R. Sahoo, Shalini Gakhar, R. Rejith, R. Ranjan, M. C. Meena, A. Dey, J. Mukherjee, R. Dhakar, Sunny Arya, Anchal Daas, S. Babu, P. K. Upadhyay, Kapila Sekhawat, Sudhirkumar, Mahesh Kumar, V. Chinnusamy, M. Khanna
{"title":"基于无人机的小麦叶片氮素成像光谱预测研究","authors":"R. Sahoo, Shalini Gakhar, R. Rejith, R. Ranjan, M. C. Meena, A. Dey, J. Mukherjee, R. Dhakar, Sunny Arya, Anchal Daas, S. Babu, P. K. Upadhyay, Kapila Sekhawat, Sudhirkumar, Mahesh Kumar, V. Chinnusamy, M. Khanna","doi":"10.14358/pers.22-00089r2","DOIUrl":null,"url":null,"abstract":"Quantitative estimation of crop nitrogen is the key to site-specific management for enhanced nitrogen (N) use efficiency and a sustainable crop production system. As an alternate to the conventional approach through wet chemistry, sensor-based noninvasive, rapid, and near-real-time\n assessment of crop N at the field scale has been the need for precision agriculture. The present study attempts to predict leaf N of wheat crop through spectroscopy using a field portable spectroradiometer (spectral range of 400–2500 nm) on the ground in the crop field and an imaging\n spectrometer (spectral range of 400–1000 nm) from an unmanned aerial vehicle (UAV) with the objectives to evaluate (1) four multivariate spectral models (i.e., artificial neural network, extreme learning machine [ELM], least absolute shrinkage and selection operator, and support vector\n machine regression) and (2) two sets of hyperspectral data collected from two platforms and two different sensors. In the former part of the study, ELM outperforms the other methods with maximum calibration and validation R2 of 0.99 and 0.96, respectively. Furthermore, the image data set acquired\n from UAV gives higher performance compared to field spectral data. Also, significant bands are identified using stepwise multiple linear regression and used for modeling to generate a wheat leaf N map of the experimental field.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Unmanned Aerial Vehicle (UAV)–Based Imaging Spectroscopy for Predicting Wheat Leaf Nitrogen\",\"authors\":\"R. Sahoo, Shalini Gakhar, R. Rejith, R. Ranjan, M. C. Meena, A. Dey, J. Mukherjee, R. Dhakar, Sunny Arya, Anchal Daas, S. Babu, P. K. Upadhyay, Kapila Sekhawat, Sudhirkumar, Mahesh Kumar, V. Chinnusamy, M. Khanna\",\"doi\":\"10.14358/pers.22-00089r2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Quantitative estimation of crop nitrogen is the key to site-specific management for enhanced nitrogen (N) use efficiency and a sustainable crop production system. As an alternate to the conventional approach through wet chemistry, sensor-based noninvasive, rapid, and near-real-time\\n assessment of crop N at the field scale has been the need for precision agriculture. The present study attempts to predict leaf N of wheat crop through spectroscopy using a field portable spectroradiometer (spectral range of 400–2500 nm) on the ground in the crop field and an imaging\\n spectrometer (spectral range of 400–1000 nm) from an unmanned aerial vehicle (UAV) with the objectives to evaluate (1) four multivariate spectral models (i.e., artificial neural network, extreme learning machine [ELM], least absolute shrinkage and selection operator, and support vector\\n machine regression) and (2) two sets of hyperspectral data collected from two platforms and two different sensors. In the former part of the study, ELM outperforms the other methods with maximum calibration and validation R2 of 0.99 and 0.96, respectively. Furthermore, the image data set acquired\\n from UAV gives higher performance compared to field spectral data. Also, significant bands are identified using stepwise multiple linear regression and used for modeling to generate a wheat leaf N map of the experimental field.\",\"PeriodicalId\":211256,\"journal\":{\"name\":\"Photogrammetric Engineering & Remote Sensing\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Photogrammetric Engineering & Remote Sensing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.14358/pers.22-00089r2\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Photogrammetric Engineering & Remote Sensing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14358/pers.22-00089r2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Quantitative estimation of crop nitrogen is the key to site-specific management for enhanced nitrogen (N) use efficiency and a sustainable crop production system. As an alternate to the conventional approach through wet chemistry, sensor-based noninvasive, rapid, and near-real-time
assessment of crop N at the field scale has been the need for precision agriculture. The present study attempts to predict leaf N of wheat crop through spectroscopy using a field portable spectroradiometer (spectral range of 400–2500 nm) on the ground in the crop field and an imaging
spectrometer (spectral range of 400–1000 nm) from an unmanned aerial vehicle (UAV) with the objectives to evaluate (1) four multivariate spectral models (i.e., artificial neural network, extreme learning machine [ELM], least absolute shrinkage and selection operator, and support vector
machine regression) and (2) two sets of hyperspectral data collected from two platforms and two different sensors. In the former part of the study, ELM outperforms the other methods with maximum calibration and validation R2 of 0.99 and 0.96, respectively. Furthermore, the image data set acquired
from UAV gives higher performance compared to field spectral data. Also, significant bands are identified using stepwise multiple linear regression and used for modeling to generate a wheat leaf N map of the experimental field.