数据增强提升烟草靶斑病识别准确率的研究

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中图分类号：S432；TP391.4文献标识码：A文章编号：2097-1354（2025）06-0077-08

Abstract： Crop disease identification is of great significance for ensuring the healthy growth of crops and the stable development of agricultural production. In recent years， many studies have shown that the introduction of data augmentation techniques has significantly improved the accuracy of crop disease recognition models. This study proposes the application of data augmentation techniques to enhance the performance of tobacco target spot disease recognition models. The research employs various data augmentation methods，including image flipping，grayscale adjustment， brightness adjustment and chroma adjustment，as well as MixUp and CutMix data augmentation methods，to expand and diversify the image data of tobacco target spot disease. The data augmentation effects on the tobacco target spot disease image recognition models were verified using mainstream image recognition models， namely AlexNet，GoogleNet，and ResNet101. The results show that after the application of data augmentation，the training set accuracy and test set accuracy of the image recognition models were increased by up to 2.80% and 3. 78% ， respectively， compared to those without data augmentation. Meanwhile， the training set loss and test set loss were reduced by 10.84% and 4.73% ，respectively. The study concludes that the use of data augmentation techniques can improve the performance of tobacco target spot disease recognition models. This method provides a data processing approach for the research of tobacco disease image recognition models and offers a scientific basis for the application of image recognition models.

Key words： tobacco target spot disease；image recognition model；data augmentation； model performance

近年来，随着深度学习技术的兴起，视觉领域的研究被广泛应用到图像识别模型中[1]。（剩余7982字）