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AgriGAN:通过循环一致的生成对抗网络对农业植物表型进行未配对的图像脱

Jin-Ting Ding¹, Yong-Yu Peng¹, Min Huang¹

¹School of Information and Electrical Engineering, Hangzhou City University, Hangzhou, 310015, Zhejiang, China.

Scientific reports

|July 1, 2024

概括

这项研究介绍了AgriGAN,一种先进的农业图像消毒方法. 通过有效地去除雾,同时保持图像细节和颜色,AgriGAN提高了植物表型的准确性.

科学领域:

农业科学农业科学
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

提取农业表型信息是主观和不准确的.
雾会干扰基于图像的农业数据,降低准确度.
现有的除方法在农业图像中与纹理和颜色细节作斗争.

研究的目的:

为了提高农业形象,为了改善植物表型,除草.
为了解决当前方法在准确性,纹理和颜色保存方面的局限性.
引入AgriGAN,这是一个新的循环一致的生成对抗网络,用于农业形象破坏.

主要方法:

开发了AgriGAN,一种使用循环一致的生成对抗网络的未配对图像脱方法.
将大气散射模型纳入了歧视器.
采用全面细节一致的歧视方法,以提高效率和更快的趋同.

主要成果:

在农业图像中,AgriGAN成功地提高了除性能.
该方法在提取表型信息方面表现出更高的准确性.
保存了详细的纹理信息和颜色表示,减轻了偏差问题.

结论:

在农业形象破坏方面,AgriGAN对现有的方法提供了显著的改进.
提出的方法提高了基于图像的农业植物表型的可靠性.
艾格里甘有效地平衡了除与保护关键图像细节的平衡.

关键词:

农业图像农业图像生成性的对抗性网络.图像消毒图像消毒提取信息提取信息

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