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RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

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Precision Soil Moisture Monitoring Through Drone-Based Hyperspectral Imaging and PCA-Driven Machine Learning.

Milad Vahidi¹, Sanaz Shafian¹, William Hunter Frame¹

¹School of Plant and Environmental Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA.

Sensors (Basel, Switzerland)

|February 13, 2025

Summary

Drone-based hyperspectral sensing combined with machine learning accurately estimates soil moisture in cornfields. Non-irrigated crops show higher accuracy due to water stress enhancing spectral signals, particularly at deeper soil layers.

Keywords:

PCA-driven data analysis drone-based imaging monitoring models root zone soil water content

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Area of Science:

Agricultural Science
Remote Sensing
Data Science

Background:

Accurate soil moisture estimation is vital for sustainable agriculture, impacting irrigation, crop yield, and water conservation.
Root zone soil moisture is a critical factor influencing plant health and crop productivity.

Purpose of the Study:

To integrate drone-based hyperspectral sensing with machine learning for soil moisture estimation at 10 cm and 30 cm depths.
To identify significant relationships between root zone water content and canopy reflectance, pinpointing informative wavelengths.
To develop and compare machine learning models for soil moisture estimation in a cornfield.

Main Methods:

Utilized a drone-mounted hyperspectral sensor to collect canopy reflectance data.
Applied Principal Component Analysis (PCA) to identify key variables for soil moisture estimation.
Trained and evaluated Artificial Neural Network (ANN), Random Forest (RF), Support Vector Regression (SVR), and Gradient Boosting (XGBoost) models.

Main Results:

The ANN model demonstrated superior performance compared to RF, SVR, and XGBoost for soil moisture estimation.
Soil moisture estimation accuracy was higher in non-irrigated plots, attributed to increased spectral variability under water stress.
Correlation between canopy spectrum and root zone soil moisture decreased as corn matured and chlorophyll content increased.

Conclusions:

Drone-based hyperspectral sensing and machine learning offer a robust approach for estimating soil moisture at multiple depths.
Plant water stress significantly influences the spectral response, enhancing the accuracy of soil moisture estimation.
Optimal estimation accuracy was observed in non-irrigated plots at a 30 cm depth, particularly during periods of high water stress.