报告题目：Spatial convolutional self-attention-based transformer module for strawberry disease identification under complex background
报告人：陈鹏，博士，安徽大学互联网学院教授，博士生导师
报告时间：2023年11月4日（周六） 8:00
报告地点：电气楼304
报告对象：感兴趣的教师、研究生等
主办单位：电气与十大网投平台信誉排行榜
报告人简介：
陈鹏,男，安徽大学互联网学院教授/博士生导师。毕业于中国科技大学模式识别与智能系统专业，中科院合肥智能机械研究所客座教授，安徽省现代农业产业技术体系农业大数据与知识工程专业组组长、岗位专家，农业生态大数据分析与应用技术国家地方联合工程研究中心副主任，中科合肥智慧农业协同创新研究院大数据首席科学家，入选2017年度安徽省人社厅留学归国人员创新创业扶持计划（重点）。2006年-2014年期间先后在新加坡南洋理工大学担任Research Fellow，美国、沙特阿卜杜拉国王科技大学、香港城市大学等担任博士后研究员。2011年-2012年期间在中科院合肥智能机械研究所任副研究员，中国科大硕士生导师/副博导。
主要从事大数据分析与数据挖掘、计算机视觉、农业物联网以及生物信息学等方面的研究。在国际重要刊物上发表了SCI/EI论文100多篇，其中中科院二区以上论文60多篇，ESI高被引论文1篇，授权国家专利6项。主持了3项国家自然科学基金，主持和参与了国家自然科学基金、国家科技支撑计划、863计划等国家级、省部级项目10 余项。担任Frontiers in Plant Science副主编，担任数个国际杂志的编辑委员会委员。同时，还兼任安徽省电子学会理事、IEEE会员、中国计算机学会会员、安徽省和北京市等多个省市的专家组成员。
研究方向：大数据分析与数据挖掘、智慧农业、计算机视觉及应用、生物医学农业信息学。
报告内容：
The occurrence of strawberry diseases has a huge impact on the yield and quality of strawberry fruits, resulting in huge economic losses. Real-time and effective identification and diagnosis of strawberry disease is an essential step for strawberry disease prevention. Machine learning-based methods are widely used in strawberry disease identification tasks, but these methods require expertise to design proper strawberry disease feature descriptors. Deep-learning methods have remarkably improved the capability of feature extraction. However, the strawberry disease with complex backgrounds brings great challenges for accurate feature extraction, which leads to poor recognition results of strawberry disease under complex backgrounds.
In this paper, an improved transformer-based strawberry disease identification method is proposed to achieve precise and fast recognition of multiple classes of strawberry diseases. First, a multi-classes strawberry disease dataset has been constructed with 5369 images and 12 types of common strawberry disease. To increase the diversity of samples under complex backgrounds, various data augmentation strategies are introduced into the strawberry disease recognition method. Then, Multi-Head Self-Attention (MSA) is used to capture feature dependencies over long distances of strawberry disease images by leveraging the self-attention mechanism. To improve the recognition efficiency, the spatial convolutional self-attention-based transformer (SCSA-Transformer) is proposed to reduce the parameters of the transformer network. The experimental results validated on the constructed strawberry disease dataset demonstrate that the recognition accuracy of the proposed method can achieve 99.10%, which outperforms other methods. Besides, we also observe that the parameters of the classification model are reduced compared with other methods, which effectively improves the recognition efficiency of strawberry diseases.
欢迎全校师生踊跃参加！
