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Estimation of wheat tiller density using remote sensing data and machine learning methods.

Jinkang Hu^1,2, Bing Zhang^1,2, Dailiang Peng^1,3

¹Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China.

Frontiers in Plant Science

|January 9, 2023

Summary

This summary is machine-generated.

Accurately estimate winter wheat tiller density using machine learning and remote sensing. Hyperspectral data with vegetation indices achieved higher accuracy than multispectral data for field management.

Keywords:

UAV hyperspectral gradient boosted regression trees random forest tiller density vegetation index winter wheat

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Area of Science:

Agronomy
Remote Sensing
Machine Learning

Background:

Tiller density is crucial for winter wheat management and yield prediction.
Manual counting is labor-intensive and prone to errors.
Remote sensing offers a potential solution for efficient tiller density assessment.

Purpose of the Study:

To develop and evaluate machine learning models for estimating wheat tiller density.
To compare the effectiveness of hyperspectral and multispectral remote sensing data.
To identify optimal vegetation indices and machine learning algorithms for this task.

Main Methods:

Utilized hyperspectral and multispectral remote sensing data.
Developed machine learning models, including Gradient Boosted Regression Trees (GBRT) and Random Forest (RF).
Analyzed vegetation indices related to vegetation cover and leaf area index.

Main Results:

Vegetation indices linked to vegetation cover and leaf area index were most effective.
Hyperspectral data yielded a lower mean relative error (5.46%) compared to multispectral data (7.71%).
GBRT and RF models showed optimal accuracy depending on sample size.

Conclusions:

Machine learning models effectively estimate wheat tiller density using remote sensing data.
Hyperspectral data offers superior accuracy for tiller density estimation.
These methods are suitable for large-scale tiller density monitoring.