数字农科院2.0

Developing Novel Rice Yield Index Using UAV Remote Sensing Imagery Fusion Technology

文献类型： 外文期刊

作者： Zhou, Jun;Lu, Xiangyu;Yang, Rui;Chen, Huizhe;Wang, Yaliang;Zhang, Yuping;Huang, Jing;Liu, Fei

作者机构：

关键词： rice;yield index;UAV;remote sensing;imagery fusion technology

期刊名称： DRONES

ISSN：

年卷期： 2022 年 6.0 卷 6 期

页码：

摘要： Efficient and quick yield prediction is of great significance for ensuring world food security and crop breeding research. The rapid development of unmanned aerial vehicle (UAV) technology makes it more timely and accurate to monitor crops by remote sensing. The objective of this study was to explore the method of developing a novel yield index (YI) with wide adaptability for yield prediction by fusing vegetation indices (VIs), color indices (CIs), and texture indices (TIs) from UAV-based imagery. Six field experiments with 24 varieties of rice and 21 fertilization methods were carried out in three experimental stations in 2019 and 2020. The multispectral and RGB images of the rice canopy collected by the UAV platform were used to rebuild six new VIs and TIs. The performance of VI-based YI (MAPE = 13.98%) developed by quadratic nonlinear regression at the maturity stage was better than other stages, and outperformed that of CI-based (MAPE = 22.21%) and TI-based (MAPE = 18.60%). Then six VIs, six CIs, and six TIs were fused to build YI by multiple linear regression and random forest models. Compared with heading stage (R-2 = 0.78, MAPE = 9.72%) and all stage (R-2 = 0.59, MAPE = 22.21%), the best performance of YI was developed by random forest with fusing VIs + CIs + TIs at maturity stage (R-2 = 0.84, MAPE = 7.86%). Our findings suggest that the novel YI proposed in this study has great potential in crop yield monitoring.

分类号：

  • 相关文献

[1]Entropy Weight Ensemble Framework for Yield Prediction of Winter Wheat Under Different Water Stress Treatments Using Unmanned Aerial Vehicle-Based Multispectral and Thermal Data. Shuaipeng Fei,Muhammad Adeel Hassan,Yuntao Ma,Meiyan Shu,Qian Cheng,Zongpeng Li,Zhen Chen,Yonggui Xiao. 2021

[2]Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review. Zhen Cao,Lammert Kooistra,Wensheng Wang,Leifeng Guo,João Valente. 2023

[3]Integrating UAV-Based Multispectral Data and Transfer Learning for Soil Moisture Prediction in the Black Soil Region of Northeast China. Tong Zhou,Shoutian Ma,Tianyu Liu,Shuihong Yao,Shenglin Li,Yang Gao. 2025

[4]UAV-Based Multimodal Monitoring of Tea Anthracnose with Temporal Standardization. Yu, Qimeng,Zhang, Jingcheng,Yuan, Lin,Li, Xin,Zeng, Fanguo,Xu, Ke,Huang, Wenjiang,Shen, Zhongting. 2025

[5]UAV remote sensing-driven precision variable management in cotton: technological framework, applications, and research outlook. Lechun Zhang,Yingkuan Wang,Xinyu Xue,Wenjiang Huang,Tianye Yang,Hang Zhu,Yubin Lan. 2026

[6]水稻淡褐斑叶突变体lbsll的遗传分析与基因定位. 奉保华,杨杨,施勇烽,林璐,陈洁,黄奇娜,魏彦林,HeiLEUNG,吴建利. 2012

[7]OsBTF3转基因水稻对病原茵侵染的反应和防卫基因的表达分析. 陈华民,吴茂森,何晨阳. 2012

[8]Combining vegetation, color, and texture indices with hyperspectral parameters using machine-learning methods to estimate nitrogen concentration in rice stems and leaves. Dunliang Wang,Rui Li,Tao Liu,Shengping Liu,Chengming Sun,Wenshan Guo. 2023

[9]Application of UAV-Borne Visible-Infared Pushbroom Imaging Hyperspectral for Rice Yield Estimation Using Feature Selection Regression Methods. Shen Y.,Yan Z.,Yang Y.,Tang W.,Sun J.,Zhang Y.. 2024

[10]LKNet: Enhancing rice canopy panicle counting accuracy with an optimized point-based framework. Ziqiu Li,Weiyuan Hong,Xiangqian Feng,Aidong Wang,Hengyu Ma,Jinhua Qin,Qin Yao,Danying Wang,Song Chen. 2025

[11]Indices to screen for grain yield and zinc mass concentrations in aerobic rice at different soil-Zn levels. Jiang, W.,Struik, P. C.,Stomph, T. J.,Jiang, W.,Zhao, M.,Van Keulen, H.,Fan, T. Q.. 2008

[12]Developing a photosynthetic sterility model to estimate CO2 fixation through the crop yield in Asia with the aid of MODIS data. Kaneko, Daijiro,Yeh, P. J. -F.,Kumakura, Toshiro,Yang, Peng. 2010

[13]Carbon partitioning as validation methods for crop yields and CO2 sequestration monitoring in Asia using a photosynthetic-sterility model. Kaneko, Daijiro,Yang, Peng,Kumakura, Toshiro. 2010

[14]Transfer Learning in Junction With a Light Use Efficiency Model for Estimating Grassland Gross Primary Production. Yu, Ruiyang,Yao, Yunjun,Tang, Qingxin,Zhang, Xueyi,Shao, Changliang,Fisher, Joshua B.,Chen, Jiquan,Zhang, Xiaotong,Li, Yufu,Xu, Jia,Liu, Lu,Xie, Zijing,Ning, Jing,Fan, Jiahui,Zhang, Luna. 2025

[15]Numerical Simulation And Experimental Verification O.n Downwash Air Flow O f Six-Rotor Agricultural Unmanned Aerial Vehicle In Hover. Xue Xinyu,Yang Fengbo,Zhang Ling,Sun Zhu. 2017

[16]Development Of A Low-Cost Quadrotor U.av Based On Adrc F or Agricultural Remote Sensing. Xue, Xinyu,Zhang, Songchao,Zhang, Songchao,Sun, Tao,Chen, Chen,Sun, Zhu. 2019

[17]Drift and deposition of ultra-low altitude and low volume application in paddy field. Xue Xinyu,Tu Kang,Xue Xinyu,Qin Weicai,Lan, Yubin,Zhang, Huihui. 2014

[18]Estimating LAI for Cotton Using Multisource UAV Data and a Modified Universal Model. Yan, Puchen,Han, Qisheng,Feng, Yangming,Kang, Shaozhong. 2022

[19]A fast and robust method for plant count in sunflower and maize at different seedling stages using high-resolution UAV RGB imagery. Bai, Yi,Nie, Chenwei,Wang, Hongwu,Cheng, Minghan,Liu, Shuaibing,Yu, Xun,Shao, Mingchao,Wang, Zixu,Wang, Siyu,Tuohuti, Nuremanguli,Shi, Lei,Ming, Bo,Jin, Xiuliang. 2022

[20]Droplet distribution in cotton canopy using single-rotor and four-rotor unmanned aerial vehicles. Meng, Yanhua,Ma, Yan,Wang, Zhiguo,Hu, Hongyan. 2022

作者其他论文 更多>>