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Plant Density Estimation Using UAV Imagery and Deep Learning

文献类型：期刊论文

作者Peng, Jinbang1,2,3; Rezaei, Ehsan Eyshi4; Zhu, Wanxue5; Wang, Dongliang7; Li, He6; Yang, Bin3,8,9; Sun, Zhigang1,2,3,8
刊名REMOTE SENSING
出版日期2022-12-01
卷号14期号:23页码:20
关键词plant density remote sensing deep learning unmanned aerial vehicle (UAV) wheat tillering
DOI10.3390/rs14235923
通讯作者Sun, Zhigang(sun.zhigang@igsnrr.ac.cn)
英文摘要Plant density is a significant variable in crop growth. Plant density estimation by combining unmanned aerial vehicles (UAVs) and deep learning algorithms is a well-established procedure. However, flight companies for wheat density estimation are typically executed at early development stages. Further exploration is required to estimate the wheat plant density after the tillering stage, which is crucial to the following growth stages. This study proposed a plant density estimation model, DeNet, for highly accurate wheat plant density estimation after tillering. The validation results presented that (1) the DeNet with global-scale attention is superior in plant density estimation, outperforming the typical deep learning models of SegNet and U-Net; (2) the sigma value at 16 is optimal to generate heatmaps for the plant density estimation model; (3) the normalized inverse distance weighted technique is robust to assembling heatmaps. The model test on field-sampled datasets revealed that the model was feasible to estimate the plant density in the field, wherein a higher density level or lower zenith angle would degrade the model performance. This study demonstrates the potential of deep learning algorithms to capture plant density from high-resolution UAV imageries for wheat plants including tillers.
WOS关键词TILLER DEVELOPMENT ; EMERGENCE ; WHEAT ; PERFORMANCE ; YIELD ; CNN
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
出版者MDPI
WOS记录号WOS:000896192200001
源URL[http://ir.igsnrr.ac.cn/handle/311030/187957]  
专题中国科学院地理科学与资源研究所
通讯作者Sun, Zhigang
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Shandong Dongying Inst Geog Sci, Inst Geog Sci & Nat Resources Res, Dongying 257000, Peoples R China
4.Leibniz Ctr Agr Landscape Res ZALF, D-15374 Muncheberg, Germany
5.Univ Gottingen, Dept Crop Sci, Von Siebold Str 8, D-37075 Gottingen, Germany
6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
7.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China
8.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, CAS Engn Lab Yellow River Delta Modern Agr, Beijing 100101, Peoples R China
9.Yusense Informat Technol & Equipment Qingdao Inc, Qingdao 266000, Peoples R China
推荐引用方式
GB/T 7714		 Peng, Jinbang,Rezaei, Ehsan Eyshi,Zhu, Wanxue,et al. Plant Density Estimation Using UAV Imagery and Deep Learning[J]. REMOTE SENSING,2022,14(23):20.
APA Peng, Jinbang.,Rezaei, Ehsan Eyshi.,Zhu, Wanxue.,Wang, Dongliang.,Li, He.,...&Sun, Zhigang.(2022).Plant Density Estimation Using UAV Imagery and Deep Learning.REMOTE SENSING,14(23),20.
MLA Peng, Jinbang,et al."Plant Density Estimation Using UAV Imagery and Deep Learning".REMOTE SENSING 14.23(2022):20.

入库方式： OAI收割

来源：地理科学与资源研究所

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