个人资料
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教育经历
个人简介张领先，博士，教授，博士生导师，全国物品编码标准化技术委员会农产品食品编码分技术委员会委员、农业农村部农业信息化标准化重点实验室副主任、农业机械学报第十届编委会编委。2006年中国农业大学管理科学与工程博士研究生毕业留校工作至今，主要从事人工智能农业应用、农业信息化标准化与政策等方面研究。 教学科研概况近年来，主持完成国家社会科学基金重点项目“基于效率提升的农业支持政策传导机制与路径优化研究（14AJY019）”（2014.1-2016.12）、北京市社会科学基金重点项目“北京自产蔬菜流通渠道优化及其电子商务发展模式（16YJA007）”（2016.7-2018.12）和面上项目“北京都市型蔬菜产业经营体制创新研究（13JGB040）”（2013.7-2015.6）、北京市项目“现代农业产业技术体系北京市叶类蔬菜创新团队（产业经济岗位）建设（BAIC07-20）”（2012.4-2021.12），作为主要参加人完成国家自然科学基金项目“蔬菜病害视频的声像镜头聚类方法与自动语义标注模型研究（31271618）”等项目6项。目前，正在主持国家自然科学基金面上项目“基于机器视觉的蔬菜霜霉病菌侵染特征提取与致病机理解析（62176261）”（2022.1-2025.12）和“基于电子病历多模态数据融合的作物病害关联挖掘与多目标决策（62376272）”（2024.1-2027.12）等项目。 发表学术论文135篇（被SCI/SSCI/EI检索66篇），其中以第一作者或通讯作者被检索47篇（SCI 20篇，SSCI 1篇，EI 27篇）；研究成果“WTO框架下我国国内农业支持水平与结构优化研究”荣获2008年北京市科学技术三等奖（排名第二），“面向移动终端的农业信息智能获取关键技术及应用”荣获教育部2014年度高等学校科学研究优秀成果奖（科学技术）科技进步二等奖（排名第二），“蔬菜病害识别诊断与预警物联网关键技术研究与示范（排名第三）”荣获2015年度第九届大北农科技奖创新奖二等奖，“都市农业智慧植保技术体系创建与应用（排名第三）”荣获2020-2021年度神农中华农业科技奖科学研究类成果三等奖；授权专利20项，其中发明专利15项；出版学术专著8部。 主讲本科生课程《经济信息管理》《现代农业概论——信息技术与智慧农业》，研究生课程《农业信息化工程案例分析》。 社会职务活动动态研究领域主要从事人工智能农业应用、农业信息化标准化与政策等方面研究。 开授课程
本科生课程：近十年课程数据
科研项目
纵向项目
论文
论文[1] Xu Chang, Zhao Lei , Wen Haojie , Zhang Lingxian(通讯作者). Spatial-temporal analysis and trend prediction of regional crop disease based on electronic medical records. Applied Soft Computing, 2024, 167:112423(SCI 2023年IF: 8.4, Q1). [2] Zhu Xinyi, Chen Feifei, Qiao Chen, Zhang Yiding*, Zhang Lingxian*, Gao Wei, Wang Yong. Cucumber pathogenic spores’ detection using the GCS-YOLOv8 network with microscopic images in natural scenes. Plant Methods, 2024, 20: 131(SCI 2023年IF: 5.6, Q1). [3] Xu Chang, Zhao Lei , Wen Haojie , Zhang Yiding(共同通讯作者)，Zhang Lingxian(共同通讯作者). A novel cascaded multi-task method for crop prescription recommendation based on electronic medical record. Computers and Electronics in Agriculture, 2024, 219: 108790(SCI 2022年IF: 8.3, Q1) . [4] Xu Chang, Zhang Lingxian(通讯作者). Cucumber diseases diagnosis based on multi-class SVM and electronic medical record. Neural Computing and Applications, 2024,36:4959–4978: (SCI 2022年IF: 6.0, Q2). [5] Ding Junqi, Li Bo, Xu Chang, Qiao Yan, Zhang Lingxian(通讯作者). Diagnosing crop diseases based on domain-adaptive pre-training BERT of electronic medical records. Applied Intelligence，2023, 53(12): 15979-15992 (SCI 2022年IF: 5.3, Q2). [6] Jing Jiaping, Li Shufei, Qiao Chen, Li Kaiyu, ZhuXinyi, ZhangLingxian (通讯作者). A tomato disease identification method based on leaf image automatic labeling algorithm and improved YOLOv5 model. Journal of The Science of Food and Agriculture，2023, 103: 7070–7082(SCI). [7] Ding Junqi, Qiao Yan, Zhang Lingxian (通讯作者). Plant disease prescription recommendation based on electronic medical records and sentence embedding retrieval. Plant Methods, 2023, 19: 91(SCI 2022年IF: 5.1, Q1). [8] 李淑菲，李凯雨，乔岩，张领先(通讯作者). 基于可见光光谱和改进YOLOv5的自然场景下黄瓜病害检测方法. 光谱学与光谱分析，2023, 43(08): 2596-2600 (SCI). [9] Ding Junqi, Li Bo, Zhang Lingxian (通讯作者). Risk analysis of agricultural input management and its drivers and obstacles: a case study of vegetable production enterprises in Beijing. British Food Journal, 2023, 125(6): 2176-2189 (SCI 2021年IF: 3.224, Q2). [10] 李凯雨，张慧，马浚诚，张领先(通讯作者). 基于语义分割和可见光谱图的作物叶部病斑分割方法. 光谱学与光谱分析，2023, 43(04): 1248-1253 (SCI/EI). [11] 李云霞，马浚诚，刘红杰，张领先(通讯作者). 基于可见光谱与轻量级卷积神经网络的冬小麦分蘖数估算. 光谱学与光谱分析，2023, 43(01): 273-279 (SCI/EI). [12] Li Kaiyu, Zhu Xinyi, Qiao Chen, Zhang Lingxian(通讯作者), Gao Wei, Wang Yong. The Gray Mold Spore Detection of Cucumber Based on Microscopic Image and Deep Learning. Plant Phenomics, 2023, 5: 0011(SCI 2022年IF: 6.5, Q1). [13] Li Shufei, Li Kaiyu, Qiao Yan, Zhang Lingxian(通讯作者). A multi-scale cucumber disease detection method in natural scenes based on YOLOv5. Computers and Electronics in Agriculture, 2022, 202: 107363(SCI 2022年IF: 8.3, Q1). [14] Li Kaiyu, Zhang Lingxian(通讯作者), Li Bo, Shufei Li, Ma Juncheng*. Attention-optimized DeepLab V3+for automatic estimation of cucumber disease severity. Plant Methods, 2022, 18:109 (SCI 2021年IF: 5.827, Q1) . [15] Xu Chang, Ding Junqi, Qiao Yan, Zhang Lingxian(通讯作者). Tomato disease and pest diagnosis method based on the Stacking of prescription data. Computers and Electronics in Agriculture, 2022, 197: 106997(SCI 2022年IF: 8.3, Q1). [16] Zhao Xue, Li Kaiyu, Li Yunxia, Ma Juncheng, Zhang Lingxian(通讯作者). Identification method of vegetable diseases based on transfer learning and attention mechanism. Computers and Electronics in Agriculture，2022, 193: 106703(SCI 2022年IF: 8.3, Q1). [17] Yin Zhengqing, Li Bo, Li Shufei, Ding Junqi, Zhang Lingxian(通讯作者). Key influencing factors of green vegetable consumption in Beijing, China. Journal of Retailing and Consumer Services, 2022, 66: 102907 (SCI/SSCI 2021年IF: 10.972, Q1). [18] Yin Zhengqing, Li Bo, Gu Dongyue, Huang Jian, Zhang Lingxian(通讯作者). Modeling of farmers' vegetable safety production based on identification of key risk factors from Beijing, China. Risk Analysis, 2022, 42(9): 2089-2106(SCI 2021年IF: 4.302, Q1) . [19] Li Yunxia, Liu Hongjie, Ma Juncheng*, Zhang Lingxian(通讯作者). Estimation of leaf area index for winter wheat at early stages based on convolutional neural networks. Computers and Electronics in Agriculture, 2021, 190: 106480 (SCI 2021年IF: 6.757, Q1). [20] Li Bo, Ding Junqi, Yin Zhengqing, Li Kaiyu, Zhao Xue, Zhang Lingxian(通讯作者). Optimized neural network combined model based on the induced ordered weighted averaging operator for vegetable price forecasting. Expert systems with applications, 2021,168:114232 (SCI 2021年IF: 8.665 ESI 10%) [21] Li Bo, Ding Junqi, Wang Jieqiong, Zhang Biao, Zhang Lingxian(通讯作者). Key influencing factors affecting the adoption willingness, behavior, and willingness behavior consistency of farmers regarding photovoltaic agriculture in China. Energy Policy, 2021, 149：112101(SCI 2021年IF:7.576 ESI 10%）. [22] Zhang Lingxian, Xu Zanyu, Xu Dan, Ma Juncheng (通讯作者), Chen Yingyi, Fu Zetian. Growth monitoring of greenhouse lettuce based on a convolutional neural network. Horticulture Research, 2020, 7: 124-135(SCI 2020年 IF: 6.793 ESI 10%；Nature子刊）. [23] Zhang Lingxian, Wang Jieqiong, Wen Haojie, Fu Zetian, Li Xinxing(通讯作者).Operating performance, industry agglomeration and its spatial characteristics of Chinese photovoltaic industry. Renewable and Sustainable Energy Reviews, 65(2016)373–386 (SCI/EI 2016年5-Year IF= 9.122, Q1）. [24] Zhang Biao, Fu Zetian, Huang Jian, Wang Jieqiong, Xu Shuyao, Zhang Lingxian(通讯作者). Consumers' perceptions, purchase intention, and willingness to pay a premium price for safe vegetables: A case study of Beijing, China. Journal of Cleaner Production, 2018, 197(1): 1498-1507(SCI 2018年ESI前10%，5-Year IF: 7.051). [25] Biao Zhang, Fu Zetian, Wang Jieqiong, Zhang Lingxian(通讯作者). Farmers’ adoption of water-saving irrigation technology alleviates water scarcity in metropolis suburbs: A case study of Beijing, China. Agricultural Water Management, 2019, 212: 349-357 (SCI 2019年5-Year IF:4.469 ESI 20%). [26] Li Bo, Yin Zhengqing, Ding Junqi, Xu Suyao, Zhang Biao, Ma Yunfei, Zhang Lingxian (通讯作者). Key influencing factors of consumers’ vegetable e-commerce adoption willingness, behavior, and willingness-behavior consistency in Beijing, China. British Food Journal, 2020, 122(12): 3741-3756(SCI). [27] 张领先，田潇，李云霞，陈运强，陈英义，马浚诚(通讯作者). 可见光光谱和机器学习的温室黄瓜霜霉病严重度定量估算. 光谱学与光谱分析，2020, 40(01): 227-232 (SCI). [28] Ma Juncheng, Li Yunxia,Liu Hongjie, Du Keming, Zheng Feixiang, Wu Yongfeng*,Zhang Lingxian(通讯作者). Improving segmentation accuracy for ears of winter wheat at flowering stage by semantic segmentation. Computers and Electronics in Agriculture, 2020, 176 (SCI). [29] Ma Juncheng, Li Yunxia,Du Keming, Zheng Feixiang,Zhang Lingxian (通讯作者),Zhihong Gong, Weihua Jiao. Segmenting ears of winter wheat at flowering stage using digital images and deep learning. Computers and Electronics in Agriculture, 2020, 168 (SCI). [30] Ma Juncheng,Du Keming*, Zheng Feixiang, Zhang Lingxian (通讯作者), Gong Zhihong, Sun Zhongfu. A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network. Computers and Electronics in Agriculture, 2018, 154: 18–24 (SCI). [31] Bai Xuebing, Li Xinxing, Fu Zetian, Lv Xiongjie, Zhang Lingxian (通讯作者). A fuzzy clustering segmentation method based on neighborhood grayscale information for defining cucumber leaf spot disease images. Computers and Electronics in Agriculture, 2017(136), 157-165 (SCI). [32] 张领先，丁俊琦，陈菲菲，李宜滨，张一丁(通讯作者). 基于电子病历多模态数据的作物病害多元场景处方推荐方法研究. 农业机械学报, 2025, 56(1):25-36(EI). [33] 乔琛，韩梦瑶，高苇，李凯雨，朱忻怡，张领先(通讯作者). 基于Faster-NAM-YOLO的黄瓜霜霉病菌孢子检测. 农业机械学报, 2023, 54(12): 288-299(EI). [34] 张领先(通讯作者) ，景嘉平，李淑菲，朱忻怡，乔琛. 基于图像自动标注与改进YOLO v5的番茄病害识别系统. 农业机械学报, 2023, 54(11):198-207(EI). [35] 张领先(通讯作者) ，韩梦瑶，丁俊琦，李凯雨. 作物病害智能诊断与处方推荐技术研究进展. 农业机械学报, 2023, 54(6): 1-18 (EI). [36] 张领先(通讯作者)，赵聃桐，丁俊琦，乔岩. 基于CDSSM的作物病害处方推荐方法[J]. 农业机械学报, 2023, 54(3): 308-317(EI). [37] 李凯雨，朱昕怡，马浚诚，张领先(通讯作者). 基于混合扩张卷积和注意力的黄瓜病害严重度估算方法[J]. 农业机械学报, 2023, 54(2): 231-239 (EI). [38] 丁俊琦，李博，乔岩，张领先. 基于植物电子病历多类型数据融合的作物病害诊断方法. 农业机械学报，2023, 54(1):196-204+223 (EI). [39] 徐畅，丁俊琦，赵聃桐，乔岩，张领先(通讯作者). 基于LightGBM和处方数据的番茄病害诊断方法[J]. 农业机械学报, 2022, 53(9): 286-294 (EI). [40] 李云霞，马浚诚，刘红杰，张领先 (通讯作者). 基于RGB图像与深度学习的冬小麦田间长势参数估算系统[J]. 农业工程学报, 2021, 37(24):189-198(EI). 科技成果
软件著作
荣誉及奖励
招生信息招收计算机科学与技术（学硕、博士）、智慧农业技术（专博）、计算机技术（专硕）、农业工程和信息技术（专硕）专业的研究生。 往期招生硕士研究生
博士研究生
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