Assessment of Regression Models for Predicting Rice Yield and Protein Content Using Unmanned Aerial Vehicle-Based Multispectral Imagery
Unmanned aerial vehicle-based multispectral imagery including five spectral bands (blue, green, red, red-edge, and near-infrared) for a rice field in the ripening stage was used to develop regression models for predicting the rice yield and protein content and to select the most suitable regression...
Main Authors: | , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-04-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/13/8/1508 |
id |
doaj-14e68f6ebb1a4379b350c7ec4e536dcc |
---|---|
record_format |
Article |
spelling |
doaj-14e68f6ebb1a4379b350c7ec4e536dcc2021-04-14T23:02:46ZengMDPI AGRemote Sensing2072-42922021-04-01131508150810.3390/rs13081508Assessment of Regression Models for Predicting Rice Yield and Protein Content Using Unmanned Aerial Vehicle-Based Multispectral ImageryYeseong Kang0Jinwoo Nam1Younggwang Kim2Seongtae Lee3Deokgyeong Seong4Sihyeong Jang5Chanseok Ryu6Department of Bio-System Engineering, Gyeongsang National University, Jinju-si 52828, KoreaGyeongnam Agricultural Research & Extension Services, Jinju-si 52733, KoreaGyeongnam Agricultural Research & Extension Services, Jinju-si 52733, KoreaGyeongnam Agricultural Research & Extension Services, Jinju-si 52733, KoreaGyeongnam Agricultural Research & Extension Services, Jinju-si 52733, KoreaDepartment of Bio-System Engineering, Gyeongsang National University, Jinju-si 52828, KoreaDepartment of Bio-System Engineering, Gyeongsang National University, Jinju-si 52828, KoreaUnmanned aerial vehicle-based multispectral imagery including five spectral bands (blue, green, red, red-edge, and near-infrared) for a rice field in the ripening stage was used to develop regression models for predicting the rice yield and protein content and to select the most suitable regression analysis method for the year-invariant model: partial least squares regression, ridge regression, and artificial neural network (ANN). The regression models developed with six vegetation indices (green normalization difference vegetation index (GNDVI), normalization difference red-edge index (NDRE), chlorophyll index red edge (CIrededge), difference NIR/Green green difference vegetation index (GDVI), green-red NDVI (GRNDVI), and medium resolution imaging spectrometer terrestrial chlorophyll index (MTCI)), calculated from the spectral bands, were applied to single years (2018, 2019, and 2020) and multiple years (2018 + 2019, 2018 + 2020, 2019 + 2020, and all years). The regression models were cross-validated through mutual prediction against the vegetation indices in nonoverlapping years, and the prediction errors were evaluated via root mean squared error of prediction (RMSEP). The ANN model was reproducible, with low and sustained prediction errors of 24.2 kg/1000 m<sup>2</sup> ≤ RMSEP ≤ 59.1 kg/1000 m<sup>2</sup> in rice yield and 0.14% ≤ RMSEP ≤ 0.28% in rice-protein content in all single-year and multiple-year analyses. When the importance of each vegetation index of the regression models was evaluated, only the ANN model showed the same ranking in the vegetation index of the first (MTCI in both rice yield and protein content) and second importance (CIrededge in rice yield and GRNDVI in rice-protein content). Overall, this means that the ANN model has the highest potential for developing a year-invariant model with stable RMSEP and consistent variable ranking.https://www.mdpi.com/2072-4292/13/8/1508multispectral imagerymutual predictionregression modelrice-protein contentrice yield |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yeseong Kang Jinwoo Nam Younggwang Kim Seongtae Lee Deokgyeong Seong Sihyeong Jang Chanseok Ryu |
spellingShingle |
Yeseong Kang Jinwoo Nam Younggwang Kim Seongtae Lee Deokgyeong Seong Sihyeong Jang Chanseok Ryu Assessment of Regression Models for Predicting Rice Yield and Protein Content Using Unmanned Aerial Vehicle-Based Multispectral Imagery Remote Sensing multispectral imagery mutual prediction regression model rice-protein content rice yield |
author_facet |
Yeseong Kang Jinwoo Nam Younggwang Kim Seongtae Lee Deokgyeong Seong Sihyeong Jang Chanseok Ryu |
author_sort |
Yeseong Kang |
title |
Assessment of Regression Models for Predicting Rice Yield and Protein Content Using Unmanned Aerial Vehicle-Based Multispectral Imagery |
title_short |
Assessment of Regression Models for Predicting Rice Yield and Protein Content Using Unmanned Aerial Vehicle-Based Multispectral Imagery |
title_full |
Assessment of Regression Models for Predicting Rice Yield and Protein Content Using Unmanned Aerial Vehicle-Based Multispectral Imagery |
title_fullStr |
Assessment of Regression Models for Predicting Rice Yield and Protein Content Using Unmanned Aerial Vehicle-Based Multispectral Imagery |
title_full_unstemmed |
Assessment of Regression Models for Predicting Rice Yield and Protein Content Using Unmanned Aerial Vehicle-Based Multispectral Imagery |
title_sort |
assessment of regression models for predicting rice yield and protein content using unmanned aerial vehicle-based multispectral imagery |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2021-04-01 |
description |
Unmanned aerial vehicle-based multispectral imagery including five spectral bands (blue, green, red, red-edge, and near-infrared) for a rice field in the ripening stage was used to develop regression models for predicting the rice yield and protein content and to select the most suitable regression analysis method for the year-invariant model: partial least squares regression, ridge regression, and artificial neural network (ANN). The regression models developed with six vegetation indices (green normalization difference vegetation index (GNDVI), normalization difference red-edge index (NDRE), chlorophyll index red edge (CIrededge), difference NIR/Green green difference vegetation index (GDVI), green-red NDVI (GRNDVI), and medium resolution imaging spectrometer terrestrial chlorophyll index (MTCI)), calculated from the spectral bands, were applied to single years (2018, 2019, and 2020) and multiple years (2018 + 2019, 2018 + 2020, 2019 + 2020, and all years). The regression models were cross-validated through mutual prediction against the vegetation indices in nonoverlapping years, and the prediction errors were evaluated via root mean squared error of prediction (RMSEP). The ANN model was reproducible, with low and sustained prediction errors of 24.2 kg/1000 m<sup>2</sup> ≤ RMSEP ≤ 59.1 kg/1000 m<sup>2</sup> in rice yield and 0.14% ≤ RMSEP ≤ 0.28% in rice-protein content in all single-year and multiple-year analyses. When the importance of each vegetation index of the regression models was evaluated, only the ANN model showed the same ranking in the vegetation index of the first (MTCI in both rice yield and protein content) and second importance (CIrededge in rice yield and GRNDVI in rice-protein content). Overall, this means that the ANN model has the highest potential for developing a year-invariant model with stable RMSEP and consistent variable ranking. |
topic |
multispectral imagery mutual prediction regression model rice-protein content rice yield |
url |
https://www.mdpi.com/2072-4292/13/8/1508 |
work_keys_str_mv |
AT yeseongkang assessmentofregressionmodelsforpredictingriceyieldandproteincontentusingunmannedaerialvehiclebasedmultispectralimagery AT jinwoonam assessmentofregressionmodelsforpredictingriceyieldandproteincontentusingunmannedaerialvehiclebasedmultispectralimagery AT younggwangkim assessmentofregressionmodelsforpredictingriceyieldandproteincontentusingunmannedaerialvehiclebasedmultispectralimagery AT seongtaelee assessmentofregressionmodelsforpredictingriceyieldandproteincontentusingunmannedaerialvehiclebasedmultispectralimagery AT deokgyeongseong assessmentofregressionmodelsforpredictingriceyieldandproteincontentusingunmannedaerialvehiclebasedmultispectralimagery AT sihyeongjang assessmentofregressionmodelsforpredictingriceyieldandproteincontentusingunmannedaerialvehiclebasedmultispectralimagery AT chanseokryu assessmentofregressionmodelsforpredictingriceyieldandproteincontentusingunmannedaerialvehiclebasedmultispectralimagery |
_version_ |
1721526878374723584 |