[1]白雪松,吴建平,景文超,等.基于卷积神经网络的农作物病虫害检测研究[J].计算机技术与发展,2022,32(12):200-205.[doi:10. 3969 / j. issn. 1673-629X. 2022. 12. 030]
　BAI Xue-song,WU Jian-ping,JING Wen-chao,et al.Research on Crop Diseases and Pests Detection Based on Convolutional Neural Network[J].,2022,32(12):200-205.[doi:10. 3969 / j. issn. 1673-629X. 2022. 12. 030]
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基于卷积神经网络的农作物病虫害检测研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年12期
页码:
200-205
栏目:
新型计算应用系统
出版日期:
2022-12-10

文章信息/Info

Title:
Research on Crop Diseases and Pests Detection Based on Convolutional Neural Network
文章编号:
1673-629X(2022)12-0200-06
作者:
白雪松1 ; 吴建平1; 2; 3 ; 景文超1 ; 何旭鑫1 ; 余 咏1
1. 云南大学 信息学院,云南 昆明 650504;
2. 云南省电子计算中心,云南 昆明 650223;
3. 云南省高校数字媒体技术重点实验室,云南 昆明 650223
Author(s):
BAI Xue-song1 ; WU Jian-ping1; 2; 3 ; JING Wen-chao1 ; HE Xu-xin1 ; YU Yong1
1. School of Information Science & Engineering,Yunnan University,Kunming 650504,China;
2. Yunnan Provincial Electronic Computing Center,Kunming 650223,China;
3. Key Laboratory of Digital Media Technology of Universities and Colleges in Yunnan Province,Kunming 650223,China
关键词:
农作物病虫害; 卷积神经网络; CBAM-Res2Net50; 迁移学习; 注意力模块; 隐式语义数据增强
Keywords:
crop diseases and pests;  convolutional neural network;  CBAM - Res2Net50;  transfer learning;  attention module;  implicitsemantic data augmentation
分类号:
TP312
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 12. 030
摘要:
农作物病虫害图像采集困难,且公共数据集较少,因此使用单一网络模型识别准确率不高。 常用的数据增强方法只能对图像进行像素空间的变换, 不能进行语义转换。对此, 研 究 并 提 出 基 于 隐 式 语 义 数 据 增 强 算 法 的 CBAM -Res2Net50 模型:该模型使用 Res2Net50 主干网络,从多尺度学习图像信息,加载预训练模型部分参数,提高模型的收敛速度;在网络残差块中添加混合注意力模块,提取并保留关键特征;训练过程中使用隐式语义数据增强算法对提取的深层网络空间特征进行语义扩充增强,提高模型的泛化能力改进模型与现有模型在 AI Challenger 2018 农作物病虫害数据集上的对比实验结果表明:改进模型具有较高的识别率,其分类准确率达 88. 33% 。 改进后的模型通过挖掘相似病虫害图像的语义信息,在一定程度上解决了深度网络中由于数据不足导致的过拟合等问题。
Abstract:
It is difficult to collect images of crop diseases and pests,and there are few common data sets,so the recognition accuracy ofsingle network model is not high. The commonly used data augmentation methods can only transform image pixel space,but not semantictransformation. Therefore,CBAM-Res2Net50 model based on implicit semantic data augmentation algorithm is studied and proposed.The model uses Res2Net50 backbone network to learn image information from multi - scale and load some parameters of pre - trainingmodel to improve the convergence speed of the model. A mixed attention module is added to the network residual block to extract andretain key features. In the training process,implicit semantic data augmentation algorithm is used to enhance semantic expansion and generalization ability of the model. The experimental results of comparison between the improved model and the existing model on AIChallenger 2018 Crop Diseases and Pests Data Set show that the improved model has a high recognition rate, and its classificationaccuracy is 88. 33% . The improved model can solve the problem of over-fitting caused by insufficient data in deep network to someextent by mining semantic information of similar images of diseases and pests.

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更新日期/Last Update: 2022-12-10