基于改进ConvNeXt模型的农作物害虫细粒度识别  

Fine-grained identification of crop pests using an enhanced ConvNeXt model

作　　者：韩源涛 张聪 詹晓芸 王正 HAN Yuantao;ZHANG Cong;ZHAN Xiaoyun;WANG Zheng(School of Mathematics and Computer Science,Wuhan Polytechnic University,Wuhan 430048,China;School of Electrical and Electronic Engineering,Wuhan Polytechnic University,Wuhan 430048,China;School of Computer Science,Wuhan University,Wuhan 430072,China)

机构地区：[1]武汉轻工大学数学与计算机学院,武汉430048 [2]武汉轻工大学电气与电子工程学院,武汉430048 [3]武汉大学计算机学院,武汉430072

出　　处：《农业工程学报》2025年第4期185-192,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：湖北省技术创新重大项目(2018A01038)。

摘　　要：精确分类不同时期的农业害虫对控制其发生和发展至关重要。针对目前不同生长时期农作物害虫分类不准确的问题,该研究创建了一个关注虫态的害虫数据集并提出了一种基于改进ConvNeXt网络的农作物害虫识别模型。通过引入多种虫态共同监督来重构网络主干,以便模型学习不同虫态的特征,引入空间注意力(spatial attention,SA)来改进模型结构,增强对害虫位置信息的提取能力。在大型公开数据集IP102上进行试验,与现有的同类最优基于Vision Transformer的方法相比,在保持模型参数量基本没有增加的前提下,准确率提高3.67个百分点,F1值提高2.49个百分点。试验证明,该研究提出的模型针对不同虫态害虫具备较强的识别准确率,可为精准农业害虫识别提供一定的参考。Precise and rapid identification of diverse pest species can greatly contribute to crop disease prevention and control in modern agriculture.However,the accuracy of pest identification has been frequently confined to the varied insect stages during different pest growth.Among them,the same pest can display distinctly different morphological features across various growth stages,while the different pests can exhibit similar morphologies in the same developmental periods.Both manual identification and machine learning approaches can often struggle to fully meet these demands of complex recognition.In this study,fine-grained identification was performed on the crop pests using an enhanced ConvNeXt model.A series of experiments were also carried out on the large-scale pest dataset with the morphological diversity of insects.The large-scale dataset contained 102 pest categories and 51670 images representing 369 classes of pests at different stages.The largest dataset was focused mainly on the whole stages of insects;Each image was precisely labelled with the pest species and their developmental stages.A robust foundation was provided for the subsequent morphological studies.Furthermore,the ConvNeXt V2 was adopted as the baseline model.A multi-stage co-supervision strategy was then introduced to optimize the structure for better feature variability of the same species across different pest stages,as well as the significant inter-species differences.Two independent streams of neural networks were also constructed during optimization.Specifically,the species-specific features were learned by the feature extraction module within the ConvNeXt Block.While the shared features were derived through the first residual block of ResNet50,and then shared with the subsequent parameters of layers.A feature fusion module was then employed to effectively integrate these shared and species-specific features.A deep feature fusion was also designed to enhance the overall performance of recognition.Moreover,there were pronounced morpholog

关 键 词：害虫识别 农作物 ConvNeXt 空间注意力机制 多虫态识别 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]