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Related Experiment Video

Updated: Dec 18, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Yield prediction by machine learning from UAS-based mulit-sensor data fusion in soybean.

Monica Herrero-Huerta¹, Pablo Rodriguez-Gonzalvez², Katy M Rainey¹

¹Department of Agronomy, Purdue University, West Lafayette, IN 47906 USA.

|June 10, 2020

Summary

Machine learning accurately predicts soybean grain yield using Unmanned Aircraft System (UAS) data. Random Forest and XGBoost models achieved over 90% accuracy, aiding crop genetics and breeding decisions.

Keywords:

High throughput phenotyping Machine learning (ML)Point clouds Soybean Structure from Motion (SfM)Unmanned aircraft system (UAS)Yield

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Published on: June 28, 2016

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Last Updated: Dec 18, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

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Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

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Published on: June 28, 2016

Area of Science:

Agricultural Science
Plant Genetics
Remote Sensing

Background:

Automated plant phenotyping is crucial for efficient crop genetics.
Machine learning (ML) techniques show promise in crop parameter modeling.
This study explores ML for soybean grain yield prediction using optical sensor data.

Purpose of the Study:

To evaluate the capability of ML algorithms (Random Forest and XGBoost) for predicting soybean grain yield.
To integrate data from multiple optical sensors for enhanced prediction accuracy.
To leverage Unmanned Aircraft System (UAS) data for crop phenotyping.

Main Methods:

Acquired multispectral and RGB imagery using UAS-mounted sensors at R4 and R5 growth stages.
Processed imagery using Structure from Motion (SfM) for 2D spectral analysis and 3D reconstruction.
Developed regression models combining sensor data and feature extraction for yield prediction.

Main Results:

Random Forest (RF) model achieved 90.72% accuracy in predicting grain yield.
eXtreme Gradient Boosting (XGBoost) model achieved 91.36% accuracy.
Both ML models demonstrated high efficacy in yield prediction from UAS imagery.

Conclusions:

UAS-derived data can serve as a valuable decision support tool for selecting phenotypes in plant breeding.
The study provides practical insights for improving operational efficiency in crop genetics.
High spatial precision in phenotyping enables proactive management and breeding advancements.