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姓 名：高攀
职 称：教授
办公室：信息工程学院 112室
邮 箱：pangao@nwafu.edu.cn
基本信息 个人简介：
高攀，男，汉族，中共党员，西北农林科技大学青年教授，主要从事作物生理-生境互作建模、设施小气候管控决策、水肥一体化智能决策研究，获西北农林科技大学优秀博士毕业论文，主持国家自然基金青年项目和中国博士后基金面上资助1项，以第一作者发表中科院一区top论文8篇，以通讯/合作作者发表中科院一区论文10余篇，授权发明专利7项，登记软件著作权5项，获陕西省高等学校科技成果一等奖1项、挑战杯全国总决赛金奖1项。欢迎对农业智能化生产决策研究感兴趣的硕博士研究生报考。
学习工作经历
2013.09-2017.06，中国民航大学，电气工程及其自动化，本科
2018.09-2024.06，西北农林科技大学，农业电气化与自动化，硕博连读
2024.07-2026.06，西北农林科技大学，水利工程流动站，博士后2026.07-今， 西北农林科技大学，信息工程学院，青年教授
获奖情况
[1] 陕西高等学校科学技术研究优秀成果奖，等级：一等奖，项目名称：面向作物需求的设施环境多因子协同调控技术与装备研发[2] 第十四届“挑战杯”大学生创业竞赛. 全国金奖.
[3] 第四届全国大学生智能农业装备大赛. 国家级一等奖.
[4] 第十八届中国研究生数学建模竞赛. 三等奖.
[5] 2019年人工智能与机器人竞赛. 国家级三等奖.
[6] 2022年陕西省科技工作者创新创业大赛. 三等奖.
[7] 第七届中国国际“互联网+”大学生创新创业大赛. 陕西赛区铜奖.
[8] 第四届陕西省博士后创新创业大赛优胜奖.
[9] 2024年度西北农林科技大学优秀博士毕业生、优秀博士论文.
[10] 2025年度信息工程学院“科学研究贡献奖”、“年度先进工作者”.
研究方向 作物生理-生境互作建模、设施小气候管控决策、水肥一体化智能决策研究
1.融合番茄生产过程中的作物生理、冠层成像和环境等多模态信息，通过深度时序处理实现对作物生理特征、形态表型特征高精度预测，探明作物生长机制，为生产调控奠定基础。
2.解析多环境因子对作物生长发育的耦合作用机制，量化关键环境因子的敏感性与临界区间，揭示生长表型随时间和环境变化的动态规律，建立以目标产量、品质和生产性能为约束的评价指标体系，研究面向不同需求导向的多环境协同调控方法。
3.结合数字孪生技术，构建大田水肥一体化与环境调控的在线仿真系统，利用生产反馈数据持续修正模型参数，实现从感知、预测到决策的闭环优化，推动农业生产向自适应、智能化的精准调控模式转变。
开设课程 《机器学习》 周志华著 清华大学出版社 学术成果 以第一/通讯作者发表的学术论文： [1] Pan Gao, Bin Li, Jinghua Bai, Miao Lu, Pan Feng, Huarui Wu, Jin Hu*. 2021. Method for optimizing controlled conditions of plant growth using U-chord curvature. Computers and Electronics in Agriculture. 185: 106141. https://doi.org/10.1016/j.compag.2021.106141. （中科院一区 Top, Q1, IF: 10.7）
[2] Pan Gao, Ziwei Tian, Youqi Lu, Miao Lu, Haihui Zhang, Huarui Wu*, Jin Hu*. 2022. A decision-making model for light environment control of tomato seedlings aiming at the knee point of light-response curves. Computers and Electronics in Agriculture. 198: 107103. https://doi.org/10.1016/j.compag.2022.107103. （中科院一区 Top, Q1, IF: 10.7）
[3] Pan Gao, Miao Lu, Huimin Li, Hanping Mao, Jin Hu*, Huarui Wu*. 2023. Greenhouse environmental control target constrained by discrete surface curvature and multi-objective optimization algorithm. Computers and Electronics in Agriculture. 215: 108431. https://doi.org/10.1016/j.compag.2023.108431. （中科院一区 Top, Q1, IF: 10.7）
[4] Pan Gao, Miao Lu, Yongxia Yang, Huarui Wu*, Hanping Mao, Jin Hu*. 2024. Greenhouse light and CO2 regulation considering cost and photosynthesis rate using i-nsGA II. Expert Systems With Applications. 237: 121680. https://doi.org/10.1016/j.eswa.2023. 121680. （中科院一区 Top, Q1, IF: 9.4）
[5] Pan Gao, Miao Lu, Jinghua Xu, Hongming Zhang, Yanfeng Li, Jin Hu*, Hongming Zhang. 2024. IPECM Platform: An open-source software for greenhouse environment regulation using machine learning and optimization algorithm. Computers and Electronics in Agriculture. 217: 108564. https://doi.org/10.1016/j.compag.2023.108564. （中科院一区 Top, Q1, IF: 10.7）
[6] Pan Gao, Miao Lu, Yongxia Yang, Huimin Li, Shijie Tian, Jin Hu*. 2025. A predictive model of photosynthetic rates for eggplants Integrating physiological and environmental parameters. Computers and Electronics in Agriculture. 234, 11024. https://doi.org/10.1016/j.compag.2025.110241.（中科院一区 Top, Q1, IF: 10.7）
[7] Pan Gao, Yongxia Yang, Huimin Li, Jinghua Xu, Shijie Tian, Jin Hu*. 2026. Preference-informed multi-objective optimization for energy-saving light environment in greenhouse cucumber seedlings. Expert Systems with Applications. 309, 131221. https://doi.org/10.1016/j.eswa.2026.131221.（中科院一区 Top, Q1, IF: 9.4）
[8] Pan Gao, Huimin LI, Jinghua Xu, Miao Lu, Jin Hu*. 2026. A decision-making method for light regulation of cucumber seedlings considering changes of temperature and CO2 in protected agriculture. Biosystems Engineering. 272, 104601. https://doi.org/10.1016/j.biosystemseng.2026.104601.（新锐一区 Top, Q1, IF: 7.8）
[9] Zhangtong Sun, Yongxia Yang, Miao Lu, Huimin Li, Jiexiao Peng, Shijie Tian, Jin Hu*, Pan Gao*. 2025. Real-Time Nitrogen Regulation via IoT Edge Computing: A Chlorophyll Fluorescence-Driven Framework for Sustainable Plant Factories. IEEE Internet of Things Journal. 12, 34432-34445. https://doi.org/10.1109/JIOT.2025.3578311.（新锐一区 Top, Q1, IF: 8.7）
授权发明专利：
[1] 胡瑾, 高攀, 荆昊男, 卢苗, 完香蓓. 2021. 一种光质优先的设施光环境调控方法. ZL202010983695.5.
[2] 胡瑾, 高攀, 陈丹艳, 张盼, 李斌, 张海辉. 2022. 融合叶片光合潜能的光合速率预测方法. ZL201910576601.X.
[3] 胡瑾, 高攀, 卢苗, 侯军英, 李慧敏, 蒲六如. 2023. 一种设施光与二氧化碳环境协同调控方法. ZL202210795688.1.
[4] 胡瑾, 白京华, 张海辉, 高攀, 张仲雄, 辛萍萍, 来海滨, 张盼. 2021. 一种基于调控效益优先的二维联合调控目标区域的获取方法. ZL201811031904.5.
[5] 胡瑾, 卢苗, 完香蓓, 袁凯凯, 高攀, 李斌. 2023. 一种基于QGA-SVR的冷害黄瓜PSII潜在活性预测方法. ZL202110042909.3.
[6] 胡瑾, 卢有琦, 雷文晔, 魏子朝, 高攀, 张瑶嘉. 2022. 面向碳中和需求的效率最优温室植物调控方法. ZL202110998843.5.
[7] 胡瑾, 冯盼, 张仲雄, 李斌, 高攀, 汪志胜. 2024. 茎流信号采集节点和利用该节点的基于温补偿的热源自适应茎流测量系统. ZL201910940858.9.
学生培养 课题组特别欢迎各位同学加入！
研究领域具有多学科高度交叉的特点，欢迎有计算机科学与技术、农业工程、光学工程、控制工程、电子信息、农学、园艺科学、植物科学、智慧农业等专业背景的学生联系我攻读硕士/博士学位。
主持科研项目 主持科研项目经历：
[1]国家自然基金青年C类项目：生境-荧光生理协同的水培番茄氮肥决策方法研究
[2]中国博士后基金面上资助项目：融合表型变化的设施生菜水肥调控决策方法研究
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Name: Gao Pan 
Title: Professor Office: Room 112, School of Information Engineering Email: pangao@nwafu.edu.cn Personal Profile Gao Pan, male, Han nationality, a member of the Communist Party of China, is a professor at Northwest A&F University. He is mainly engaged in research on crop physiology habitat interaction modeling, protected microclimate control decision-making, and water fertilizer integration intelligent decision-making. He has won an excellent doctoral dissertation from Northwest A&F University, presided over the National Natural Science Foundation Youth Project and one general support from the China Postdoctoral Fund, published 8 top papers in the first district of the Chinese Academy of Sciences as the first author, published more than 10 papers in the first district of the Chinese Academy of Sciences as the communication/cooperation author, authorized 7 invention patents, registered 5 software copyrights, and won one first prize for scientific and technological achievements of Shaanxi universities, and one gold prize for the national finals of the Challenge Cup. Welcome master's and doctoral students interested in research on agricultural intelligent production decision-making to apply. Research Direction 1. By integrating multimodal information such as crop physiology, canopy imaging, and environment during tomato production, high-precision prediction of crop physiological and morphological phenotype characteristics can be achieved through deep temporal processing, exploring crop growth mechanisms and laying the foundation for production regulation.
2. Analyze the coupling mechanism of multiple environmental factors on crop growth and development, quantify the sensitivity and critical range of key environmental factors, reveal the dynamic laws of growth phenotype changes over time and environment, establish an evaluation index system constrained by target yield, quality, and production performance, and study multi environment collaborative regulation methods oriented towards different needs.
3. Combining digital twin technology, build an online simulation system for integrated water and fertilizer management and environmental regulation in the field, continuously adjust model parameters using production feedback data, achieve closed-loop optimization from perception, prediction to decision-making, and promote the transformation of agricultural production to an adaptive and intelligent precise regulation mode.
Course Machine Learning, Zhou Zhihua, published by Tsinghua University Press Academic achievements Academic papers published as the first/corresponding author:
[1] Pan Gao, Bin Li, Jinghua Bai, Miao Lu, Pan Feng, Huarui Wu, Jin Hu*. 2021. Method for optimizing controlled conditions of plant growth using U-chord curvature. Computers and Electronics in Agriculture. 185: 106141. https://doi.org/10.1016/j.compag.2021.106141. （Q1, IF: 10.7）
[2] Pan Gao, Ziwei Tian, Youqi Lu, Miao Lu, Haihui Zhang, Huarui Wu*, Jin Hu*. 2022. A decision-making model for light environment control of tomato seedlings aiming at the knee point of light-response curves. Computers and Electronics in Agriculture. 198: 107103. https://doi.org/10.1016/j.compag.2022.107103. （Q1, IF: 10.7）
[3] Pan Gao, Miao Lu, Huimin Li, Hanping Mao, Jin Hu*, Huarui Wu*. 2023. Greenhouse environmental control target constrained by discrete surface curvature and multi-objective optimization algorithm. Computers and Electronics in Agriculture. 215: 108431. https://doi.org/10.1016/j.compag.2023.108431. （Q1, IF: 10.7）
[4] Pan Gao, Miao Lu, Yongxia Yang, Huarui Wu*, Hanping Mao, Jin Hu*. 2024. Greenhouse light and CO2 regulation considering cost and photosynthesis rate using i-nsGA II. Expert Systems With Applications. 237: 121680. https://doi.org/10.1016/j.eswa.2023. 121680. （Q1, IF: 9.4）
[5] Pan Gao, Miao Lu, Jinghua Xu, Hongming Zhang, Yanfeng Li, Jin Hu*, Hongming Zhang. 2024. IPECM Platform: An open-source software for greenhouse environment regulation using machine learning and optimization algorithm. Computers and Electronics in Agriculture. 217: 108564. https://doi.org/10.1016/j.compag.2023.108564. （Q1, IF: 10.7）
[6] Pan Gao, Miao Lu, Yongxia Yang, Huimin Li, Shijie Tian, Jin Hu*. 2025. A predictive model of photosynthetic rates for eggplants Integrating physiological and environmental parameters. Computers and Electronics in Agriculture. 234, 11024. https://doi.org/10.1016/j.compag.2025.110241.（Q1, IF: 10.7）
[7] Pan Gao, Yongxia Yang, Huimin Li, Jinghua Xu, Shijie Tian, Jin Hu*. 2026. Preference-informed multi-objective optimization for energy-saving light environment in greenhouse cucumber seedlings. Expert Systems with Applications. 309, 131221. https://doi.org/10.1016/j.eswa.2026.131221.（Q1, IF: 9.4）
[8] Pan Gao, Huimin LI, Jinghua Xu, Miao Lu, Jin Hu*. 2026. A decision-making method for light regulation of cucumber seedlings considering changes of temperature and CO2 in protected agriculture. Biosystems Engineering. 272, 104601. https://doi.org/10.1016/j.biosystemseng.2026.104601.（Q1, IF: 7.8）
[9] Zhangtong Sun, Yongxia Yang, Miao Lu, Huimin Li, Jiexiao Peng, Shijie Tian, Jin Hu*, Pan Gao*. 2025. Real-Time Nitrogen Regulation via IoT Edge Computing: A Chlorophyll Fluorescence-Driven Framework for Sustainable Plant Factories. IEEE Internet of Things Journal. 12, 34432-34445. https://doi.org/10.1109/JIOT.2025.3578311.（Q1, IF: 8.7）