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Tianxiao Zhang¹, Kaidong Li¹, Xiangyu Chen¹

¹Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS, 66045, USA.

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|August 17, 2023

Summary

This summary is machine-generated.

Deep learning models accurately detect aphid clusters in sorghum fields, enabling targeted pesticide application. This approach improves pest management efficiency and reduces environmental impact.

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Area of Science:

Agricultural Science
Computer Science
Machine Learning

Background:

Aphid infestations threaten crop yields and food security.
Current broad-spectrum pesticide application is unsustainable and costly.
Precise aphid localization is needed for targeted pest management.

Purpose of the Study:

To develop and evaluate deep learning models for detecting aphid clusters in sorghum fields.
To create a large-scale, annotated dataset for aphid detection research.
To improve the accuracy and efficiency of insect detection for precision agriculture.

Main Methods:

Collected and annotated a dataset of 5447 sorghum field images with aphid clusters.
Processed images into 151,380 patches for machine learning model training.
Implemented and compared four object detection models: VFNet, GFLV2, PAA, and ATSS.
Developed a post-processing technique to merge clusters and remove artifacts, boosting performance.

Main Results:

All four tested object detection models showed stable and similar performance (average precision and recall).
The proposed post-processing method enhanced detection performance by approximately 17%.
Demonstrated the feasibility of automated insect detection using machine learning.

Conclusions:

Deep learning models are effective for detecting aphid clusters in agricultural settings.
The developed dataset and methods contribute to advancing precision agriculture and sustainable pest management.
Open-sourcing the dataset will benefit future research in automated insect detection.