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个人简介

刘小军,在南京农业大学获得农学学士、作物栽培学与耕作学硕士及农业信息学博士学位。现任国家信息农业工程技术中心副主任、江苏省信息农业重点实验室常务副主任、智慧农业系副主任等。主要从事作物精确 栽培、养分资源高效利用、基于地面/UAV 传感器的作物生长诊断与调控等方面的研究,作为主要完成人研究建立了作物管理知识模型及作物精确栽培理论体系等。先后主持国家自然科学基金、国家 863 计划、国家重点研发专项、江苏省自然科学基金、省科技支撑计划、省农业三新工程项目、省农业科技自主创新项目、中央高校基本科研业务费等多个科研项目;受国家留学基金委项目资助,赴美国内布拉斯加州大学精确农业实验室开展了 1 年的合作研究。在国内外核心期刊发表论文 100 余篇,其中 SCI 论文 40 余篇;授权国家发明专利 7 项,登记国家计算机软件著作权 17 项;参编《数字农作技术》专著 1 部、《农业信息学》及《精确农业概述》教材各 1 部。作为主要完成人,先后获得国家科技进步二等奖 2 项、教育部科技进步一等奖 2 项、江苏省科技进步一等奖 1 项、江苏省农业技术推广一等奖 1 项;并获国家粮丰工程优秀工作者、农学院优秀新人奖、优秀教师、优秀共产党员及优秀班主任等荣誉称号。指导的多名研究生分别获得了江苏省优秀硕士学位论文、南京农业大学校长奖学金、国家奖学金等荣誉称号。担任Computer and Electronics in Agriculture、Journal of Cleaner Production、Field Crops Research、Frontiers in Plant Science、Archives of Agronomy and Soil Science 等期刊审稿人。 承担的主要科研项目: 1. 多时相无人机图谱信息与临界氮稀释模型耦合的水稻氮素营养诊断研究,国 家自然科学基金项目,2021.1-2014.12. 2. 星-机-地作物长势实时监测与智能诊断平台构建,江苏省农业科技自主创新 项目,2020.8-2022.7. 3. 高标准农田多种生产模式下的资源安全高效利用与地力提升关键技术集成 与示范,江苏省重点研发计划(现代农业)重点项目任务课题,2019.7-2023.6. 4. 长江中下游小麦适宜指标动态模型及诊断调控,国家重点研发专项任务课 题,2016.1-2020.12. 5. 南方稻麦轮作区稻麦精准变量施肥管理模型研发,国家重点研发专项任务课 题,2016.1-2020.12. 6. 长江下游稻作区水稻临界氮浓度稀释模型及追氮调控方法研究,中央高校基 本科研业务费重点项目,2016.1-2018.12. 7. 作物精确管理技术在宜兴市的集成与示范,江苏省农业三新工程, 2016.5-2018.4. 8. 冬小麦植株适宜氮浓度模型及诊断指标研究,国家自然科学基金项目, 2013.1-2015.12. 9. 小麦适宜氮素指标动态模型及诊断方法研究,江苏省自然科学基金, 2013.1-2015.12. 10. 基于传感网的稻麦生长诊断与调控技术开发应用,江苏省农业三新工程, 2013.8-2015.7. 11. 麦稻精准农作平行管理系统实现关键技术,国家 863 计划,2012.1-2015.11. 12. 基于 Web 服务的数字农作管理系统研究, 江苏省科技支撑计划 , 2009.7-2012.7. 13. 种植业生产过程信息化关键技术与产品研发,国家科技支撑计划子课题, 2006.11-2009.10. 14. 农田感知与智慧管理技术服务,横向课题,2018.7-2018.12. 获得的主要科研奖励: 1. 稻麦生长指标光谱监测与定量诊断技术。2015 年,国家科技进步二等奖(排 名:6/10) 2. 基于模型的作物生长预测与精确管理技术。2008 年,国家科技进步二等奖(排 名:5/10) 3. 作物管理知识模型系统的构建与应用。2008 年,教育部科技进步一等奖(排 名:5/15) 4. 稻麦生长指标无损监测与精确诊断技术。2014 年,江苏省科技进步一等奖(排 名:6/11) 5. 稻麦精确管理技术的集成与推广。2017 年,江苏省农业技术推广一等奖(排 名:3/25)

研究领域

作物精确栽培、养分高效利用、作物生长诊断与调控

近期论文

查看导师新发文章 (温馨提示:请注意重名现象,建议点开原文通过作者单位确认)

1. Zhang K, Ma J, Wang Y, Cao W, Zhu Y, Cao Q, Liu X*, Tian Y. Key variable for simulating critical nitrogen dilution curve of wheat: leaf area ratio-driven approach. Pedosphere. 2020 (Accepted). 2. Jiang J, Zhang Z, Cao Q, Liang Y, Krienke B, Tian Y, Zhu Y, Cao W, Liu X*. Use of an active canopy sensor mounted on an unmanned aerial vehicle to monitor the growth and nitrogen status of winter wheat. Remote Sensing. 2020, 12, 3684. 3. Zhang K, Wang X, Wang X, Ata-Ul-Karim S, Tian Y, Zhu Y, Cao W, Liu X*. Does the organ-based N dilution curve improve the predictions of N status in winter wheat? Agriculture. 2020, 10, 500. 4. Jiang J, Wang C, Wang Y, Cao Q, Tian Y, Zhu Y, Cao W, Liu X*. Using an active sensor to develop new critical nitrogen dilution curve for winter wheat. Sensors. 2020, 20, 1577. 5. Wu Y, Qiu X, Zhang K, Chen Z, Pang A, Tian Y, Cao W, Liu X*, Zhu Y. A rice model system for determining suitable sowing and transplanting dates. Agronomy. 2020, 10, 604. 6. Fu Z, Jiang J, Gao Y, Krienke B, Wang M, Zhong K, Cao Q, Tian Y, Zhu Y, Cao W, Liu X*. Wheat growth monitoring and yield estimation based on multi-rotor unmanned aerial vehicle. Remote Sensing. 2020, 12, 508. 7. Zhang K, Yuan Z, Yang T, Lu Z, Cao Q, Tian Y, Zhu Y, Cao W, Liu X*. Chlorophyll meter–based nitrogen fertilizer optimization algorithm and nitrogen nutrition index for in-season fertilization of paddy rice. Agronomy Journal. 2020, 112: 288-300. 8. Zhang K, Liu X, Ma Y, Zhang R, Cao Q, Zhu Y, Cao W, Tian Y*. A comparative assessment of measures of leaf nitrogen in rice using two leaf-clip meters. Sensors. 2020, 20, 175. 9. Wang Y, Zhang K, Tang C, Cao Q, Tian Y, Zhu Y, Cao W, Liu X*. Estimation of rice growth parameters based on linear mixed-effect model using multispectral images from fixed-wing unmanned aerial vehicles. Remote Sensing. 2019, 11, 1371. 10. Zhang J, Liu X, Liang Y, Cao Q, Tian Y, Zhu Y, Cao W, Liu X*. Using a portable active sensor to monitor growth parameters and predict grain yield of winter wheat. Sensors. 2019, 19, 1108. 11. Zhang K, Liu X, Ata Ul-Karim S, Lu J, Krienke B, Li S, Cao Q, Zhu Y, Cao W, Tian Y*. Development of chlorophyll-meter-index-based dynamic models for evaluation of high-yield japonica rice production in Yangtze River Reaches. Agronomy. 2019, 9, 106. 12. Zhang K, Ge X, Shen P, Li W, Liu X, Cao Q, Zhu Y, Cao W, Tian Y*. Predicting rice grain yield based on dynamic changes in vegetation indexes during early to mid-growth stages. Remote Sensing. 2019, 11, 387. 13. Liu X, Cao Q, Yuan Z, Liu X, Wang X, Tian Y, Cao W, Zhu Y*. Leaf area index based nitrogen diagnosis in irrigated lowland rice. Journal of Integrative Agriculture. 2018, 17(1): 60345-7. 14. Lv Z, Zhu Y, Liu X, Ye H, Tian Y, Li F. Climate change impacts on regional rice production in China. Climatic Change, 2018, 147: 523–537. 15. Liu X, Ferguson R, Zheng H, Cao Q, Tian Y, Cao W, Zhu Y*. Using an active-optical sensor to develop an optimal NDVI dynamic model for high-yield rice production (Yangtze, China). Sensors. 2017, 17(4): 672. 16. Liu X, Zhang K, Zhang Z, Cao Q, Lv Z, Yuan Z, Tian Y, Cao W, Zhu Y*. Canopy chlorophyll density based index for estimating nitrogen status and predicting grain yield in rice. Frontiers in Plant Science. 2017, 8: 1829. 17. Lv Z, Liu X (Co-first author), Cao W, Zhu Y*. A model-based estimate of regional wheat yield gaps and water use efficiency in main winter wheat production regions of China. Scientific Reports. 2017, 7: 6081. 18. Ata-Ul-Karim S, Liu X, Lu Z, Zheng H, Cao W, Zhu Y*. Estimation of nitrogen fertilizer requirement for rice crop using critical nitrogen dilution curve. Field Crops Research. 2017, 201: 32-40. 19. Zhang Y, Tang L, Liu X, Liu L, Cao W, Zhu Y*. Modeling curve dynamics and spatial geometry characteristics of rice leaves. Journal of Integrative Agriculture. 2017, 16(10): 2177-2190. 20. Zhang Y, Tang L, Liu X, Liu L, Cao W, Zhu Y*. Modeling the leaf angle dynamics in rice plant. Plos One. 2017, 12(2): e0171890. 21. Ata-Ul-Karim S, Zhu Y, Liu X, Cao Q, Tian Y, Cao W*. Comparison of different critical nitrogen dilution curves for nitrogen diagnosis in rice. Scientific Reports. 2017, 7: 42679. 22. He Z, Qiu X, Ata-Ul-Karim, S, Li Y, Liu X, Cao Q, Zhu Y, Cao W, Tang L*. Development of a critical nitrogen dilution curve of double cropping rice in south China. Frontiers in Plant Science. 2017, 8: 638. 23. Lv Z, Liu X (Co-first author), Tang L, Liu L, Cao W, Zhu Y*. Estimation of ecotype-specific cultivar parameters in a wheat phenology model and uncertainty analysis. Agricultural and Forest Meteorology, 2016, 221: 219-229. 24. Yuan Z, Ata-Ul-Karim S, Cao Q, Lu Z, Cao W, Zhu Y, Liu X*. Indicators for diagnosing nitrogen status of rice based on chlorophyll meter readings. Field Crops Research, 2016, 185:12-20. 25. Yuan Z, Cao Q, Zhang K, Ata-Ul-Karim ST, Tian Y, Zhu Y, Cao W, Liu X*. Optimal leaf positions for SPAD meter measurement in rice. Frontiers in Plant Science, 2016, 7:719. 26. Zhao B, Ata-UI-Karim ST, Yao X, Tian Y, Cao W, Zhu Y, Liu X*. A New Curve of Critical Nitrogen Concentration Based on Spike Dry Matter for Winter Wheat in Eastern China. PLoS ONE, 2016, 11(10): e0164545. 27. Ata-Ul-Karim S, Liu X, Lu Z, Yuan Z, Zhu Y, Cao W*. In-season estimation of rice grain yield using critical nitrogen dilution curve. Field Crops Research. 2016, 195:1-8.

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