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

文献类型： 外文期刊

作者： Feng, Xiao 1 ; Ullah, Farman 2 ; Liu, Jiali 1 ; Ji, Yunliang 1 ; Abbas, Sohail 1 ; Liao, Siqi 1 ; Ali, Jamin 1 ; Desneux, Nicolas 3 ; Chen, Rizhao 1 ;

作者机构： 1.Jilin Agr Univ, Coll Plant Protect, Changchun 130118, Peoples R China

2.Zhejiang Acad Agr Sci, Inst Plant Protect & Microbiol, State Key Lab Managing Biot & Chem Threats Qual &, Hangzhou, Peoples R China

3.Univ Cote Azur, INRAE, CNRS, UMR ISA, F-06000 Nice, France

关键词： larval instar identification; machine learning; maize pest; weight predication; Ostrinia furnacalis

期刊名称：BULLETIN OF ENTOMOLOGICAL RESEARCH （ 影响因子：1.6； 五年影响因子：1.7 ）

ISSN： 0007-4853

年卷期： 2025 年 115 卷 1 期

页码：

收录情况： SCI

摘要： The Asian corn borer, Ostrinia furnacalis (Guen & eacute;e), 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.

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