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Scaling Effects on Chlorophyll Content Estimations with RGB Camera Mounted on a UAV Platform Using Machine-Learning
Yahui Guo1, Guodong Yin1, Hongyong Sun2
1Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, College of Water Sciences, Beijing Normal University, Beijing 100875, China.
Sensors (Basel, Switzerland)
|September 12, 2020
Summary
Estimating maize leaf chlorophyll content using vegetation index from UAV RGB images is crucial for agriculture. Machine learning, particularly random forest, combined with 50m altitude imagery, offers precise chlorophyll estimation.
Area of Science:
- Agricultural remote sensing
- Precision agriculture
- Plant physiology
Background:
- Accurate monitoring of maize leaf chlorophyll is vital for agricultural management.
- Scale effects in vegetation index (VI) calculations impact quantitative remote sensing accuracy.
Purpose of the Study:
- Investigate scale effects on VI-based chlorophyll estimation using UAV RGB imagery.
- Evaluate machine learning methods for grid-based chlorophyll content estimation.
Main Methods:
- Analyzed linear relationships between VI from UAV RGB images and ground-measured chlorophyll (SPAD-502).
- Assessed scale impacts using varying flight altitudes (optimal at 50m).
- Applied backpropagation neural network (BP), support vector machine (SVM), and random forest (RF) for chlorophyll estimation.
Main Results:
- Highest coefficient of determination (R²=0.85) achieved with VI from 50m altitude UAV imagery.
- Random forest (RF) demonstrated superior performance with the lowest RMSE (2.90) and MAE (2.389).
- Machine learning methods showed high precision in chlorophyll estimation.
Conclusions:
- Integrated machine learning (especially RF) with UAV RGB imagery at 50m altitude provides a precise method for maize leaf chlorophyll estimation.
- This approach is suitable for agricultural applications, enhancing crop monitoring and management.
