UAV-based individual Chinese cabbage weight prediction using multi-temporal data
The use of unmanned aerial vehicles (UAVs) has facilitated crop canopy monitoring, enabling yield prediction by integrating regression models. However, the application of UAV-based data to individual-level harvest weight prediction is limited by the effectiveness of obtaining individual features. In...
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Published in | Scientific reports Vol. 13; no. 1; p. 20122 |
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Main Authors | , , , , , , , , , |
Format | Journal Article |
Language | English |
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England
Nature Publishing Group
17.11.2023
Nature Portfolio |
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Abstract | The use of unmanned aerial vehicles (UAVs) has facilitated crop canopy monitoring, enabling yield prediction by integrating regression models. However, the application of UAV-based data to individual-level harvest weight prediction is limited by the effectiveness of obtaining individual features. In this study, we propose a method that automatically detects and extracts multitemporal individual plant features derived from UAV-based data to predict harvest weight. We acquired data from an experimental field sown with 1196 Chinese cabbage plants, using two cameras (RGB and multi-spectral) mounted on UAVs. First, we used three RGB orthomosaic images and an object detection algorithm to detect more than 95% of the individual plants. Next, we used feature selection methods and five different multi-temporal resolutions to predict individual plant weights, achieving a coefficient of determination (R
) of 0.86 and a root mean square error (RMSE) of 436 g/plant. Furthermore, we achieved predictions with an R
greater than 0.72 and an RMSE less than 560 g/plant up to 53 days prior to harvest. These results demonstrate the feasibility of accurately predicting individual Chinese cabbage harvest weight using UAV-based data and the efficacy of utilizing multi-temporal features to predict plant weight more than one month prior to harvest. |
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AbstractList | The use of unmanned aerial vehicles (UAVs) has facilitated crop canopy monitoring, enabling yield prediction by integrating regression models. However, the application of UAV-based data to individual-level harvest weight prediction is limited by the effectiveness of obtaining individual features. In this study, we propose a method that automatically detects and extracts multitemporal individual plant features derived from UAV-based data to predict harvest weight. We acquired data from an experimental field sown with 1196 Chinese cabbage plants, using two cameras (RGB and multi-spectral) mounted on UAVs. First, we used three RGB orthomosaic images and an object detection algorithm to detect more than 95% of the individual plants. Next, we used feature selection methods and five different multi-temporal resolutions to predict individual plant weights, achieving a coefficient of determination (R
) of 0.86 and a root mean square error (RMSE) of 436 g/plant. Furthermore, we achieved predictions with an R
greater than 0.72 and an RMSE less than 560 g/plant up to 53 days prior to harvest. These results demonstrate the feasibility of accurately predicting individual Chinese cabbage harvest weight using UAV-based data and the efficacy of utilizing multi-temporal features to predict plant weight more than one month prior to harvest. Abstract The use of unmanned aerial vehicles (UAVs) has facilitated crop canopy monitoring, enabling yield prediction by integrating regression models. However, the application of UAV-based data to individual-level harvest weight prediction is limited by the effectiveness of obtaining individual features. In this study, we propose a method that automatically detects and extracts multitemporal individual plant features derived from UAV-based data to predict harvest weight. We acquired data from an experimental field sown with 1196 Chinese cabbage plants, using two cameras (RGB and multi-spectral) mounted on UAVs. First, we used three RGB orthomosaic images and an object detection algorithm to detect more than 95% of the individual plants. Next, we used feature selection methods and five different multi-temporal resolutions to predict individual plant weights, achieving a coefficient of determination (R 2 ) of 0.86 and a root mean square error (RMSE) of 436 g/plant. Furthermore, we achieved predictions with an R 2 greater than 0.72 and an RMSE less than 560 g/plant up to 53 days prior to harvest. These results demonstrate the feasibility of accurately predicting individual Chinese cabbage harvest weight using UAV-based data and the efficacy of utilizing multi-temporal features to predict plant weight more than one month prior to harvest. The use of unmanned aerial vehicles (UAVs) has facilitated crop canopy monitoring, enabling yield prediction by integrating regression models. However, the application of UAV-based data to individual-level harvest weight prediction is limited by the effectiveness of obtaining individual features. In this study, we propose a method that automatically detects and extracts multitemporal individual plant features derived from UAV-based data to predict harvest weight. We acquired data from an experimental field sown with 1196 Chinese cabbage plants, using two cameras (RGB and multi-spectral) mounted on UAVs. First, we used three RGB orthomosaic images and an object detection algorithm to detect more than 95% of the individual plants. Next, we used feature selection methods and five different multi-temporal resolutions to predict individual plant weights, achieving a coefficient of determination (R2) of 0.86 and a root mean square error (RMSE) of 436 g/plant. Furthermore, we achieved predictions with an R2 greater than 0.72 and an RMSE less than 560 g/plant up to 53 days prior to harvest. These results demonstrate the feasibility of accurately predicting individual Chinese cabbage harvest weight using UAV-based data and the efficacy of utilizing multi-temporal features to predict plant weight more than one month prior to harvest. Abstract The use of unmanned aerial vehicles (UAVs) has facilitated crop canopy monitoring, enabling yield prediction by integrating regression models. However, the application of UAV-based data to individual-level harvest weight prediction is limited by the effectiveness of obtaining individual features. In this study, we propose a method that automatically detects and extracts multitemporal individual plant features derived from UAV-based data to predict harvest weight. We acquired data from an experimental field sown with 1196 Chinese cabbage plants, using two cameras (RGB and multi-spectral) mounted on UAVs. First, we used three RGB orthomosaic images and an object detection algorithm to detect more than 95% of the individual plants. Next, we used feature selection methods and five different multi-temporal resolutions to predict individual plant weights, achieving a coefficient of determination (R2) of 0.86 and a root mean square error (RMSE) of 436 g/plant. Furthermore, we achieved predictions with an R2 greater than 0.72 and an RMSE less than 560 g/plant up to 53 days prior to harvest. These results demonstrate the feasibility of accurately predicting individual Chinese cabbage harvest weight using UAV-based data and the efficacy of utilizing multi-temporal features to predict plant weight more than one month prior to harvest. |
ArticleNumber | 20122 |
Author | Miyazaki, Toshio Guo, Wei Saito, Aika Khaing, Hlaing Phyoe Aguilar-Ariza, Andrés Kondo, Tomohiro Fujiwara, Toru Kamiya, Takehiro Ishii, Masanori Phoo, Hnin Wint |
Author_xml | – sequence: 1 givenname: Andrés surname: Aguilar-Ariza fullname: Aguilar-Ariza, Andrés organization: Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan – sequence: 2 givenname: Masanori surname: Ishii fullname: Ishii, Masanori organization: Institute for Sustainable Agro-Ecosystem Services, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1, Midoricho, Nishitokyo-shi, Tokyo, 188-0002, Japan – sequence: 3 givenname: Toshio surname: Miyazaki fullname: Miyazaki, Toshio organization: Nippon Norin Seed Co., 6-6-5 Takinogawa, Kita-ku, Tokyo, 114-0023, Japan – sequence: 4 givenname: Aika surname: Saito fullname: Saito, Aika organization: Nippon Norin Seed Co., 6-6-5 Takinogawa, Kita-ku, Tokyo, 114-0023, Japan – sequence: 5 givenname: Hlaing Phyoe surname: Khaing fullname: Khaing, Hlaing Phyoe organization: Nippon Norin Seed Co., 6-6-5 Takinogawa, Kita-ku, Tokyo, 114-0023, Japan – sequence: 6 givenname: Hnin Wint surname: Phoo fullname: Phoo, Hnin Wint organization: Nippon Norin Seed Co., 6-6-5 Takinogawa, Kita-ku, Tokyo, 114-0023, Japan – sequence: 7 givenname: Tomohiro surname: Kondo fullname: Kondo, Tomohiro organization: Nippon Norin Seed Co., 6-6-5 Takinogawa, Kita-ku, Tokyo, 114-0023, Japan – sequence: 8 givenname: Toru surname: Fujiwara fullname: Fujiwara, Toru organization: Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan – sequence: 9 givenname: Wei surname: Guo fullname: Guo, Wei organization: Institute for Sustainable Agro-Ecosystem Services, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1, Midoricho, Nishitokyo-shi, Tokyo, 188-0002, Japan – sequence: 10 givenname: Takehiro surname: Kamiya fullname: Kamiya, Takehiro email: akamiyat@g.ecc.u-tokyo.ac.jp organization: Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan. akamiyat@g.ecc.u-tokyo.ac.jp |
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Cites_doi | 10.1016/1011-1344(93)06963-4 10.34133/2021/9840192 10.1007/978-3-319-24277-4 10.1109/CVPR.2016.91 10.1016/0034-4257(79)90013-0 10.3390/rs12234000 10.1016/j.atech.2021.100010 10.3389/fpls.2018.01455 10.3390/rs13132622 10.3390/rs10040563 10.3390/s21020669 10.1364/OL.33.000156 10.1078/0176-1617-00887 10.1038/S41598-021-82797-X 10.1109/MCSE.2007.55 10.1016/j.jclepro.2017.09.224 10.1016/j.compeleceng.2013.11.024 10.1016/0034-4257(88)90106-X 10.1007/s11119-022-09938-8 10.1016/j.biosystemseng.2020.02.014 10.1038/s41586-020-2649-2 10.1016/j.rse.2019.111599 10.3389/fpls.2017.01111 10.1016/j.cj.2021.03.015 10.1109/TGRS.2004.834800 10.1109/MGRS.2018.2890023 10.1186/s13007-022-00861-7 10.1038/s41438-019-0212-9 10.1038/s41592-019-0686-2 10.1080/02757259509532298 10.1016/j.isprsjprs.2018.09.008 10.1016/j.isprsjprs.2020.09.015 10.14397/jals.2020.54.3.95 10.1016/j.ipm.2009.03.002 10.3390/rs14030731 10.1016/j.isprsjprs.2020.02.013 10.34133/plantphenomics.0007 10.1038/ncomms6989 10.5334/jors.148 10.1002/ece3.6861 10.1002/cplx.21499 10.1016/S1011-1344(01)00145-2 |
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References | XX Sun (47431_CR33) 2019; 6 AR Huete (47431_CR50) 1988; 25 S Hoyer (47431_CR43) 2017; 5 DW Kim (47431_CR21) 2018; 10 M Sokolova (47431_CR40) 2009; 45 DK Ray (47431_CR2) 2015; 6 H Wang (47431_CR34) 2021; 13 47431_CR35 H Wickham (47431_CR28) 2016 47431_CR37 L Deng (47431_CR5) 2018; 146 JD Hunter (47431_CR25) 2007; 9 47431_CR36 47431_CR39 47431_CR38 Z Tang (47431_CR9) 2021; 11 A Ashapure (47431_CR17) 2020; 169 CJ Tucker (47431_CR44) 1979; 8 P Ghamisi (47431_CR11) 2019; 7 MB Bisbis (47431_CR1) 2018; 170 J Bendig (47431_CR45) 2015; 39 F Pedregosa (47431_CR54) 2011; 12 47431_CR24 47431_CR23 M Guizar-Sicairos (47431_CR42) 2008; 33 47431_CR26 G Yang (47431_CR6) 2017; 8 47431_CR29 S Fei (47431_CR15) 2022 J Zhang (47431_CR30) 2022; 2022 B Li (47431_CR13) 2020; 162 P Virtanen (47431_CR53) 2020; 17 E Pantazi (47431_CR31) 2022; 14 W Guo (47431_CR4) 2020; 10 47431_CR22 H Fu (47431_CR32) 2021; 21 CR Harris (47431_CR52) 2020; 585 W Guo (47431_CR7) 2021; 2021 YC Hum (47431_CR51) 2014; 20 Y Ji (47431_CR14) 2022; 18 A Bannari (47431_CR8) 1995; 13 A Feng (47431_CR16) 2020; 193 G Chandrashekar (47431_CR27) 2014; 40 AA Gitelson (47431_CR46) 2003; 160 YS Kang (47431_CR20) 2020; 54 A Gitelson (47431_CR49) 1994; 22 P Song (47431_CR3) 2021; 9 BDS Barbosa (47431_CR12) 2021; 1 XX Sun (47431_CR19) 2018; 9 M Maimaitijiang (47431_CR10) 2020; 237 47431_CR48 P Nevavuori (47431_CR18) 2020; 12 A Maccioni (47431_CR47) 2001; 61 CD Kuglin (47431_CR41) 1975; 6 |
References_xml | – volume: 22 start-page: 247 year: 1994 ident: 47431_CR49 publication-title: J. Photochem. Photobiol. B doi: 10.1016/1011-1344(93)06963-4 contributor: fullname: A Gitelson – ident: 47431_CR23 – volume: 2021 start-page: 9840192 year: 2021 ident: 47431_CR7 publication-title: Plant Phenom. doi: 10.34133/2021/9840192 contributor: fullname: W Guo – volume-title: ggplot2: Elegant Graphics for Data Analysis year: 2016 ident: 47431_CR28 doi: 10.1007/978-3-319-24277-4 contributor: fullname: H Wickham – ident: 47431_CR22 doi: 10.1109/CVPR.2016.91 – volume: 8 start-page: 127 year: 1979 ident: 47431_CR44 publication-title: Remote Sens. Environ. doi: 10.1016/0034-4257(79)90013-0 contributor: fullname: CJ Tucker – volume: 12 start-page: 2825 year: 2011 ident: 47431_CR54 publication-title: J. Mach. Learn. Res. contributor: fullname: F Pedregosa – ident: 47431_CR37 – volume: 12 start-page: 23 year: 2020 ident: 47431_CR18 publication-title: Remote Sens. Basel doi: 10.3390/rs12234000 contributor: fullname: P Nevavuori – volume: 1 start-page: 100010 year: 2021 ident: 47431_CR12 publication-title: Smart Agric. Technol. doi: 10.1016/j.atech.2021.100010 contributor: fullname: BDS Barbosa – volume: 39 start-page: 79 year: 2015 ident: 47431_CR45 publication-title: Int. J. Appl. Earth Observ. Geoinf. contributor: fullname: J Bendig – volume: 9 start-page: 1455 year: 2018 ident: 47431_CR19 publication-title: Front. Plant Sci. doi: 10.3389/fpls.2018.01455 contributor: fullname: XX Sun – ident: 47431_CR24 – volume: 13 start-page: 13 year: 2021 ident: 47431_CR34 publication-title: Remote Sens. Basel doi: 10.3390/rs13132622 contributor: fullname: H Wang – volume: 10 start-page: 4 year: 2018 ident: 47431_CR21 publication-title: Remote Sens. Basel doi: 10.3390/rs10040563 contributor: fullname: DW Kim – volume: 21 start-page: 2 year: 2021 ident: 47431_CR32 publication-title: Sens. Switzerl. doi: 10.3390/s21020669 contributor: fullname: H Fu – volume: 33 start-page: 156 year: 2008 ident: 47431_CR42 publication-title: Opt. Lett. doi: 10.1364/OL.33.000156 contributor: fullname: M Guizar-Sicairos – volume: 160 start-page: 271 year: 2003 ident: 47431_CR46 publication-title: J. Plant Physiol. doi: 10.1078/0176-1617-00887 contributor: fullname: AA Gitelson – volume: 11 start-page: 1 year: 2021 ident: 47431_CR9 publication-title: Sci. Rep. doi: 10.1038/S41598-021-82797-X contributor: fullname: Z Tang – volume: 9 start-page: 3 year: 2007 ident: 47431_CR25 publication-title: Comput Sci Eng doi: 10.1109/MCSE.2007.55 contributor: fullname: JD Hunter – volume: 170 start-page: 1602 year: 2018 ident: 47431_CR1 publication-title: J. Clean. Prod. doi: 10.1016/j.jclepro.2017.09.224 contributor: fullname: MB Bisbis – volume: 40 start-page: 16 year: 2014 ident: 47431_CR27 publication-title: Comput. Electr. Eng. doi: 10.1016/j.compeleceng.2013.11.024 contributor: fullname: G Chandrashekar – volume: 25 start-page: 295 year: 1988 ident: 47431_CR50 publication-title: Remote Sens. Environ. doi: 10.1016/0034-4257(88)90106-X contributor: fullname: AR Huete – ident: 47431_CR38 – year: 2022 ident: 47431_CR15 publication-title: Precis. Agric. doi: 10.1007/s11119-022-09938-8 contributor: fullname: S Fei – volume: 193 start-page: 101 year: 2020 ident: 47431_CR16 publication-title: Biosyst. Eng. doi: 10.1016/j.biosystemseng.2020.02.014 contributor: fullname: A Feng – volume: 585 start-page: 357 year: 2020 ident: 47431_CR52 publication-title: Nature doi: 10.1038/s41586-020-2649-2 contributor: fullname: CR Harris – volume: 237 start-page: 111599 year: 2020 ident: 47431_CR10 publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2019.111599 contributor: fullname: M Maimaitijiang – volume: 8 start-page: 1111 year: 2017 ident: 47431_CR6 publication-title: Front. Plant Sci. doi: 10.3389/fpls.2017.01111 contributor: fullname: G Yang – ident: 47431_CR48 – volume: 9 start-page: 633 year: 2021 ident: 47431_CR3 publication-title: Crop J. doi: 10.1016/j.cj.2021.03.015 contributor: fullname: P Song – ident: 47431_CR26 doi: 10.1109/TGRS.2004.834800 – volume: 7 start-page: 1 year: 2019 ident: 47431_CR11 publication-title: IEEE Geosci. Remote Sens. Mag. doi: 10.1109/MGRS.2018.2890023 contributor: fullname: P Ghamisi – volume: 18 start-page: 26 year: 2022 ident: 47431_CR14 publication-title: Plant Methods doi: 10.1186/s13007-022-00861-7 contributor: fullname: Y Ji – ident: 47431_CR29 – volume: 6 start-page: 130 year: 2019 ident: 47431_CR33 publication-title: Hortic. Res. doi: 10.1038/s41438-019-0212-9 contributor: fullname: XX Sun – volume: 17 start-page: 261 year: 2020 ident: 47431_CR53 publication-title: Nat. Methods doi: 10.1038/s41592-019-0686-2 contributor: fullname: P Virtanen – volume: 13 start-page: 95 year: 1995 ident: 47431_CR8 publication-title: Remote Sens. Rev. doi: 10.1080/02757259509532298 contributor: fullname: A Bannari – volume: 146 start-page: 124 year: 2018 ident: 47431_CR5 publication-title: ISPRS J. Photogram. Remote Sens. doi: 10.1016/j.isprsjprs.2018.09.008 contributor: fullname: L Deng – ident: 47431_CR39 – volume: 169 start-page: 180 year: 2020 ident: 47431_CR17 publication-title: ISPRS J. Photogram. Remote Sens. doi: 10.1016/j.isprsjprs.2020.09.015 contributor: fullname: A Ashapure – volume: 54 start-page: 3 year: 2020 ident: 47431_CR20 publication-title: J. Agric. Life Sci. doi: 10.14397/jals.2020.54.3.95 contributor: fullname: YS Kang – ident: 47431_CR35 – volume: 45 start-page: 427 year: 2009 ident: 47431_CR40 publication-title: Inf. Process Manag. doi: 10.1016/j.ipm.2009.03.002 contributor: fullname: M Sokolova – volume: 14 start-page: 3 year: 2022 ident: 47431_CR31 publication-title: Remote Sens. Basel doi: 10.3390/rs14030731 contributor: fullname: E Pantazi – volume: 162 start-page: 161 year: 2020 ident: 47431_CR13 publication-title: ISPRS J. Photogram. Remote Sens. doi: 10.1016/j.isprsjprs.2020.02.013 contributor: fullname: B Li – volume: 2022 start-page: 896 year: 2022 ident: 47431_CR30 publication-title: Plant Phenom. doi: 10.34133/plantphenomics.0007 contributor: fullname: J Zhang – volume: 6 start-page: 5989 year: 2015 ident: 47431_CR2 publication-title: Nat. Commun. doi: 10.1038/ncomms6989 contributor: fullname: DK Ray – ident: 47431_CR36 – volume: 5 start-page: 1 year: 2017 ident: 47431_CR43 publication-title: J. Open Res. Softw. doi: 10.5334/jors.148 contributor: fullname: S Hoyer – volume: 10 start-page: 12318 year: 2020 ident: 47431_CR4 publication-title: Ecol. Evol. doi: 10.1002/ece3.6861 contributor: fullname: W Guo – volume: 20 start-page: 2 year: 2014 ident: 47431_CR51 publication-title: Complexity doi: 10.1002/cplx.21499 contributor: fullname: YC Hum – volume: 61 start-page: 52 year: 2001 ident: 47431_CR47 publication-title: J. Photochem. Photobiol. B doi: 10.1016/S1011-1344(01)00145-2 contributor: fullname: A Maccioni – volume: 6 start-page: 163 year: 1975 ident: 47431_CR41 publication-title: IEEE Int. Conf. Cybern. Soc. contributor: fullname: CD Kuglin |
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Snippet | The use of unmanned aerial vehicles (UAVs) has facilitated crop canopy monitoring, enabling yield prediction by integrating regression models. However, the... Abstract The use of unmanned aerial vehicles (UAVs) has facilitated crop canopy monitoring, enabling yield prediction by integrating regression models.... Abstract The use of unmanned aerial vehicles (UAVs) has facilitated crop canopy monitoring, enabling yield prediction by integrating regression models.... |
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StartPage | 20122 |
SubjectTerms | Brassica oleracea Harvesting Plant extracts Predictions Regression analysis Temporal variations Unmanned aerial vehicles Weight |
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Title | UAV-based individual Chinese cabbage weight prediction using multi-temporal data |
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