国家自然科学基金面上项目，32371997，小麦临界氮稀释对水氮互作效应的响应机制与区域尺度模型构建，2024/01-2027/12，50万元，在研，主持；

国家自然科学基金青年科学基金项目，31601222，基于时序植被指数的小麦氮素营养诊断模型研究，2017/01-2019/12，19万元，结题，主持；

国家重点研发计划项目“小麦生产全程无人化作业技术装备创制与应用”，2022YFD2001501，课题1：小麦无人农场信息感知与生产决策，任务“小麦肥药处方智能决策系统”，2022/11-2027/05，150万元，在研，子任务主持；

国家重点研发计划项目“中低产田作物耐瘠抗逆品种精准鉴定与综合利用”，2022YFD1900703，课题3：抗旱节水作物品种精准鉴定与综合利用，任务“基于无人机遥感的小麦抗旱节水表型特征高通量测量技术”，2022/11-2025/12，45万元，在研，子任务主持；

国家重点研发计划“粮食作物生长监测诊断与精确栽培技术”，2016YFD0300608，课题8：作物生长监测诊断与精确栽培技术在长江中下游稻麦主产区的示范应用，2016/01-2021/06，850万元，结题，参加（全面负责）；

国家重点研发计划“地面与航空高工效施药技术及智能化装备”，2016YFD0200702，课题2：研发与完善农业航空植保智能化装备关键部件，子课题8：作物病害光谱监测系统，2016/01-2021/06，100万元，结题，子课题主持；

江苏省农业科技自主创新基金“基于多源信息融合的小麦氮肥智慧管理关键技术研究”，CX（24）3126，2024/07-2026/06，30万元，在研，主持；

江苏省重点研发计划（现代农业）“智慧稻麦无人农场的构建与示范应用”，BE2021308，课题2：稻麦智慧生产管理处方精确设计及工程应用，2021/07-2023/06，50万元，在研，主持；

江苏省2022年稻麦轮作“无人化农场”集成示范项目，NJ2022-56，2022/06-2023/06，20万元，在研，子任务主持；

江苏省基础研究计划（自然科学基金）——青年基金项目，BK20150663，基于无人机遥感的水稻氮素营养无损监测研究，2015/07-2018/06，20万元，结题，主持；

南京农业大学中央高校基本科研业务费专项资金项目（西藏联合项目），KYYZ2022002，基于数据驱动的西藏特色农作物产量监测预测，2022/01-2022/12，5万元，在研，主持；

南京农业大学中央高校基本科研业务费专项资金项目（国青项目配套），KJQN201725，基于时序植被指数的小麦氮素营养诊断模型研究，2017/01-2019/12，10万元，结题，主持；

南京农业大学中央高校基本科研业务费专项资金项目，KYZ201502，基于冠层传感器的水稻中后期氮肥精确推荐方法研究，2015/01-2017/12，20万元，结题，主持。

Zhuang, T., Ata-UI-Karim, S. T., Zhao B., Liu, X., Tian, Y., Zhu, Y., Cao, W., Cao, Q.*, 2024. Investigating the impacts of different degrees of deficit irrigation and nitrogen interactions on assimilate translocation, yield, and resource use efficiencies in winter wheat. Agricultural Water Management., 304, 109089. Doi: 10.1016/j.agwat.2024.109089 (IF₅=6.2, Q1)

Ruan, G., Cammarano, D., Ata-UI-Karim, S. T., Liu, X., Tian, Y., Zhu, Y., Cao, W., Cao, Q.*, 2024. Investigating data-driven approaches to optimize nitrogen recommendations for winter wheat. Computers and Electronics in Agriculture, 220, 108857. Doi: 10.1016/j.compag.2024.108857 (IF₅=8.4, Q1)

Zhuang, T., Zhao B., Ata-UI-Karim, S. T., Lemaire, G., Liu, X., Tian, Y., Zhu, Y., Cao, W., Cao, Q.*, 2024. Can the allometric relationship between saturated water accumulation and dry mass be used to diagnose the water status of winter wheat? Field Crops Research, 315, 109474. Doi: 10.1016/j.fcr.2024.109474 (IF₅=6.1, Q1)

Li, Y., Miao, Y., Ata-UI-Karim, S. T., Liu, X., Tian, Y., Zhu, Y., Cao, W., Cao, Q.*, 2024. Sustainability assessment of nitrogen nutrition index based topdressing nitrogen application. Field Crops Research, 307, 109260. Doi: 10.1016/j.fcr.2024.109260  (IF₅=6.1, Q1)

Li, Y., Cammarano, D., Yuan, F., Khosla, R., Mandal, D., Fan, M., Ata-UI-Karim, S. T., Liu, X., Tian, Y., Zhu, Y., Cao, W., Cao, Q.*, 2024. A novel method for optimizing regional-scale management zones based on a sustainable environmental index. Precision Agriculture, 25, 257-282. Doi: 10.1007/s11119-023-10067-z (IF₅=6.1, Q1)

Chen, P.^#, Li, Y.^#, Liu, X., Tian, Y., Zhu, Y, Cao, W., Cao, Q.*, 2023. Improving yield prediction based on spatio-temporal deep learning approaches for winter wheat: A case study in Jiangsu Province, China. Computers and Electronics in Agriculture, 213, 108201. Doi: 10.1016/j.compag.2023.108201 (IF₅=8.4, Q1)

Liu, Z., Wang, Y., Ata-UI-Karim, S. T., Liu, X., Tian, Y., Zhu, Y., Cao, W., Cao, Q.*, 2023. Developing a nitrogen application estimation model for diverse wheat fields: A user-friendly approach for smallholder nitrogen fertilizer recommendations. European Journal of Agronomy, 151, 126984. Doi: 10.1016/j.eja.2023.126984 (IF₅=5.0, Q1)

Zhuang, T., Zhang Y., Li, D., Schmidhalter, U., Ata-UI-Karim, S. T., Cheng, T., Liu, X., Tian, Y., Zhu, Y., Cao, W., Cao, Q.*, 2023. Coupling continuous wavelet transform with machine learning to improve water status prediction in winter wheat. Precision Agriculture. Doi: 10.1007/s11119-023-10036-6 (IF₅=6.1, Q1)

Yao, B.^#, Ata-UI-Karim, S. T.^#, Li, Y., Ye, T., Zhu, Y., Cao, W., Cao, Q.*, Tang, L.*, 2023. Plant nitrogen status at phenological stages can well estimate wheat yield and its components. Field Crops Research, 297, 108950. Doi: 10.1016/j.fcr.2023.108950 (IF₅=6.1, Q1)

Ruan, G., Schmidhalter, U., Yuan, F., Cammarano, D., Liu, X., Tian, Y., Zhu, Y., Cao, W., Cao, Q.*, 2023. Exploring the transferability of wheat nitrogen status estimation with multisource data and Evolutionary Algorithm-Deep Learning (EA-DL) framework. European Journal of Agronomy, 143, 126727. Doi: 10.1016/j.eja.2022.126727 (IF₅=5.0, Q1)

Wang, Y.^#, Yuan, Y.^#, Yuan, F., Ata-UI-Karim, S. T., Liu, X., Tian, Y., Zhu, Y., Cao, W., Cao, Q.*, 2023. Evaluation of variable application rate of fertilizers based on site-specific management zones for winter wheat in small-scale farming. Agronomy, 13(11), 2812. Doi: 10.3390/agronomy13112812 (IF₅=3.7, Q1)

Song, Y.^#, Zheng, X.^#, Chen, X.^#, Xu, Q., Liu, X., Tian, Y., Zhu, Y., Cao, W., Cao, Q.^*, 2023. Improving the prediction of grain protein content in winter wheat at country level with multisource data: A case study in Jiangsu Province of China. Agronomy, 13(10), 2577. Doi: 10.3390/agronomy13102577 (IF₅=3.7, Q1)

Shi, B., Yuan, Y., Zhuang, T., Xu, X., Schmidhalter, U., Ata-UI-Karim, S. T., Zhao, B., Liu, X., Tian, Y., Zhu, Y., Cao, W., Cao, Q.*, 2022. Improving water status prediction of winter wheat using multi-source data with machine learning. European Journal of Agronomy, 139, 126548. Doi: 10.1016/j.eja.2022.126548 (IF₅=5.0, Q1)

Li, Y., Miao, Y., Zhang, J., Cammarano, D., Li, S., Liu, X., Tian, Y., Zhu, Y., Cao, W.*, Cao, Q.*, 2022. Improving estimation of winter wheat nitrogen status using random forest by integrating multi-source data across different agro-ecological zones. Frontiers in Plant Science, 13, 890892. Doi: 10.3389/fpls.2022.890892 (IF₅=6.8, Q1)

Li, X.^#, Ata-UI-Karim, S. T.^#, Li, Y., Yuan, F., Miao, Y., Yoichiro K., Cheng, T., Tang, L., Tian, X., Liu, X., Tian, Y., Zhu, Y., Cao, W., Cao, Q.*, 2022. Advances in the estimations and applications of critical nitrogen dilution curve and nitrogen nutrition index of major cereal crops. A review. Computers and Electronics in Agriculture, 197, 106998. Doi: 10.1016/j.compag.2022.106998 (IF₅=8.4, Q1)

Ruan, G., Li, X., Yuan, F., Cammarano, D., Ata-UI-Karim, S. T., Liu, X., Tian, Y., Zhu, Y., Cao, W., Cao, Q.*, 2022. Improving wheat yield prediction integrating proximal sensing and weather data with machine learning. Computers and Electronics in Agriculture, 195, 106852. Doi: 10.1016/j.compag.2022.106852 (IF₅=8.4, Q1)

Yuan, Y., Miao, Y., Yuan, F., Ata-UI-Karim, S. T., Liu, X., Tian, Y., Zhu, Y., Cao, W., Cao, Q.*, 2022. Delineating soil nutrient management zone based on optimal sampling interval in medium- and small-scale intensive farming systems. Precision Agriculture, 23: 538-558. Doi: 10.1007/s11119-021-09848-1 (IF₅=6.1, Q1)

Yuan, Y., Shi, B., Yost, R., Liu, X., Tian, Y., Zhu, Y., Cao, W., Cao, Q.*, 2022. Optimization of management zone delineation for precision crop management in an intensive farming system. Plants, 11(19), 2611. Doi: 10.3390/plants11192611 (IF₅=4.4, Q1)

Li, S., Yuan, F., Ata-UI-Karim, S. T., Zheng, H., Cheng, T., Liu, X., Tian, Y., Zhu, Y., Cao, W., Cao, Q.*, 2019. Combining color indices and textures of UAV-based digital imagery for rice LAI estimation. Remote Sensing, 11(15), 1763. Doi: 10.3390/rs11151763 (IF₅=4.9, Q1)

Li, S., Ding, X., Kuang, Q., Ata-UI-Karim, S. T., Cheng, T., Liu., X., Tian, Y., Zhu, Y., Cao, W., Cao, Q.*, 2018. Potential of UAV-based active sensing for monitoring rice leaf nitrogen status. Frontiers in Plant Science, 9: 1834. Doi: 10.3389/fpls.2018.01834 (IF₅=6.8, Q1)

Cao, Q., Miao, Y.*, Shen, J., Yuan, F., Cheng, S., Cui, Z., 2018. Evaluating two Crop Circle active canopy sensors for in-season diagnosis of winter wheat nitrogen status. Agronomy, 8(10), 201. Doi: 10.3390/agronomy 8100201 (IF₅=3.7, Q1)

Cao, Q., Miao, Y.*, Feng, G., Gao, X., Liu, B., Liu, Y., Li, F., Khosla, R., Mulla, D.J., Zhang, F., 2017. Improving nitrogen use efficiency with minimal environmental risks using an active canopy sensor in a wheat-maize cropping system. Field Crops Research, 214: 365-372. Doi: 10.1016/j.fcr.2017.09.033 (IF₅=6.1, Q1)

Cao, Q., Miao, Y.*, Li, F., Lu, D., Gao, X., Liu, B., Chen, X., 2017. Developing a new Crop Circle active canopy sensor - based precision nitrogen management strategy for winter wheat in North China Plain. Precision Agriculture, 18: 2-18. Doi: 10.1007/s11119-016-9456-7 (IF₅=6.1, Q1)

Cao, Q., Miao, Y.*, Shen, J., Yu, W., Yuan, F., Cheng, S., Huang, S., Wang, H., Jiang, R., Yang, W., Li H., 2016. Improving in-season estimation of rice yield potential and responsiveness to topdressing nitrogen application with Crop Circle active crop canopy sensor. Precision Agriculture, 17(2): 136-154. Doi: 10.1007/s11119-015-9412-y (IF₅=6.1, Q1)

Cao, Q., Miao, Y.*, Feng, G., Gao, X., Li, F., Liu B., Yue, S., Cheng, S., Ustin, S., Khosla, R., 2015. Active canopy sensing of winter wheat nitrogen status: an evaluation of two sensor systems. Computers and Electronics in Agriculture, 112: 54-67. Doi: 10.1016/j.compag.2014.08.012 (IF₅=8.4, Q1)

Cao, Q., Miao, Y.*, Wang, H., Huang, S., Cheng, S., Khosla, R., Jiang, R., 2013. Non-destructive estimation of rice plant nitrogen status with Crop Circle multispectral active canopy sensor. Field Crops Research, 154: 133-144. Doi: 10.1016/j.fcr.2013.08.005 (IF₅=6.1, Q1)

Cao, Q., Cui, Z., Chen, X., Khosla, R., Dao, T. H., Miao, Y.*, 2012.Quantifying spatial variability of indigenous nitrogen supply for precision nitrogen management in small scale farming. Precision Agriculture, 13: 45-61. Doi: 10.1007/s11119-011-9244-3 (IF₅=6.1, Q1)

李鑫格, 高杨, 刘小军, 田永超, 朱艳, 曹卫星, 曹强*, 2022. 播期播量及施氮量对冬小麦生长及光谱指标的影响. 作物学报, 48(4), 975-987. Doi: 10.3724/SP.J.1006.2022.11033

李鑫格, 项方林, 吴思雨, 刘小军, 田永超, 朱艳, 曹卫星, 曹强*, 2022. 基于植被指数时序动态的冬小麦氮素营养诊断方法. 麦类作物学报, 42(1), 109-119. Doi: 10.7606/j.issn.1009-1041.2022.01.13

史博, 马祖凯, 刘小军, 田永超, 朱艳, 曹卫星, 曹强*, 2022. 小麦植株水分状况遥感监测研究进展与展望. 麦类作物学报, 42(4), 495-503. Doi: 10.7606/j.issn.1009-1041.2022.04.13

项方林, 李鑫格, 马吉锋, 刘小军, 田永超, 朱艳, 曹卫星, 曹强*, 2020. 基于冠层时序植被指数的冬小麦单产预测. 中国农业科学, 53(18), 3679-3692. Doi: 10.3864/j.issn. 0578-1752.2020.18.005

杜宇笑, 李鑫格, 张羽, 程涛, 刘小军, 田永超, 朱艳, 曹卫星, 曹强*, 2020. 不同产量水平稻茬小麦氮素营养指标特征. 植物营养与肥料学报, 26(8), 1420-1429. Doi: 10.11674/zwyf.19498

杜宇笑, 李鑫格, 王雪, 刘小军, 田永超, 朱艳, 曹卫星, 曹强*, 2020. 不同产量水平稻茬小麦氮素需求特征研究. 作物学报, 46(11), 1780-1789. Doi: 10.3724/SP.J.1006.2020.01027

曹强, 田兴帅, 马吉锋, 姚霞, 刘小军, 田永超, 曹卫星, 朱艳*, 2020. 中国三大粮食作物临界氮浓度稀释曲线研究进展. 南京农业大学学报, 43(3), 392-402. Doi: 10.7685/jnau.201907005

田兴帅, 李松阳, 张羽, 刘小军, 田永超, 朱艳, 曹卫星, 曹强*, 2019. 基于临界氮浓度稀释曲线的小麦氮肥需求量估测研究. 麦类作物学报, 39(9), 1112-1120. Doi: 10.7606/j.issn.1009-1041.2019.09.12