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Related Experiment Video

Updated: Jul 23, 2025

Microplot Design and Plant and Soil Sample Preparation for 15Nitrogen Analysis

Microplot Design and Plant and Soil Sample Preparation for 15Nitrogen Analysis

Published on: May 10, 2020

Characterization of Rice Yield Based on Biomass and SPAD-Based Leaf Nitrogen for Large Genotype Plots.

Andres F Duque¹, Diego Patino¹, Julian D Colorado^1,2

¹School of Engineering, Pontificia Universidad Javeriana Bogota, Cra. 7 No. 40-62, Bogota 110231, Colombia.

Sensors (Basel, Switzerland)

|July 14, 2023

Summary

Unmanned Aerial Vehicle (UAV) imagery and machine learning accurately estimate rice biomass and nitrogen. This enables precise yield prediction and variety selection for improved crop production.

Keywords:

UAV machine learning multispectral imagery nitrogen and biomass estimation rice yield vegetation indices

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Last Updated: Jul 23, 2025

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Area of Science:

Agricultural Science
Remote Sensing
Data Science

Background:

Unmanned Aerial Vehicle (UAV) imagery offers high-resolution data for vegetation analysis.
Accurate biomass and nitrogen estimation are crucial for optimizing rice yields.
Previous methods lacked the precision for detailed genotypic analysis.

Purpose of the Study:

To assess the efficacy of UAV-derived Vegetation Indices (VIs) for estimating rice biomass and nitrogen content.
To correlate VIs with ground-truth yield data across diverse rice genotypes.
To identify optimal machine learning models for biomass and nitrogen prediction.

Main Methods:

Multispectral UAV imagery was acquired over 59 randomly selected rice plots.
Vegetation Indices (VIs) were calculated and weighted by plant-soil segmentation.
Machine learning models, including Gaussian Process Regression (GPR), were employed for estimation.

Main Results:

The genotype IR 93346 showed the highest yield, with significant biomass gain.
NDVI, SAVI, GNDVI, and ARVI demonstrated strong correlations with biomass, height, and nitrogen.
The GPR model achieved high correlation factors (up to 0.99 for biomass, 1 for nitrogen).

Conclusions:

UAV imagery combined with VIs and machine learning provides a robust method for rice yield characterization.
This approach supports precise phenotyping, yield prediction, and the selection of superior rice genotypes.
The study validates the potential of remote sensing for advancing precision agriculture in rice cultivation.