Deep Learning-Based Detection of Cannibalism and Competition Behaviour in Asian Corn Borer, Ostrinia furnacalis Larvae

文献类型： 外文期刊

第一作者： Feng, Xiao

作者： Feng, Xiao;Liu, Jiali;Abbas, Sohail;Ali, Jamin;Chen, Rizhao;Ullah, Farman;Manduca, Gianluca;Romano, Donato;Manduca, Gianluca;Romano, Donato;Desneux, Nicolas

作者机构：

关键词： cannibalism; deep learning; larval competition; Ostrinia furnacalis; pest management

期刊名称：JOURNAL OF APPLIED ENTOMOLOGY （ 影响因子：2.0； 五年影响因子：2.1 ）

ISSN： 0931-2048

年卷期： 2025 年

页码：

收录情况： SCI

摘要： As Ostrinia furnacalis, a notorious pest, navigates the intricate ecology of its habitat, a profound comprehension of cannibalism and competitive behaviour stands as a cornerstone for the implementation of effective pest management strategies. To address this gap, we used a novel deep learning approach based on a convolutional neural network (CNN) to assess larval competition under controlled laboratory conditions. Our study categorised O. furnacalis larval behaviours into 10 distinct types and observed significant differences in their frequency. The CNN model achieved high precision (0.958), recall (0.959) and mAP@0.5 score (0.972) for real-time monitoring of larval activity. During intraspecific competition without food, both the frequency and duration of interactions increased significantly. In particular, the duration of biting the head was longer during the 4th instar. Third instar larvae exhibited a higher cannibalism rate (40%). Pupation rates were highest (80%-90%) in the absence of competition, producing heavier pupae associated with kernel feeding. This study enhances our understanding of intraspecific competition and its effects on larval survival in O. furnacalis. It also promotes the use of artificial intelligence-based approaches in exploring and managing economically important insect species.

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