Department of Mechanical Engineering, South Dakota State University, Brookings, SD, 56007, USA.
Raven Industries, Inc., Sioux Falls, SD, 57104, USA.
Sci Rep. 2023 May 9;13(1):7545. doi: 10.1038/s41598-023-34320-7.
This work focuses on leveraging deep learning for agricultural applications, especially for spray pattern segmentation and spray cone angle estimation. These two characteristics are important to understanding the sprayer system such as nozzles used in agriculture. The core of this work includes three deep-learning convolution-based models. These models are trained and their performances are compared. After the best model is selected based on its performance, it is used for spray region segmentation and spray cone angle estimation. The output from the selected model provides a binary image representing the spray region. This binary image is further processed using image processing to estimate the spray cone angle. The validation process is designed to compare results obtained from this work with manual measurements.
这项工作专注于利用深度学习在农业中的应用，特别是在喷雾模式分割和喷雾锥角估计方面。这两个特性对于了解农业中使用的喷嘴等喷雾器系统很重要。这项工作的核心包括三个基于深度学习卷积的模型。这些模型经过训练并比较其性能。基于性能选择最佳模型后，用于喷雾区域分割和喷雾锥角估计。所选模型的输出提供表示喷雾区域的二进制图像。然后使用图像处理进一步处理此二进制图像以估计喷雾锥角。验证过程旨在将本工作的结果与手动测量进行比较。
