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Instar identification and weight prediction of Ostrinia furnacalis (Guenée) larvae using machine learning

Published online by Cambridge University Press:  27 January 2025

Xiao Feng ,
Farman Ullah ,
Jiali Liu ,
Yunliang Ji ,
Sohail Abbas ,
Siqi Liao ,
Jamin Ali ,
Nicolas Desneux  and
Rizhao Chen Open the ORCID record for Rizhao Chen [Opens in a new window]
Xiao Feng
Affiliation:
College of Plant Protection, Jilin Agricultural University, Changchun, 130118, PR China
Farman Ullah
Affiliation:
State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products, Institute of Plant Protection and Microbiology, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
Jiali Liu
Affiliation:
College of Plant Protection, Jilin Agricultural University, Changchun, 130118, PR China
Yunliang Ji
Affiliation:
College of Plant Protection, Jilin Agricultural University, Changchun, 130118, PR China
Sohail Abbas
Affiliation:
College of Plant Protection, Jilin Agricultural University, Changchun, 130118, PR China
Siqi Liao
Affiliation:
College of Plant Protection, Jilin Agricultural University, Changchun, 130118, PR China
Jamin Ali
Affiliation:
College of Plant Protection, Jilin Agricultural University, Changchun, 130118, PR China
Nicolas Desneux
Affiliation:
Université Côte d’Azur, INRAE, CNRS, UMR ISA, 06000 Nice, France
Rizhao Chen*
Affiliation:
College of Plant Protection, Jilin Agricultural University, Changchun, 130118, PR China
*
Corresponding author: Rizhao Chen; Email: [email protected]
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Abstract

The Asian corn borer, Ostrinia furnacalis (Guenée), emerges as a significant threat to maize cultivation, inflicting substantial damage upon the crops. Particularly, its larval stage represents a critical point characterised by significant economic consequences on maize yield. To manage the infestation of this pest effectively, timely and precise identification of its larval stages is required. Currently, the absence of techniques capable of addressing this urgent need poses a formidable challenge to agricultural practitioners. To mitigate this issue, the current study aims to establish models conducive to the identification of larval stages. Furthermore, this study aims to devise predictive models for estimating larval weights, thereby enhancing the precision and efficacy of pest management strategies. For this, 9 classification and 11 regression models were established using four feature datasets based on the following features geometry, colour, and texture. Effectiveness of the models was determined by comparing metrics such as accuracy, precision, recall, F1-score, coefficient of determination, root mean squared error, mean absolute error, and mean absolute percentage error. Furthermore, Shapley Additive exPlanations analysis was employed to analyse the importance of features. Our results revealed that for instar identification, the DecisionTreeClassifier model exhibited the best performance with an accuracy of 84%. For larval weight, the SupportVectorRegressor model performed best with R2 of 0.9742. Overall, these findings present a novel and accurate approach to identify instar and predict the weight of O. furnacalis larvae, offering valuable insights for the implementation of management strategies against this key pest.

Keywords

larval instar identificationmachine learningmaize pestweight predicationOstrinia furnacalis
Type
Research Paper
Information
Bulletin of Entomological Research , First View , pp. 1 - 12
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Copyright
© The Author(s), 2025. Published by Cambridge University Press.

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