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徐焕良

Professor Supervisor of Doctorate Candidates

School/Department: College of Artificial Intelligence
Education Level:With Certificate of Graduation for Doctorate Study
Degree:Doctoral degree
Professional Title:Professor
Alma Mater:南京航空航天大学机电学院博士毕业

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Paper Publications Home >> Scientific Research >> Paper Publications: Yuan Peisen,Xia Ye,Tian Yongchao,Xu Huanliang.TRiP: a transfer learning based rice disease phenotype recognition platform using SENet and microservices,FRONTIERS IN PLANT SCIENCE,2024,14(Participating authors)

袁培森,丁毅飞,徐焕良.基于深度主动学习与CBAM的细粒度菊花表型识别,农业机械学报,2024,(Participating authors)

Xie Xiaojun,Xia Fei,Wu Yufeng,Liu Shouyang,Yan Ke,Xu Huanliang,Ji Zhiwei.A Novel Feature Selection Strategy Based on Salp Swarm Algorithm for Plant Disease Detection,PLANT PHENOMICS,2023,2023(Participating authors)

Zhao Qingsong,Liu Ximeng,Xu Huanliang,Li Yanbin.Practical reusable garbled circuits with parallel updates,COMPUTER STANDARDS & INTERFACES,2023,86(Participating authors)

Wu Xuehui,Luo Zhiwei,Xu Huanliang.Recognition of pear leaf disease under complex background based on DBPNet and modified mobilenetV2,IET IMAGE PROCESSING,2023,17(10):3055-3067(Correspondence Author)

Yuan Peisen, Xu Shuning, Zhai Zhaoyu, Xu Huanliang.Phenotype Analysis of Arabidopsis thaliana Based on Optimized Multi-Task Learning,MATHEMATICS,2023,11(18):-(Participating authors)

Yang Ruotong, Chen Zikang, Xu Huanliang, Shen Mingxia, Li Pinghua, Norton Tomas, Lu Mingzhou.Recognizing the rooting action of prepartum sow in free-farrowing pen using computer vision,COMPUTERS AND ELECTRONICS IN AGRICULTURE,2023,213:-(Participating authors)

Yuan Peisen,Xu Shuning,Zhai Zhaoyu,Xu Huanliang.Research of intelligent reasoning system of Arabidopsis thaliana phenotype based on automated multi-task machine learning,FRONTIERS IN PLANT SCIENCE,2023,14(Participating authors)

顾兴健,刘子儒,任守纲,郑恒彪,徐焕良.基于注意力机制和边缘感知的田梗提取模型,农业机械学报,2023,54(2023年05期):210-218(Participating authors)

Liu Chao,Cao Yifei,Wu Ejiao,Yang Risheng,Xu Huanliang,Qiao Yushan.A Discriminative Model for Early Detection of Anthracnose in Strawberry Plants Based on Hyperspectral Imaging Technology,REMOTE SENSING,2023,15(18)(Participating authors)

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