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Estimation of Off-Target Dicamba Damage on Soybean Using UAV Imagery and Deep Learning.

Fengkai Tian¹, Caio Canella Vieira², Jing Zhou³

¹Department of Biomedical, Biological and Chemical Engineering, University of Missouri, Columbia, MO 65211, USA.

Sensors (Basel, Switzerland)

|March 30, 2023

Summary

This study used drone imagery and deep learning to accurately assess dicamba damage in soybean. This technology can speed up breeding efforts for dicamba-tolerant (DT) soybean varieties.

Keywords:

deep learning dicamba tolerance high-throughput phenotyping soybean

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Area of Science:

Agricultural Science
Plant Breeding
Remote Sensing

Background:

Dicamba herbicide drift causes significant yield losses in non-tolerant crops.
Developing non-genetically engineered dicamba-tolerant (DT) soybeans is crucial for agricultural sustainability.
Efficient phenotyping is essential for accelerating crop breeding programs.

Purpose of the Study:

To evaluate the efficacy of unmanned aerial vehicle (UAV) imagery and deep learning for quantifying off-target dicamba damage in diverse soybean genotypes.
To develop and validate deep learning models for high-throughput phenotyping of dicamba-induced crop damage.

Main Methods:

Collected UAV RGB imagery of 463 soybean genotypes exposed to off-target dicamba over two years.
Segmented soybean plots and applied deep learning models (DenseNet121, ResNet50, VGG16, Xception) to classify damage levels.
Assessed model performance based on classification accuracy and confidence intervals.

Main Results:

The DenseNet121 model achieved the highest classification accuracy of 82% (95% CI: 79-84%) for dicamba damage.
No extreme misclassifications occurred between tolerant and susceptible soybean genotypes.
The developed method demonstrated high potential for identifying soybean genotypes with extreme tolerance phenotypes.

Conclusions:

UAV imagery combined with deep learning offers a powerful tool for high-throughput phenotyping of dicamba damage in soybeans.
This approach can significantly enhance the efficiency of breeding programs selecting for dicamba-tolerant soybean varieties.
The findings support the development of improved soybean cultivars resilient to herbicide drift.