一、个人简介
陈震，男，汉族，副研究员，硕士生导师，所级科研英才，2013至今在中国农业科学院农田灌溉研究所工作，主要从事高效灌溉新技术与精准灌溉方面的研究,自工作以来，主持“十四五”国家重点研发计划课题、国家农业科技重大项目课题等6项国家省部级以上项目课题，获得河南省科技进步二等奖一项；发表论文60余篇，其中第一作者/通讯作者发表SCI论文20篇（JCI一区），ESI 1‰高被引论文1篇，1%高被引论文3篇，获得发明专利近十项（其中第一发明人发明专利6项），获得软件著作权12项。近期主要针对我国大田灌溉时空精准决策难、大型喷灌机多功能变量控制不系统、管控平台不智能等问题，围绕实现大田尺度变量精准灌溉的目标，开展了基于光谱感知的喷灌系统变量灌溉及多目标利用理论与方法研究，构建了作物灌溉信息光谱感知技术及精准灌溉处方图反演模型，研发了多功能电动平移式喷灌机关键装备和精准水肥管理平台，实现了感知-决策-控制为主线的大型喷灌机变量精准灌溉。成果先后被人民网、新华网、CCTV13、科技日报、农民日报等中央媒体报道。
二、研究方向
主要从事智慧灌溉技术与装备研发。
三、承担项目
1.国家重点研发计划“黄淮地区智慧喷灌系统技术集成与示范应用”课题，执行期：2023.12-2027.12，经费765万元；
2.国家农业科技重大专项课题，执行期：2023.10-2026.12，经费1200万元；
3.中国农业科学院重大产出苗头培育项目“天空地农田精准灌溉信息智能获取技术与装备研发”，执行期：2019.01–2021.12，经费160万；
4.河南省重大科技专项“基于多源感知的数字化灌溉处方图生成管理平台研发”，执行期：2022.01 – 2024.12，经费45万元；
5.河南省科技攻关项目“无人机光谱感知喷灌机变量喷洒水氮空间变异性研究”，执行期：2019.01 – 2020.12，经费10万；
6.中央基本科研业务费“光谱反演水氮精准灌溉模型及多组学响应机制”,执行期：202201 – 202312，经费15万。
四、获奖成果
丘陵坡地喷灌系统关键产品创制与示范,河南省科技进步二等奖（第10完成人），2017年.
五、出版著作、获得专利和软件著作权等
1.出版著作
陈震，程千 等 著，无人机光谱感知作物信息及变量灌溉方法研究[M]，郑州：黄河水利出版社，2021年。
陈震，程千，段福义 等 著，无人机光谱信息感知大田作物冠层信息研究[M]，郑州：黄河水利出版社，2023年。
2.授权专利：
[1]陈震,程千,段福义. 一种基于无人机光谱数据的灌溉处方图反演方法[P]. 河南省: CN202110078410.8, 2022-09-02.
[2]陈震,李金山,苏欣,等. 一种茶园、果园智能施肥喷药系统[P]. 河南省: CN201811386323.3, 2021-07-13.
[3]陈震,苏欣,孙浩,等. 一种点控式精准智能灌溉施肥施药系统[P]. 河南省: CN201710642390.6, 2019-11-29.
[4]陈震,范永申,李金山,等. 一种平移式变域喷洒喷灌机[P]. 河南: CN201721462924.9, 2018-05-29.
[5]陈震,黄修桥,段福义,等. 一种点控式精准灌溉施肥施药装置[P]. 河南: CN201720941383.1, 2018-02-09.
[6]陈震,苏欣,黄修桥,等. 一种平移式变域喷洒喷灌机及其使用方法[P]. 河南: CN201711077948.7, 2018-02-02.
[7]陈震,段福义,范永申,等. 一种灌溉施肥加气搅拌装置[P]. 河南: CN201620904172.6, 2017-02-08.
[8]陈震,苏欣,段福义,等. 一种灌溉施肥加气搅拌装置及方法[P]. 河南: CN201610688435.9, 2017-02-01.
六、代表性论文
1.Li, Z., Q. Cheng, L. Chen, W. Zhai, B. Zhang, B. Mao, Y. Li, F. Ding, X. Zhou, and Z. Chen, Novel spectral indices and transfer learning model in estimat moisture status across winter wheat and summer maize. Computers and Electronics in Agriculture[J], 2025. 229.
2.Mao, B., Q. Cheng, L. Chen, F. Duan, X. Sun, Y. Li, Z. Li, W. Zhai, F. Ding, H. Li, and Z. Chen, Multi-random ensemble on Partial Least Squares regression to predict wheat yield and its losses across water and nitrogen stress with hyperspectral remote sensing. Computers and Electronics in Agriculture[J], 2024. 222: p. 109046.
3.Li, Z., Q. Cheng, L. Chen, B. Zhang, S. Guo, X. Zhou, and Z. Chen, Predicting Winter Wheat Yield with Dual-Year Spectral Fusion, Bayesian Wisdom, and Cross-Environmental Validation. Remote Sensing[J], 2024. 16(12).
4.Cheng, Q., F. Ding, H. Xu, S. Guo, Z. Li, and Z. Chen, Quantifying corn LAI using machine learning and UAV multispectral imaging. Precision Agriculture[J], 2024. 25(4): p. 1777-1799.
5.Zhai, W., C. Li, S. Fei, Y. Liu, F. Ding, Q. Cheng, and Z. Chen, CatBoost algorithm for estimating maize above-ground biomass using unmanned aerial vehicle-based multi-source sensor data and SPAD values. Computers and Electronics in Agriculture[J], 2023. 214: p. 108306.
6.Zhai, W., C. Li, Q. Cheng, B. Mao, Z. Li, Y. Li, F. Ding, S. Qin, S. Fei, and Z. Chen, Enhancing Wheat Above-Ground Biomass Estimation Using UAV RGB Images and Machine Learning: Multi-Feature Combinations, Flight Height, and Algorithm Implications. Remote Sensing[J], 2023. 15(14): p. 3653.
7.Zhai, W., C. Li, Q. Cheng, F. Ding, and Z. Chen, Exploring Multisource Feature Fusion and Stacking Ensemble Learning for Accurate Estimation of Maize Chlorophyll Content Using Unmanned Aerial Vehicle Remote Sensing. Remote Sensing[J], 2023. 15(13): p. 3454.
8.Li, Z., X. Zhou, Q. Cheng, W. Zhai, B. Mao, Y. Li, and Z. Chen, An integrated feature selection approach to high water stress yield prediction. Frontiers in Plant Science[J], 2023. 14.
9.Li, Z., X. Zhou, Q. Cheng, S. Fei, and Z. Chen, A Machine-Learning Model Based on the Fusion of Spectral and Textural Features from UAV Multi-Sensors to Analyse the Total Nitrogen Content in Winter Wheat. Remote Sensing[J], 2023. 15(8): p. 2152.
10.Ji, Y., Z. Liu, Y. Cui, R. Liu, Z. Chen, X. Zong, and T. Yang, Faba bean and pea harvest index estimations using aerial-based multimodal data and machine learning algorithms. Plant Physiology[J], 2023.
11.Ji, Y., R. Liu, Y. Xiao, Y. Cui, Z. Chen, X. Zong, and T. Yang, Faba bean above-ground biomass and bean yield estimation based on consumer-grade unmanned aerial vehicle RGB images and ensemble learning. Precision Agriculture[J], 2023.
12.Fei, S., Z. Chen, L. Li, Y. Ma, and Y. Xiao, Bayesian model averaging to improve the yield prediction in wheat breeding trials. Agricultural and Forest Meteorology[J], 2023. 328: p. 109237.
13.Li, Z., Z. Chen, Q. Cheng, F. Duan, R. Sui, X. Huang, and H. Xu, UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat. Agronomy[J], 2022. 12(1): p. 202.
14.Ji, Y., Z. Chen, Q. Cheng, R. Liu, M. Li, X. Yan, G. Li, D. Wang, L. Fu, Y. Ma, X. Jin, X. Zong, and T. Yang, Estimation of plant height and yield based on UAV imagery in faba bean (Vicia faba L.). Plant Methods[J], 2022. 18(1): p. 26.
15.Fei, S., L. Li, Z. Han, Z. Chen, and Y. Xiao, Combining novel feature selection strategy and hyperspectral vegetation indices to predict crop yield. Plant Methods[J], 2022. 18(1): p. 119.
16.Fei, S., M.A. Hassan, Y. Xiao, X. Su, Z. Chen, Q. Cheng, F. Duan, R. Chen, and Y. Ma, UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat. Precis Agric[J], 2022: p. 1-26.
17.Fei, S., M.A. Hassan, Y. Xiao, A. Rasheed, X. Xia, Y. Ma, L. Fu, Z. Chen, and Z. He, Application of multi-layer neural network and hyperspectral reflectance in genome-wide association study for grain yield in bread wheat. Field Crops Research[J], 2022. 289: p. 108730.
18.Ding, F., C. Li, W. Zhai, S. Fei, Q. Cheng, and Z. Chen, Estimation of Nitrogen Content in Winter Wheat Based on Multi-Source Data Fusion and Machine Learning. Agriculture[J], 2022. 12(11): p. 1752.
19.Cheng, Q., H. Xu, S. Fei, Z. Li, and Z. Chen, Estimation of Maize LAI Using Ensemble Learning and UAV Multispectral Imagery under Different Water and Fertilizer Treatments. Agriculture[J], 2022. 12(8): p. 1267.
20.Fei, S., M.A. Hassan, Y. Ma, M. Shu, Q. Cheng, Z. Li, Z. Chen, and Y. Xiao, Entropy Weight Ensemble Framework for Yield Prediction of Winter Wheat Under Different Water Stress Treatments Using Unmanned Aerial Vehicle-Based Multispectral and Thermal Data. Front Plant Sci[J], 2021. 12: p. 730181.
七、联系方式
联系电话（微信同号）：15660136611