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Related Experiment Video

Updated: Sep 10, 2025

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

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

Published on: August 8, 2017

Assessing leaf nitrogen concentration in rice using RGB imaging: a comparative study at leaf, canopy, and plot

Haixiao Ge¹, Gaoqiang Lv², Yang Qin¹

¹College of Rural Revitalization, Jiangsu Open University, Nanjing, China.

Frontiers in Plant Science

|August 21, 2025

Summary

RGB imaging offers a cost-effective method for estimating rice leaf nitrogen concentration (LNC) at various scales. Segmentation improves accuracy for canopy and plot-level analysis, supporting precision agriculture and sustainable farming practices.

Keywords:

LNC RGB imaging SMLR plot scale rice

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Last Updated: Sep 10, 2025

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Area of Science:

Agricultural Science
Remote Sensing
Plant Physiology

Background:

Leaf nitrogen concentration (LNC) is vital for crop health and nitrogen management.
Traditional multispectral/hyperspectral methods for LNC estimation are costly and complex.
RGB imaging presents a practical, affordable alternative for LNC assessment.

Purpose of the Study:

To evaluate RGB imaging for estimating rice LNC at leaf, canopy, and plot scales.
To compare the accuracy of RGB imaging with traditional methods.
To assess the impact of vegetation segmentation and spatial resolution on LNC estimation.

Main Methods:

Field experiments were conducted using RGB images at three spatial resolutions.
Vegetation segmentation was performed using green minus red (GMR) band indices and thresholding.
Stepwise multiple linear regression (SMLR) models with 13 color indices were developed.

Main Results:

Leaf-scale models achieved high accuracy (R² = 0.84-0.87).
Canopy-scale models showed improved performance with vegetation segmentation (avg. R² increase of 3%).
Plot-scale models were minimally affected by UAV altitude (100m comparable).

Conclusions:

RGB imaging is a scalable and accurate tool for rice nitrogen monitoring.
Vegetation segmentation is crucial for improving accuracy at larger spatial scales.
Findings support precision nitrogen management in smallholder farming for sustainability.