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Assessment of Soybean Lodging Using UAV Imagery and Machine Learning.

Shagor Sarkar¹, Jing Zhou², Andrew Scaboo¹

¹Division of Plant Science and Technology, University of Missouri, Columbia, MO 65211, USA.

Plants (Basel, Switzerland)

|August 26, 2023

Summary

Unmanned aerial vehicle (UAV) imagery combined with machine learning effectively assesses soybean lodging, a key breeding trait. This automated approach improves accuracy and efficiency over traditional visual evaluations.

Keywords:

crop breeding high-throughput phenotyping image features remote sensing

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Area of Science:

Agricultural Science
Plant Breeding
Remote Sensing

Background:

Plant lodging is a critical soybean phenotype for breeding, traditionally assessed visually with limitations.
Visual assessment is time-consuming and prone to human error, necessitating advanced methods.

Purpose of the Study:

To explore the potential of unmanned aerial vehicle (UAV)-based imagery and machine learning for soybean lodging assessment.
To develop an automated system for classifying soybean lodging severity in breeding programs.

Main Methods:

Collected UAV RGB imagery of 1266 soybean plots at the reproductive stage.
Segmented plots and extracted 12 image features for lodging assessment.
Evaluated four machine learning models (XGBoost, RF, KNN, ANN) with SMOTE-ENN preprocessing for imbalanced data.

Main Results:

The Synthetic Minority Oversampling-Edited Nearest Neighbor (SMOTE-ENN) preprocessing method improved classification accuracy across all models.
The artificial neural network (ANN) classifier achieved 96% overall accuracy when using the SMOTE-ENN processed dataset.
The study demonstrated the effectiveness of UAV imagery and machine learning in differentiating soybean lodging phenotypes.

Conclusions:

UAV-based imagery and machine learning offer a robust and accurate method for soybean lodging assessment.
The developed classification model can be integrated into breeding programs for efficient phenotype evaluation.
SMOTE-ENN is a suitable preprocessing technique for imbalanced datasets in this classification task.