计算机技术与发展2025，Vol.35Issue(3)：210-214,5.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0334
用于植物病虫害图像识别的数据增强方法
A Data Augmentation Method for Image Recognition of Plant Pests and Diseases
摘要
Abstract
In the field of deep learning image recognition of plant pests and diseases,regional data augmentation is a key strategy to improve model generalization.It prompts models to focus on extracting less discriminative features by selectively removing image regions,thereby improving adaptability to new data.The proposed SaliencyBatchMix method utilizes Class Activation Mapping(CAM)to derive Semantic Percentage Maps(SPM)after selecting representative regions based on batch dimensionality.It mixes indicative patches into target images to guide learning more suitable representations.This reduces meaningless cropped pixels during training and la-beling noise.Experiments using GhostNet show SaliencyBatchMix achieves 72.05%and 96.86%accuracy on IP102 and Embrapa,out-performing CutMix by 0.62 percentage points and 1 percentage points,respectively.Results validation and ablation study findings corroborate SaliencyBatchMix's effectiveness in boosting model generalization and accuracy.关键词
数据增强/类激活映射/深度学习/植物病虫害识别/GhostNetKey words
data augmentation/class activation mapping/deep learning/plant pests and diseases recognition/GhostNet分类
信息技术与安全科学引用本文复制引用
肖宇,吴杰,马驰..用于植物病虫害图像识别的数据增强方法[J].计算机技术与发展,2025,35(3):210-214,5.基金项目
广东省教育科学研究项目(2022ZDZX4052,2021ZDJS082) （2022ZDZX4052,2021ZDJS082）
