Comparison of new hyperspectral index and machine learning models for prediction of winter wheat leaf water content
Abstract Background The leaf water content estimation model is established by hyperspectral technology, which is crucial and provides technical reference for precision irrigation. Methods In this study, two consecutive years of field experiments (different irrigation times and seven wheat varieties)...
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doaj-a9e9448811dc4799936358a46fd183982021-04-04T11:11:34ZengBMCPlant Methods1746-48112021-03-0117111410.1186/s13007-021-00737-2Comparison of new hyperspectral index and machine learning models for prediction of winter wheat leaf water contentJuanjuan Zhang0Wen Zhang1Shuping Xiong2Zhaoxiang Song3Wenzhong Tian4Lei Shi5Xinming Ma6Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural UniversityCollaborative Innovation Center of Henan Grain Crops, Henan Agricultural UniversityCollaborative Innovation Center of Henan Grain Crops, Henan Agricultural UniversityAdelphi UniversityLuoyang of Agriculture and ForestryCollaborative Innovation Center of Henan Grain Crops, Henan Agricultural UniversityCollaborative Innovation Center of Henan Grain Crops, Henan Agricultural UniversityAbstract Background The leaf water content estimation model is established by hyperspectral technology, which is crucial and provides technical reference for precision irrigation. Methods In this study, two consecutive years of field experiments (different irrigation times and seven wheat varieties) in 2018–2020 were performed to obtain the canopy spectra reflectance and leaf water content (LWC) data. The characteristic bands related to LWC were extracted from correlation coefficient method (CA) and x-Loading weight method (x-Lw). Five modeling methods, spectral index and four other methods (Partial Least-Squares Regression (PLSR), Random Forest Regression (RFR), Extreme Random Trees (ERT), and K-Nearest Neighbor (KNN)) based characteristic bands, were employed to construct LWC estimation models. Results The results showed that the canopy spectral reflectance increased with the increase of irrigation times, especially in the near-infrared band (750–1350 nm). The prediction accuracy of the newly developed differential spectral index DVI (R1185, R1307) was higher than that of the existing spectral index, with R2 of 0.85 and R2 of 0.78 for the calibration and validation, respectively. Due to a large amount of hyperspectral data, the correlation coefficient method (CA) and x-Loading weight (x-Lw) were used to select the water characteristic bands (100 and 28 characteristic bands, respectively) from the full spectrum. We found that the accuracy of the model based on the characteristic bands was not significantly lower than that of the full spectrum-based models. Among these models, the ERT- x-Lw model performed the best (R2 and RMSE of 0.88 and 1.46; 0.84 and 1.62 for the calibration and validation, respectively). In addition, the accuracy of the LWC estimation model constructed by ERT-x-Lw was higher than that of DVI (R1185, R1307). Conclusion The two models based on ERT-x-Lw and DVI (R1185, R1307) can effectively predict wheat leaf water content. The results provide a technical reference and a basis for crop water monitoring and diagnosis under similar production conditions.https://doi.org/10.1186/s13007-021-00737-2Winter wheatLeaf water contentSpectral indexCharacteristic bandModeling methodInversion model |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Juanjuan Zhang Wen Zhang Shuping Xiong Zhaoxiang Song Wenzhong Tian Lei Shi Xinming Ma |
spellingShingle |
Juanjuan Zhang Wen Zhang Shuping Xiong Zhaoxiang Song Wenzhong Tian Lei Shi Xinming Ma Comparison of new hyperspectral index and machine learning models for prediction of winter wheat leaf water content Plant Methods Winter wheat Leaf water content Spectral index Characteristic band Modeling method Inversion model |
author_facet |
Juanjuan Zhang Wen Zhang Shuping Xiong Zhaoxiang Song Wenzhong Tian Lei Shi Xinming Ma |
author_sort |
Juanjuan Zhang |
title |
Comparison of new hyperspectral index and machine learning models for prediction of winter wheat leaf water content |
title_short |
Comparison of new hyperspectral index and machine learning models for prediction of winter wheat leaf water content |
title_full |
Comparison of new hyperspectral index and machine learning models for prediction of winter wheat leaf water content |
title_fullStr |
Comparison of new hyperspectral index and machine learning models for prediction of winter wheat leaf water content |
title_full_unstemmed |
Comparison of new hyperspectral index and machine learning models for prediction of winter wheat leaf water content |
title_sort |
comparison of new hyperspectral index and machine learning models for prediction of winter wheat leaf water content |
publisher |
BMC |
series |
Plant Methods |
issn |
1746-4811 |
publishDate |
2021-03-01 |
description |
Abstract Background The leaf water content estimation model is established by hyperspectral technology, which is crucial and provides technical reference for precision irrigation. Methods In this study, two consecutive years of field experiments (different irrigation times and seven wheat varieties) in 2018–2020 were performed to obtain the canopy spectra reflectance and leaf water content (LWC) data. The characteristic bands related to LWC were extracted from correlation coefficient method (CA) and x-Loading weight method (x-Lw). Five modeling methods, spectral index and four other methods (Partial Least-Squares Regression (PLSR), Random Forest Regression (RFR), Extreme Random Trees (ERT), and K-Nearest Neighbor (KNN)) based characteristic bands, were employed to construct LWC estimation models. Results The results showed that the canopy spectral reflectance increased with the increase of irrigation times, especially in the near-infrared band (750–1350 nm). The prediction accuracy of the newly developed differential spectral index DVI (R1185, R1307) was higher than that of the existing spectral index, with R2 of 0.85 and R2 of 0.78 for the calibration and validation, respectively. Due to a large amount of hyperspectral data, the correlation coefficient method (CA) and x-Loading weight (x-Lw) were used to select the water characteristic bands (100 and 28 characteristic bands, respectively) from the full spectrum. We found that the accuracy of the model based on the characteristic bands was not significantly lower than that of the full spectrum-based models. Among these models, the ERT- x-Lw model performed the best (R2 and RMSE of 0.88 and 1.46; 0.84 and 1.62 for the calibration and validation, respectively). In addition, the accuracy of the LWC estimation model constructed by ERT-x-Lw was higher than that of DVI (R1185, R1307). Conclusion The two models based on ERT-x-Lw and DVI (R1185, R1307) can effectively predict wheat leaf water content. The results provide a technical reference and a basis for crop water monitoring and diagnosis under similar production conditions. |
topic |
Winter wheat Leaf water content Spectral index Characteristic band Modeling method Inversion model |
url |
https://doi.org/10.1186/s13007-021-00737-2 |
work_keys_str_mv |
AT juanjuanzhang comparisonofnewhyperspectralindexandmachinelearningmodelsforpredictionofwinterwheatleafwatercontent AT wenzhang comparisonofnewhyperspectralindexandmachinelearningmodelsforpredictionofwinterwheatleafwatercontent AT shupingxiong comparisonofnewhyperspectralindexandmachinelearningmodelsforpredictionofwinterwheatleafwatercontent AT zhaoxiangsong comparisonofnewhyperspectralindexandmachinelearningmodelsforpredictionofwinterwheatleafwatercontent AT wenzhongtian comparisonofnewhyperspectralindexandmachinelearningmodelsforpredictionofwinterwheatleafwatercontent AT leishi comparisonofnewhyperspectralindexandmachinelearningmodelsforpredictionofwinterwheatleafwatercontent AT xinmingma comparisonofnewhyperspectralindexandmachinelearningmodelsforpredictionofwinterwheatleafwatercontent |
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