Application of Vegetative Indices for Leaf Nitrogen Estimation in Sugarcane Using Hyperspectral Data

Spectral indices are important tools for monitoring nitrogen levels in plants. This study assessed the potential application of spectral indices in monitoring the nitrogen nutritional status in sugarcane crops. Seven sugarcane varieties and three production environments were studied, with the SP8132...

Full description

Saved in:
Bibliographic Details
Published in:Sugar tech : an international journal of sugar crops & related industries 2024-02, Vol.26 (1), p.160-170
Main Authors: Martins, Juliano Araújo, Fiorio, Peterson Ricardo, Silva, Carlos Augusto Alves Cardoso, Demattê, José Alexandre Melo, Silva Barros, Pedro Paulo da
Format: Article
Language:English
Subjects:
Agriculture
Ammonium
Ammonium nitrate
Biomedical and Life Sciences
Crops
leaf nitrogen content
Leaves
Life Sciences
model validation
Monitoring
Nitrogen
Nutritional status
prediction
reflectance
regression analysis
Research Article
Root-mean-square errors
Sugarcane
sugars
Vegetation index
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Spectral indices are important tools for monitoring nitrogen levels in plants. This study assessed the potential application of spectral indices in monitoring the nitrogen nutritional status in sugarcane crops. Seven sugarcane varieties and three production environments were studied, with the SP813250 variety cultivated in all three experimental areas. Ammonium nitrate was used as the nitrogen source at doses of 0, 50, 100, and 150 kg ha −1 . Leaf samples for hyperspectral analyses and Leaf Nitrogen Content (LNC) were collected during the maximum vegetative development phase of the crop. Based on reflectance data, 20 spectral indices were calculated and then subjected to simple linear regression (SLR) testing for LNC prediction. In the validation of prediction results, the coefficient of determination ( R 2 ) values, root mean square error (RMSE), and predicted relative error were used as reference. All models were calibrated using the 2012/13 crop data and validated using the 2013/14 crop data. Indices involving the 530–570 nm, 680–750 nm, and 750–1300 nm spectral ranges showed the best performance in model validation. Across all varieties and production environments, the most acceptable indices were: BNi ( R 2  > 0.66, RMSE  0.65, RMSE  0.68, RMSE  0.69, RMSE  0.69, RMSE 
ISSN:0972-1525
0974-0740
0972-1525
DOI:10.1007/s12355-023-01329-1