3.8 Proceedings Paper

From RGB to NIR: Predicting of near infrared reflectance from visible spectrum aerial images of crops

向作者/读者索取更多资源

Near infrared spectroscopy (NIR) provides valuable information for agricultural operations, with image-to-image translation being investigated as a method to generate an NIR spectral band from RGB images in aerial crop imagery.
Near infrared spectroscopy (NIR) provides rich information in agricultural operations and experiments to determine crop parameters which are not visible to the human eye. Collecting the NIR spectral band requires a multispectral camera which is typically more expensive and has lower resolution than a comparable RGB camera. We investigate image-to-image translation as a means to generate an NIR spectral band from an RGB image alone in aerial crop imagery. Aerial images were captured via a multispectral sensor mounted on an unmanned aerial vehicle (UAV) flown over canola, lentil, dry bean, and wheat breeding trials. A software workflow was created to preprocess raw aerial images creating a dataset suitable for training and evaluating deep learning based band inferencing algorithms. Two different experiments including in-domain and out-of-domain experiments over different crop types in our dataset were conducted to evaluate efficacy in an agricultural context.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

3.8
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据