基于ResViT模型的城市草地草本植物智能分类  

Intelligent classification of urban grassland herbs based on ResViT modeling

作　　者：南喆 杨宏伟 杨梦鹭 NAN Zhe;YANG Hongwei;YANG Mengu(Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau/Inner Mongolia Key Laboratory of Grassland Ecology/School of Ecology and Environment,Inner Mongolia University,Hohhot 010021,Inner Mongolia,China)

机构地区：[1]蒙古高原生态学与资源利用教育部重点实验室/省部共建草地生态学国家重点实验室培育基地/内蒙古大学生态与环境学院,内蒙古呼和浩特010021

出　　处：《草业科学》2025年第3期628-637,共10页Pratacultural Science

摘　　要：在干旱半干旱城市草地监测领域中,草本植物分类识别的用处及贡献不容小觑,但当前的深度学习模型在样本数据偏重且规模较小的任务中尚有不足之处。城市草地监测能够有效地评估草地的生长状况,并根据草本植物的分类,对当地生态系统的潜在危害提供判断信息。基于ViT(VisionTransformer)和ResNet50(深度残差网络)构建了混合神经网络模型ResViT。ResViT在测试集准确率上最高,优于AlexNet、ResNet50和VGG19模型,在平均召回率和F1评分上也均优于AlexNet、ResNet50和VGG19模型。ResViT的训练时间约是VGG19的一半。ResViT在16分类任务中测试集达到了95.45%的准确率和0.95的F1评分。综上所述,ResViT模型可以准确高效地完成草本植物分类的图像识别任务,比其他3种模型均有优势。其在偏重的小规模数据集上展现出优异的性能,显著降低了前期数据准备的成本,同时提升了训练效率,减少了训练时间。因此,ResViT的建立为草本植物分类领域的研究提供了新视角,并有望在干旱半干旱城市草地监测的广泛应用中发挥重要作用。In the field of grassland monitoring in arid and semi-arid areas,the utility and contribution of the classification and recognition of herbaceous plants cannot be underestimated.However,current deep learning models continue to have shortcomings with respect to tasks involving substantial sample data and small scale.Urban grassland monitoring can effectively enable assessments of the growth status of grasslands and provide information for evaluating the potential harm to local ecosystems based on the classification of herbs.On the basis the ViT(Vision Transformer)and ResNet50(Residual Network 50 layers)models,in this study,we constructed a hybrid neural network model referred to as ResViT,which is superior to the AlexNet,ResNet50,and VGG19 models in terms of test set accuracy,average recall rates,and F1 scores.ResViT can be trained within half the time needed for VGG19,and achieved an accuracy of 95.45%and an F1 score of 0.95 when used to perform a test set of 16 classification tasks.To summarize,the ResViT model can accurately and efficiently accomplish image recognition tasks for the classification of herbs and has distinct advantages compared with the AlexNet,ResNet50,and VGG19 models.It has shown excellent performance when used to assess heavily small-scale datasets,significantly reducing the cost of preliminary data preparation,whilst also improving training efficiency and reducing training time.Consequently,the establishment of ResViT offers a novel perspective for research in the field of herb classification,and it is anticipated that this model will play key roles in extensive applications for grassland monitoring in arid and semi-arid areas.

关 键 词：草本植物分类 卷积神经网络 ResViT模型 干旱半干旱 草地监测 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]