一、个人简介
陈震，男，汉族，副研究员，所级科研英才
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
主持国家重点研发计划“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；
主持国家农业科技重大专项课题，执行期：2023.10-2026.12，经费800万元。
主持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。
主持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。
主持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，
主持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
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。
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.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.
2.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).
3.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.
4.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.
5.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.
6.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.
7.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.
8.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.
9.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.
10.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.
11.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.
12.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.
13.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.
14.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.
15.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.
16.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.
17.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.
18.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.
19.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
邮箱：chenzhen@caas.cn，联系电话：0373-3393248