Adaboost-Based Machine Learning Improved the Modeling Robust and Estimation Accuracy of Pear Leaf Nitrogen Concentration by In-Field VIS-NIR Spectroscopy
Different cultivars of pear trees are often planted in one orchard to enhance yield for its gametophytic self-incompatibility. Therefore, an accurate and robust modelling method is needed for the non-destructive determination of leaf nitrogen (N) concentration in pear orchards with mixed cultivars....
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doaj-c18cc2bc5af34793872ddc66a098a4892021-09-26T01:23:58ZengMDPI AGSensors1424-82202021-09-01216260626010.3390/s21186260Adaboost-Based Machine Learning Improved the Modeling Robust and Estimation Accuracy of Pear Leaf Nitrogen Concentration by In-Field VIS-NIR SpectroscopyJie Wang0Wei Xue1Xiaojun Shi2Yangchun Xu3Caixia Dong4College of Resources and Environment, Southwest University, Chongqing 400716, ChinaCollege of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Resources and Environment, Southwest University, Chongqing 400716, ChinaCollege of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, ChinaDifferent cultivars of pear trees are often planted in one orchard to enhance yield for its gametophytic self-incompatibility. Therefore, an accurate and robust modelling method is needed for the non-destructive determination of leaf nitrogen (N) concentration in pear orchards with mixed cultivars. This study proposes a new technique based on in-field visible-near infrared (VIS-NIR) spectroscopy and the Adaboost algorithm initiated with machine learning methods. The performance was evaluated by estimating leaf N concentration for a total of 1285 samples from different cultivars, growth regions, and tree ages and compared with traditional techniques, including vegetation indices, partial least squares regression, singular support vector regression (SVR) and neural networks (NN). The results demonstrated that the leaf reflectance responded to the leaf nitrogen concentration were more sensitive to the types of cultivars than to the different growing regions and tree ages. Moreover, the AdaBoost.RT-BP had the best accuracy in both the training (R<sup>2</sup> = 0.96, root mean relative error (RMSE) = 1.03 g kg<sup>−1</sup>) and the test datasets (R<sup>2</sup> = 0.91, RMSE = 1.29 g kg<sup>−1</sup>), and was the most robust in repeated experiments. This study provides a new insight for monitoring the status of pear trees by the in-field VIS-NIR spectroscopy for better N managements in heterogeneous pear orchards.https://www.mdpi.com/1424-8220/21/18/6260mixed cultivarsVIS-NIR spectroscopyAdaboostsupport vector regressionback-propagation neural networks |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jie Wang Wei Xue Xiaojun Shi Yangchun Xu Caixia Dong |
spellingShingle |
Jie Wang Wei Xue Xiaojun Shi Yangchun Xu Caixia Dong Adaboost-Based Machine Learning Improved the Modeling Robust and Estimation Accuracy of Pear Leaf Nitrogen Concentration by In-Field VIS-NIR Spectroscopy Sensors mixed cultivars VIS-NIR spectroscopy Adaboost support vector regression back-propagation neural networks |
author_facet |
Jie Wang Wei Xue Xiaojun Shi Yangchun Xu Caixia Dong |
author_sort |
Jie Wang |
title |
Adaboost-Based Machine Learning Improved the Modeling Robust and Estimation Accuracy of Pear Leaf Nitrogen Concentration by In-Field VIS-NIR Spectroscopy |
title_short |
Adaboost-Based Machine Learning Improved the Modeling Robust and Estimation Accuracy of Pear Leaf Nitrogen Concentration by In-Field VIS-NIR Spectroscopy |
title_full |
Adaboost-Based Machine Learning Improved the Modeling Robust and Estimation Accuracy of Pear Leaf Nitrogen Concentration by In-Field VIS-NIR Spectroscopy |
title_fullStr |
Adaboost-Based Machine Learning Improved the Modeling Robust and Estimation Accuracy of Pear Leaf Nitrogen Concentration by In-Field VIS-NIR Spectroscopy |
title_full_unstemmed |
Adaboost-Based Machine Learning Improved the Modeling Robust and Estimation Accuracy of Pear Leaf Nitrogen Concentration by In-Field VIS-NIR Spectroscopy |
title_sort |
adaboost-based machine learning improved the modeling robust and estimation accuracy of pear leaf nitrogen concentration by in-field vis-nir spectroscopy |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-09-01 |
description |
Different cultivars of pear trees are often planted in one orchard to enhance yield for its gametophytic self-incompatibility. Therefore, an accurate and robust modelling method is needed for the non-destructive determination of leaf nitrogen (N) concentration in pear orchards with mixed cultivars. This study proposes a new technique based on in-field visible-near infrared (VIS-NIR) spectroscopy and the Adaboost algorithm initiated with machine learning methods. The performance was evaluated by estimating leaf N concentration for a total of 1285 samples from different cultivars, growth regions, and tree ages and compared with traditional techniques, including vegetation indices, partial least squares regression, singular support vector regression (SVR) and neural networks (NN). The results demonstrated that the leaf reflectance responded to the leaf nitrogen concentration were more sensitive to the types of cultivars than to the different growing regions and tree ages. Moreover, the AdaBoost.RT-BP had the best accuracy in both the training (R<sup>2</sup> = 0.96, root mean relative error (RMSE) = 1.03 g kg<sup>−1</sup>) and the test datasets (R<sup>2</sup> = 0.91, RMSE = 1.29 g kg<sup>−1</sup>), and was the most robust in repeated experiments. This study provides a new insight for monitoring the status of pear trees by the in-field VIS-NIR spectroscopy for better N managements in heterogeneous pear orchards. |
topic |
mixed cultivars VIS-NIR spectroscopy Adaboost support vector regression back-propagation neural networks |
url |
https://www.mdpi.com/1424-8220/21/18/6260 |
work_keys_str_mv |
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