Bengal, India


Affiliations
1 School of Agricultural Sciences, GD Goenka University, Gurugram 122 103, India
2 Directorate of Research (RRS-TZ), Uttar Banga Krishi Viswavidyalaya, Pundibari, Cooch Behar 736 165, India
3 Regional Research Sub-station (Terai Zone) Kharibari, Uttar Banga Krishi Viswavidyalaya, Pundibari, Cooch Behar 736 165, India
4 Department of Statistics, North-Eastern Hill University, Shillong 793 022, India
5 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India

The primary goal of this study is to determine the effect of weather variables on aphid populations and develop­ment of a weather-based forewarning model using a powerful machine learning technique called random forest. The developed model could be employed to formulate proper management strategies to help the farming community control aphid infestation.

Keywords

Aphid infestation, forewarning model, machine learning, random forest, weather parameters, wheat-based cropping system
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  • A machine learning model for studying the seasonality of aphids in wheat-based cropping systems of the terai zone of Darjeeling, West Bengal, India

Abstract Views: 166  | 

Authors

Biwash Gurung
School of Agricultural Sciences, GD Goenka University, Gurugram 122 103, India
Suprakash Pal
Directorate of Research (RRS-TZ), Uttar Banga Krishi Viswavidyalaya, Pundibari, Cooch Behar 736 165, India
Md. Wasim Reza
Regional Research Sub-station (Terai Zone) Kharibari, Uttar Banga Krishi Viswavidyalaya, Pundibari, Cooch Behar 736 165, India
Bishal Gurung
Department of Statistics, North-Eastern Hill University, Shillong 793 022, India
Achal Lama
ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India

Abstract


The primary goal of this study is to determine the effect of weather variables on aphid populations and develop­ment of a weather-based forewarning model using a powerful machine learning technique called random forest. The developed model could be employed to formulate proper management strategies to help the farming community control aphid infestation.

Keywords


Aphid infestation, forewarning model, machine learning, random forest, weather parameters, wheat-based cropping system



DOI: https://doi.org/10.18520/cs%2Fv125%2Fi11%2F1244-1249