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Related Experiment Video

Updated: Aug 8, 2025

Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography

Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography

Published on: October 9, 2018

UAV Multisensory Data Fusion and Multi-Task Deep Learning for High-Throughput Maize Phenotyping.

Canh Nguyen^1,2,3, Vasit Sagan^1,2, Sourav Bhadra^1,2

¹Taylor Geospatial Institute, St. Louis, MO 63108, USA.

Sensors (Basel, Switzerland)

|February 28, 2023

Summary

Unmanned aerial vehicles (UAV) with multisensory data fusion and deep learning enhance high-throughput phenotyping in maize. This technology improves crop productivity and secures food systems by accurately predicting key plant traits.

Keywords:

GeoAI LiDAR UAV data fusion high-throughput phenotyping hyperspectral multi-task deep learning

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High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.

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Published on: June 16, 2018

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Last Updated: Aug 8, 2025

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Published on: October 9, 2018

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Published on: March 28, 2025

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.

Published on: June 16, 2018

Area of Science:

Agricultural Science
Remote Sensing
Artificial Intelligence

Background:

Unmanned aerial vehicles (UAVs), mini-sensors, and GeoAI are advancing agricultural innovations.
Improving crop productivity is crucial for securing global food systems.
High-throughput phenotyping enables rapid assessment of plant traits.

Purpose of the Study:

To investigate the versatility of UAV-borne multisensory data fusion for maize phenotyping.
To apply multi-task deep learning for predicting multiple maize traits simultaneously.
To assess the correlation between spectral, thermal, and LiDAR data with plant phenotypes.

Main Methods:

Collected hyperspectral, thermal, and LiDAR data using UAVs in an experimental corn field.
Measured eight ground-truth phenotypes including biomass, yield, nitrogen content, and grain density.
Employed extended Normalized Difference Spectral Index (NDSI), machine learning (SVM, RF), and multi-task deep convolutional neural networks (CNNs).

Main Results:

Near-infrared (NIR) spectra showed a strong correlation with all eight maize traits.
Machine learning models achieved high prediction accuracy (R² up to 0.85) for nitrogen content and total plant nitrogen content.
Multi-task deep learning models performed comparably or better than single-task models, with data augmentation improving accuracy.

Conclusions:

UAV-borne multisensory data fusion combined with multi-task deep learning is effective for high-throughput maize phenotyping.
This integrated approach overcomes limitations of single data modalities and small sample sizes.
Findings offer practical implications for plant breeders and crop growers to enhance crop management and productivity.