Related Experiment Video
Updated: Jan 7, 2026
11:49
Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
Published on: February 2, 2019
9.8K
Precise Image Color Correction Based on Dual Unmanned Aerial Vehicle Cooperative Flight
Xuqi Lu1,2, Jiayang Xie1,2, Jiayou Yan3
1State Key Laboratory for Vegetation Structure, Function and Construction (VegLab), College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, PR China.
Plant Phenomics (Washington, D.C.)
|December 19, 2025
Summary
This study introduces a cooperative dual unmanned aerial vehicle (UAV) flight method with a color chart for in-flight RGB image correction. The novel approach significantly improves color accuracy and consistency in remote sensing for plant health monitoring.
Area of Science:
- Remote Sensing and Geospatial Technology
- Agricultural Science and Crop Monitoring
- Image Processing and Computer Vision
Background:
- Accurate color in remote sensing is vital for plant health monitoring, growth stage identification, and stress detection.
- Uncorrected color distortions from lighting and sensor variations compromise image data quality and analysis reliability.
- Existing color correction methods often require post-processing, limiting real-time applications.
Purpose of the Study:
- To introduce and validate a novel in-flight color correction approach for RGB imagery using cooperative dual unmanned aerial vehicle (UAV) flights integrated with a color chart (CoF-CC).
- To enhance color accuracy and consistency in remote sensing imagery under diverse field conditions.
- To demonstrate the practical utility of the CoF-CC method in improving crop monitoring applications, such as rice maturity estimation.
Main Methods:
- Developed a cooperative dual UAV system where one UAV (master) acquires RGB imagery while a second UAV carries a ColorChecker chart.
- Ensured persistent visibility of the ColorChecker chart within the master UAV's field of view for real-time calculation of a color correction matrix (CCM).
- Applied the CCM for in-flight correction of RGB images, followed by field experiments to assess cross-sensor consistency and measurement accuracy on rice leaves.
Main Results:
- The CCM significantly reduced average CIE 2000 color difference (ΔE) by 66.1%, bringing it down from 18.2 to 5.0 for rice leaf color.
- Color consistency among six RGB sensors improved by 70.2% with a notable increase in intracluster distance.
- Corrected imagery substantially enhanced rice maturity prediction accuracy, increasing R² from 0.28 to 0.67.
Conclusions:
- The cooperative dual UAV flight with a color chart (CoF-CC) method effectively standardizes RGB images across varying lighting and sensor conditions.
- This approach demonstrates robust performance for accurate color analysis and interpretation in open-field remote sensing applications.
- CoF-CC offers a practical solution for improving the reliability and accuracy of UAV-based crop monitoring and analysis.
More Related Videos
Related Concept Videos
02:16Light Acquisition
9.3K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
9.3K
01:24Color Vision
1.3K
Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
1.3K
01:15Distance Corrections
247
To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
247
01:12Differential Leveling
627
Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...
627
01:20Deconvolution
519
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
519