数字农科院2.0

Automatic Potato Crop Beetle Recognition Method Based on Multiscale Asymmetric Convolution Blocks

文献类型： 外文期刊

作者： Jingjun Cao;Xiaoqing Xian;Minghui Qiu;Xin Li;Yajie Wei;Wanxue Liu;Guifen Zhang;Lihua Jiang

作者机构：

关键词： asymmetric convolution;automatic recognition;colorado potato beetle;deep learning;field investigation

期刊名称： Agronomy

ISSN： 2073-4395

年卷期： 2025 年 15 卷 7 期

页码：

收录情况： SCIE（2025版）

摘要： Five beetle species can occur in potato fields simultaneously, including one quarantine pest (the Colorado potato beetle (CPB)), one phytophagous pest (the 28-spotted potato ladybird beetle), and three predatory ladybird beetles (the 7-spotted lady beetle, the tortoise beetle, and the harlequin ladybird beetle). The timely detection and accurate identification of CPB and other phytophagous or predatory beetles are critical for the effective implementation of monitoring and control strategies. However, morphological identification requires specialized expertise, is time-consuming, and is particularly challenging due to the dark brown body color of these beetles when in the young larval stages. This study provides an effective solution to distinguish between phytophagous and/or quarantine and predatory beetles. This solution is in the form of a new convolutional neural network architecture, known as MSAC-ResNet. Specifically, it comprises several multiscale asymmetric convolution blocks, which are designed to extract features at multiple scales, mainly by integrating different-sized asymmetric convolution kernels in parallel. We evaluated the MSAC-ResNet through comprehensive model training and testing on a beetle image dataset of 11,325 images across 20 beetle categories. The proposed recognition model achieved accuracy, precision, and recall rates of 99.11%, 99.18%, and 99.11%, respectively, outperforming another five existing models, namely, AlexNet, MobileNet-v3, EfficientNet-b0, DenseNet, and ResNet-101. Notably, the developed field investigation mini-program can identify all the developmental stages of these five beetle species, from young larvae to adults, and provide timely management (or protection) suggestions to farmers. Our findings could be significant for future research related to precise pest control and the conservation of natural enemies.

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