Dry Matter Yield and Nitrogen Content Estimation in Grassland Using Hyperspectral Sensor

Hitoshi Nishikawa, Jouke Oenema, Fedde Sijbrandij, Keiji Jindo*, Gert Jan Noij, Frank Hollewand, Bert Meurs, Idse Hoving, Peter van der Vlugt, Max Bouten, Corné Kempenaar

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Estimation of Dry Matter Yield (DMY) and Nitrogen Content (NC) in forage is a big concern for growers. In this study, an estimation model of DMY and NC using Visible and Near Infrared (V-NIR) spectroscopy was developed. An adequate number of grass samples (5078) of perennial ryegrass (Lolium perenne), collected from Dutch grassland in 2019 and 2020 were sensed with a hyperspectral sensor, while grass height was recorded in situ by an ultrasonic sensor mounted on a tractor. The samples were treated with Artificial Intelligence (AI) techniques. PCA based feature selection was applied first, revealing that visible green wavelength (around 500 nm) and red edge wavelength (around 700 nm) were enough to express the overall variability of the dataset. Then, Feature Importance analysis of Random Forest Regressor showed that NIR wavelengths (around 910, 960 and 990nm) were the most sensitive in DMY estimation, while red edge (around 710 nm) and visible orange wavelengths (around 610 nm) were the most related to NC estimation. Finally, SHAP (SHapley Additive exPlanations) analysis was applied to the Random Forest estimation models, resulting in the visualization of wavelength selection, thus assisting in the interpretation of the results and the intermediate processes. Overall, this method can lead to the reduction of the number of wavelengths to be measured in the field and thus, to the possible development of a low cost hyperspectral sensor for the above purposes.

Original languageEnglish
Article number419
Number of pages19
JournalRemote Sensing
Volume15
Issue number2
DOIs
Publication statusPublished - 10 Jan 2023

Keywords

  • dry matter yield
  • feature importance
  • grass height
  • hyperspectral sensor
  • nitrogen content
  • random forest
  • SHAP value

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