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Agricultural Technology & Equipment
2024
(2)
18-21
基于ResNet和ViT双流网络的小麦病虫害识别
作 者：
王汉生;姚建斌
单 位：
关键词：
小麦病虫害识别;ResNet;ViT;双流网络;深度学习
摘 要：
针对小麦病虫害识别过程中,传统深度学习模型表现不稳定、识别精度低、泛化能力有限的现状,提出了新的双流网络模型,即结合ResNet和ViT以提高识别准确性.该方法融合了卷积神经网络处理图像局部结构,同时利用Transformer捕捉长距离依赖关系,改进了识别性能.通过2 070张小麦病虫害图片数据集训练验证,调整ResNet50和ViT预训练模型参数,结果显示,双流模型在训练集上达96.5%准确率,在验证集获0.94的F1分数,明显优于其他主流单一模型.结果证实,新模型在小麦病虫害识别卓越性能,为其在智能农业系统中广泛应用提供潜力.
译 名：
Identification of Wheat Pests and Diseases Based on ResNet and ViT Dual Flow Network
关键词：
identification of wheat pests and diseases%ResNet%ViT%dual-flow network%deep learning
摘 要：
In the process of wheat pest identification,the traditional deep learning model is unstable,the recognition accuracy is low,and the generalization ability is limited.A new dual-flow network model is proposed,which combines ResNet and ViT to improve the recognition accuracy.This method integrates the convolutional neural network to process the local structure of the image,while Transformer is used to capture the long-distance dependency,which improves the recognition performance.Through the training veri-fication of 2 070 images of wheat pests and diseases,the parameters of ResNet50 and ViT pre-training models were adjusted.The re-sults showed that the double-flow model achieved 96.5%accuracy in the training set and 0.94 F1 score in the verification set,which was significantly better than other mainstream single models.The results confirm that the new model has excellent performance in wheat pest and disease identification,providing potential for its wide application in intelligent agricultural systems.
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