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Related Experiment Video

Updated: Jan 8, 2026

Remote Sensing Evaluation of Two-spotted Spider Mite Damage on Greenhouse Cotton

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Published on: April 28, 2017

Growth analysis of cotton using UAS derived multi temporal canopy features.

Sindhu Palla^1,2, Sayantan Sarkar², Lei Zhao^2,3

¹Texas A&M University - Kingsville, Kingsville, TX, 78363, USA.

Scientific Reports

|December 12, 2025

Summary

This summary is machine-generated.

Unoccupied aerial systems (UAS) provide valuable plant phenotypic data. This study used UAS-derived canopy height to model cotton growth, assess maturity, and estimate yield.

Keywords:

Canopy height High-throughput phenotyping Plant growth analysis UAS

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Area of Science:

Agricultural Science
Remote Sensing
Plant Physiology

Background:

Unoccupied aerial systems (UAS) are increasingly utilized in agriculture for plant phenotyping.
Acquiring high-resolution, multi-temporal data is crucial for understanding crop growth dynamics.

Purpose of the Study:

To develop and validate a method for analyzing UAS-derived canopy height data in cotton.
To correlate cotton growth parameters with yield and maturity using remote sensing data.

Main Methods:

Collected RGB images using UAS throughout the growing season in 2016 and 2021.
Generated digital surface models (DSMs) to extract multi-temporal canopy height (CH) measurements.
Fitted non-linear growth functions, specifically the five-parameter logistic function, to CH data and analyzed its derivatives.

Main Results:

The five-parameter logistic function accurately modeled cotton crop growth (R²=0.98, RMSE=6.41).
Maximum growth rate derived from the model showed strong correlation with cotton yield (R²=0.46 in 2016, R²=0.68 in 2021).
The onset of the steady growth phase accurately predicted cotton genotype maturity with 80% accuracy.

Conclusions:

UAS-based remote sensing provides a robust approach for monitoring cotton growth and development.
Derived growth parameters can be effectively used for yield estimation and assessing genotype maturity.
This methodology supports informed agricultural management decisions, including the application of plant growth regulators.