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$Measuring Spray Droplet Size from Agricultural Nozzles Using Laser Diffraction$

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Published on: September 16, 2016

A deep-learning framework for spray pattern segmentation and estimation in agricultural spraying systems.

Praneel Acharya¹, Travis Burgers², Kim-Doang Nguyen³

¹Department of Mechanical Engineering, South Dakota State University, Brookings, SD, 56007, USA.

Scientific Reports

|May 10, 2023

Summary

Deep learning models accurately segment agricultural spray patterns and estimate cone angles. This technology aids in understanding agricultural sprayer nozzles and optimizing spray applications.

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Area of Science:

Agricultural engineering
Computer vision
Machine learning

Background:

Accurate characterization of agricultural spray patterns and cone angles is crucial for optimizing pesticide and fertilizer application.
Traditional methods for spray analysis can be labor-intensive and lack precision.

Purpose of the Study:

To develop and evaluate deep learning models for automated spray pattern segmentation and spray cone angle estimation in agriculture.
To compare the performance of different convolution-based deep learning architectures for this task.

Main Methods:

Three deep learning convolution-based models were trained and evaluated for spray region segmentation.
The best-performing model was utilized for segmenting spray regions and estimating spray cone angles.
Image processing techniques were applied to the model's output for angle estimation.
Results were validated against manual measurements.

Main Results:

The developed deep learning models demonstrated effectiveness in segmenting agricultural spray regions.
Accurate estimation of spray cone angles was achieved using the selected model and subsequent image processing.
Validation confirmed the reliability of the automated approach compared to manual measurements.

Conclusions:

Deep learning offers a powerful and automated solution for analyzing agricultural spray characteristics.
This approach enhances the understanding of agricultural sprayer nozzle performance.
The findings support the integration of AI in precision agriculture for improved application efficiency and reduced environmental impact.