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王儒敬——研究生院科学岛分院（中科院合肥物质研究院）

时间：2023-11-05

王儒敬：中国科学院合肥物质科学研究院合肥智能机械研究所总工程师（原所长），二级研究员，中科合肥智慧农业谷有限责任公司总经理，中国科学技术大学教授、博士生导师；兼任国家自动化协会智慧农业专业委员会主任，农业传感器与智能感知安徽省技术创新中心主任，安徽省仿生感知与先进机器人技术重点实验室主任，安徽省安徽省智慧农业工程实验室主任，模式识别与人工智能杂志副主编；国家科技部十三五、十四五重点专项领域专家，安徽省数字农业产业体系首席专家；国内外核心期刊发表学术论文200多篇，学术专著1部，获得国家发明专利100余项；获得国家科技进步二等奖1项，安徽省科技进步一等奖2项，享誉国务院津贴。



研究方向：智慧农业，20多年从事农业人工智能的理论方法研究与智能装备开发。主持国家863课题、国家科技支撑、国家重点专项、世行农业科技基金、国家自然基金、中科院STS重点专项、安徽省重大科技攻关项目等科研课题60余项。突破：高通量、低成本、智能化土壤速测技术，研制成功首台套土壤高通量智能检测机器人，实现复杂土壤成分检测智能化，已由比亚迪代工量产；突破复杂自然条件下病虫草害智能识别技术，获国际人工智能挑战赛冠军，研制成功作物四情（苗情、墒情、灾情、病虫情）探测智能装备，农业部发文全国推广，广泛用于我国各种测报装置。


发表文章：

  1. Rujing Wang, Rui Li*, Tiaojiao Chen, Jie Zhang*, Chenjun Xie,Kun Qiu,Peng Chen, Jianming Du, Hongbo Chen, Fanglong Shao,Haiying Hu, Haiyun Liu.An automatic system for pest recognition and forecasting, Pest Management Science, 2021,DOI 10.1002/ps.6684

  2. Shifeng Dong , Rujing Wang* Jianming Du and Lin Jiao. Enhancement-fusion feature pyramid networkfor object detection, Journal of Electronic Imaging, 2023,32(1):013045-013059

  3. Xiaobo Hu, Rujing Wang*, Jianming Du , Yimin Hu ,Lin Jiaoand Taosheng Xu*. Class-attention-based lesionproposal convolutional neuralnetwork for strawberrydiseases identifification , Frontiers in Plant Science, 2023, 10.3389/fpls.2023.1091600

  4. Qiong Zhou , Ziliang Huang, Shijian Zheng, Lin Jiao*,Liusan Wang* and Rujing Wang*. A wheat spike detection method based on Transformer, Frontiers in Plant Science,2023,10.3389/fpls.2022.1023924

  5. Tianjiao Chen, Rujing Wang*, Jianming Du*, Hongbo Chen, Jie Zhang, Wei Dong,Meng Zhang. CMRD-Net: A Deep Learning-BasedCnaphalocrocis medinalis DamageSymptom Rotated Detection Frameworkfor In-Field Survey, frontiers in plant science, 2023,14, DOI 10.3389/fpls.2023.1180716

  6. Junqing Zhang, Rujing Wang *, Zhou Jin, Hongyan Guo , Yi Liu , Yongjia Chang, Jiangning Chen, Mengya Li and Xiangyu Chen. Development of On-Site Rapid Detection Device for Soil Macronutrients Based on Capillary Electrophoresis and Capacitively Coupled Contactless Conductivity Detection (C4D) Method , chemosensors, 2022, 10, 84.doi.org/10.3390/chemosensors10020084

  7. Yue Teng, Rujing Wang*, Jianming Du, Ziliang Huang , Qiong Zhou  and Lin Jiao. TD-Det: A Tiny Size Dense Aphid Detection Network under In-Field Environment ,insects, 2022,13(6):501, doi: 10.3390/insects13060501.

  8. Ziliang Huang , Rujing Wang , Ying Cao , Shijian Zheng , Yue Teng , Fenmei Wang , Liusan Wang ∗ , Jianming Du . Deep learning based soybean seed classification,Computers and Electronics in Agriculture, 2022, 202 ,107393

  9. Wang R, Liu L, Xie C, Yang P, Li R. AgriPest: A Large-Scale Domain-Specific Benchmark Dataset for Practical Agricultural Pest Detection in the Wild, Sensors,2021,doi:10.3390/s21051601.

  1. Wang X, Zhang Z, Wang X, Bao Q, Wang R*, Duan Z. The Impact of Host  Genotype, Intestinal Sites and Probiotics Supplementation on the Gut Microbiota Composition and Diversity in Sheep. Biology ,2021, 10:769. 

  2. Wang X, Zhang  Z, Yin W, Zhang J, Wang R*, Duan Z. Interactions between Cryptosporidium, Enterocytozoon, Giardia and Intestinal Microbiota in Bactrian Camels on Qinghai-Tibet Plateau, China. Applied Sciences 11(8):3595,doi:10.3390/app11083595

  3. Zhou Man, Liu Liu, and Rujing Wang*. "ReinforceDet: Object Detection by Integrating Reinforcement Learning with Decoupled Pipeline. ", International Conference on Image Processing (ICIP) 2021.

  4. Man Zhou and Rujing Wang*. Control Theory-Inspired Model Design for Single Image De-raining. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS，2021.

  5. Wang F, Wang R*, Xie C, Yang P, Liu L. Fusing multi-scale context-aware information representation for automatic in-field pest detection and recognition,Computers and Electronics in Agriculture, 2020,105222.

  6. Liu L, Wang R*, Xie C, Yang P, Liu L, Zhang J, Wang F, Sudirman S, Li R. Deep Learning based Automatic Multi-Class Wild Pest Monitoring Approach using Hybrid Global and Local Activated Features,IEEE Transactions on Industrial Informatics, 2020.

  7. Zhao Y, Liu L, Xie C, Wang R*, Wang F, Bu Y, Zhang S. An effective automatic system deployed in agricultural Internet of Things using Multi-Context Fusion Network towards crop disease recognition in the wild,Applied Soft Computing, 2020.

  8. Li D, Wang R*, Xie C, Liu L, Zhang J, Li R, Wang F, Zhou M. A Recognition Method for Rice Plant Diseases and Pests Video Detection Based on Deep Convolutional Neural Network,Sensors, 2020, 20(3).

  9. Liu L, Wang R*, Xie C, Yang P, Wang F, Sudirman S, Liu W. PestNet: An End-to-End Deep Learning Approach for Large-Scale Multi-Class Pest Detection and Classification,IEEE Access, 2019: 45301-45312.

  10. Li R, Jia X, Hu M, Zhou M, Li D, Liu W, Wang R*, Zhang J, Xie C, Liu L, Wang F, Chen H, Chen T, Hu H. An Effective Data Augmentation Strategy for CNN-Based Pest Localization and Recognition in the Field,IEEE Access, 2019: 160274-160283.

  11. Yushan Zhao, Liu Liu, Chengjun Xie, Rujing Wang*, Fangyuan Wang, Yingqiao Bu, and Shunxiang Zhang. An effective automatic system deployed in agricultural Internet of Things using Multi­Context Fusion Network towards crop disease recognition in the wild. Applied Soft Computing,89 (2020): 106128.

  12. Liu L, Wang R*, Xie C, Li R, Teng Y. Learning Region-Guided Scale-Aware Feature Selection for Object Detection,Neural Computing and Applications(4), 1-15.2020.

  1. Liu L, Wang R*, Xie C, Yang P, Zhang J, Wang F, Sudirman S, Li R. Deep Learning based Automatic MultiClass Wild Pest Monitoring Approach using Hybrid Global and Local Activated Features.” IEEE Transactions on Industrial Informatics. 2020.

  2. Liu Liu, Rujing Wang*, Chengjun Xie, Po Yang, Jie Zhang, Fangyuan Wang,Sud Sudirman, Rui Li. ”Deep Learning based Automatic Multi­Class Wild Pest

  1. Monitoring Approach using Hybrid Global and Local Activated Features.” IEEE Transactions on Industrial Informatics.

  2. Liu Liu, Rujing Wang*, Chengjun Xie, Po Yang, Fangyuan Wang, Sud Sudirman, and Wancai Liu. ”PestNet: An end­to­end deep learning approach for large­scale multi­class pest detection and classification.” IEEE Access. 7 (2019): 4530145312.

  3. Li R, Wang R*, Xie C, Liu L, Zhang J, Wang F, Liu W. A coarse-to-fine network for aphid recognition and detection in the field,Biosystems Engineering, 2019: 39-52.

  4. Li R, Wang R*, Zhang J, Xie C, Liu L, Wang F, Jia X, Hu M, Zhou M, Li D, Liu W, Chen H, Chen T, Hu H. An Effective Data Augmentation Strategy for CNN-Based Pest Localization and Recognition in the Field,IEEE Access, 2019: 160274-160283.

  5. Shu Yan, Caoyuan Cui, Bingyu Sun, and Rujing Wang*. Markov Boundary discovery based on variant ridge regularized linear models, IEEE ACCESS, 2019. doi: https://doi.org/10.1109/ACESS.2019.2924341.

  1. Hongyan Guo, Aiwu Zhao, Qinye He, Ping Chen, Yuanyuan Wei, Xiangyu Chen, Haiying Hu, Min Wang, He Huang, Rujing Wang*. Multifunctional Fe3O4@mTiO2@noble metal composite NPs as ultrasensitive SERS substrates for trace detection. Arabian Journal of Chemistry, 2019, doi: 10.1016/j.arabjc.2019.01.007

  2. SU Ya-Ru，RJ Wang，P Chen，YY Wei，LI Chuan-Xi，and HU Yi-min. Agricultural Ontology Based Feature Optimization for Agricultural Text Clustering. Journal of Integrative Agriculture,2012.

  3. WEI Yuan-yuan, WANG Ru-jing, HU Yi-minand WANG Xue. From Web Resources to Agricultural Ontology: a Method for Semi-automatic Construction. Journal of Integrative Agriculture, 2012, 11(5): 775-783.

  4. Y Wang，C Lu，L Wang，L Song，R Wang，Y Ge. Prediction of Soil Organic Matter Content Using VIS/NIR Soil Sensor.Sensors & Transducers, 2014,168(4):113-119.

  5. Rujing Wang , Xiaoming Zhang. Particle swarm optimization with opposite particles. MICAI 2005: Advances in Artificial Intelligence, 2005, 3789: 633-640.

  6. Yujie Li, Rujing Wang*, Wei Li, Man Zhou, Yan Wu. Cross-grained context guided Chinese entity extraction with graph convolutional network[C]. Proc.SPIE 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence.2020.

  1. Liu L, Wang R*, Xie C, Yang P, Sudirman S, Wang F, Li R. Deep Learning based Automatic Approach using Hybrid Global and Local Activated Features towards Large-scale Multi-class Pest Monitoring[C]//INDIN 2019:vol. 1, pp.  1507-1510.

  2. Shu Yan，Xiaobo Hu, Rujing Wang*. Alternative models for the modified form of ridge regularized linear model in discovering Markov boundary [C], The Second International Conference on Physics, Mathematics and Statistics.

  1. Yuanmiao Gui*, Rujing Wang, Yuanyuan Wei, Xue Wang. Construction of protein­protein interactions model by deep neural networks. 2018 International Workshop on Bioinformatics, Biochemistry, Biomedical Sciences (BBBS 2018). DOI: https://dx.doi.org/10.2991/bbbs­18.2018.47

  1. 王儒敬,　檀敬东, 黄河. 一种新的复杂自适应搜索模型, 模式识别与人工智, 2009, 22(6), 815-820.

  2. 王儒敬,葛运健, 滕明贵, 张晓明, 基于粗集的空间对象分类学习算法, 中国科学技术大学学报, 2006,36(2):163-169.

  3. RJ Wang, XM Zhang, Particle Swarm Optimization with Opposite Particles, Lecture Notes in Artificial Intelligence-,2005,3789:633-640.

  4. 王儒敬，滕明贵, 一种用于空间对象属性预测的空间广义线性回归模型, 模式识别与人工智能,2005,18(6):798-712.

  5. 王儒敬，白石磊，毛雪岷. 大型知识库存储结构的研究, 计算机工程, 2003, 21(2003): 25-27.






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