[1]李晓振,徐岩,吴作宏,等.基于注意力神经网络的番茄叶部病害识别系统[J].江苏农业学报,2020,(03):561-568.[doi:doi:10.3969/j.issn.1000-4440.2020.03.005]
　LI Xiao-zhen,XU Yan,WU Zuo-hong,et al.Recognition system of tomato leaf disease based on attentional neural network[J].,2020,(03):561-568.[doi:doi:10.3969/j.issn.1000-4440.2020.03.005]
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基于注意力神经网络的番茄叶部病害识别系统()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2020年03期
页码:
561-568
栏目:
植物保护
出版日期:
2020-06-30

文章信息/Info

Title:
Recognition system of tomato leaf disease based on attentional neural network
作者:
李晓振; 徐岩; 吴作宏; 高照; 刘林
(山东科技大学电子信息工程学院，山东青岛266590)
Author(s):
LI Xiao-zhen; XU Yan; WU Zuo-hong; GAO Zhao; LIU Lin
(School of Electronic Information Engineering, Shandong University of Science & Technology, Qingdao 266590, China)
关键词:
番茄; 叶部病害; 注意力机制; 并行池化; WEB界面
Keywords:
tomato; leaf disease; attention mechanism; parallel pooling; WEB interface
分类号:
TP391
DOI:
doi:10.3969/j.issn.1000-4440.2020.03.005
文献标志码:
A
摘要:
基于注意力机制的卷积神经网络构建了番茄叶部病害识别系统。依据注意力机制构建并行注意力模块以提升特征提取能力，并与残差结构相结合构建PARNet模型。以分别患有早疫病、晚疫病、叶霉病、斑枯病和花叶病毒病这5类病害的叶片和健康叶片的叶部图像为研究对象，将PARNet模型与VGG16、ResNet50、SeNet等模型相对比，结果显示PARNet模型的识别率为96.91%，高出其他模型2.25%～11.58%。各类预测结果的精确率平均为96.84%。最后使用Flask完成WEB应用程序的开发,实现了跨平台的番茄叶部病害识别。
Abstract:
The convolutional neural network based on attention mechanism was proposed to construct tomato leaf disease recognition system. According to the attention mechanism, a parallel attention module was constructed to improve the feature extraction ability, and combined with the residual structure to construct a PARNet model. The images of healthy leaves and leaves suffering from early blight, late blight, leaf mold, leaf blight and mosaic virus were used as research objects. Compared with other models such as VGG16, ResNet50 and SeNet the recognition rate of the PARNet model was increased by 2.25%-11.58%. The accuracy rate of various prediction results was 96.84% on average. Flask was used to complete the development of WEB application program, and the cross-platform tomato leaf disease recognition was realized.

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备注/Memo

备注/Memo:
收稿日期：2020-03-23基金项目：国家自然科学基金项目（11547037、11604181）；山东省研究生教育创新计划项目（01040105305）；海信（山东）冰箱有限公司研发中心资助课题作者简介：李晓振(1994-)，男，山东临沂人，硕士研究生，研究方向为计算机视觉、深度学习和人工智能等。(E-mail)lxz201019@126.com通讯作者：徐岩，(E-mail)xuyan@sdust.edu.cn
更新日期/Last Update: 2020-07-14