Non-destructive method of biomass and nitrogen (N) level estimation in Stevia rebaudiana using various multispectral indices

Abstract

Unmanned Aerial Vehicle (UAV) based remote sensing is one of the modern techniques for crop management, which has been used in this study for biomass and Nitrogen (N) level estimations for Stevia rebaudiana, a medicinal crop used as an alternative to sugar as a natural sweetener. Different levels of nitrogen treatments were given to S. rebaudiana and the crops were harvested for biomass estimation. Mica sense Altum multispectral sensor on board was used for acquiring the image data of the crop. The linear regression model was used to examine the best vegetation index using K-fold cross validation approach. Excess Green Index (ExG) was identified as best vegetation index for biomass estimation (R² = 0.7; RMSE = 23.77 g/m²; nRMSE = 29.14%), whereas Enhanced Normalized Difference Vegetation Index (ENDVI) was found as best predictor for Nitrogen (N) level estimation (R² = 0.9; RMSE = 1.75 g/m²; nRMSE = 14.59%).

Keywords:

• Aerial image
• biomass
• K-fold cross validation
• nitrogen
• Stevia rebaudiana
• Unmanned Aerial Vehicle (UAV)
• vegetation indices

Acknowledgement

The authors would like to thank Dr Sanjay Kumar, Director, CSIR-IHBT, Palampur, India, for his guidance and support in carrying out this research. Staff members of the Environmental Technology and Agrotechnology divisions of CSIR-IHBT are acknowledged for their help in fieldwork and laboratory. The CSIR-IHBT communication number is 4631.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This project is funded by the Council of Scientific & Industrial Research (CSIR) under Agri-Nutri-Biotech mission project entitled UAV based high-resolution remote sensing for modernized and efficient cultivation practices of commercially important medicinal and aromatic crops (MLP-0139) project carried out in collaboration with CSIR-IHBT and CSIR-NAL.