欧亚国际

摘要

将深度学习应用于种植系统的图像，为研究和商业应用提供了新的知识和见解。将在地面采集的RGB图像进行语义分割或逐像素分类，将其划分为植被和背景，是估计几种冠层特征的关键步骤。基于卷积神经网络（CNNs）的现有技术方法是在受控或室内环境下获取的数据集上进行训练的。这些模型无法推广应用到真实世界的图像，因此需要使用新的标记数据集进行微调。这推动了VegAnn-植被注释-数据集的创建，该数据集由3775张多作物RGB图像组成，这些图像是在不同的光照条件下使用不同的系统和平台为不同的物候期采集的。我们预计VegAnn将有助于提高分割算法的性能，促进基准测试，并促进大规模作物植被分割研究。

LITERAL便携式植物表型成像系统

VegAnn, Vegetation Annotation of multi-crop RGB images acquired under diverse conditions for segmentation

Abstract

Applying deep learning to images of cropping systems provides new knowledge and insights in research and commercial applications. Semantic segmentation or pixel-wise classification, of RGB images acquired at the ground level, into vegetation and background is a critical step in the estimation of several canopy traits. Current state of the art methodologies based on convolutional neural networks (CNNs) are trained on datasets acquired under controlled or indoor environments. These models are unable to generalize to real-world images and hence need to be fine-tuned using new labelled datasets. This motivated the creation of the VegAnn - Vegetation Annotation - dataset, a collection of 3775 multi-crop RGB images acquired for different phenological stages using different systems and platforms in diverse illumination conditions. We anticipate that VegAnn will help improving segmentation algorithm performances, facilitate benchmarking and promote large-scale crop vegetation segmentation research. 

LITERAL便携式表型成像系统，鱼眼设备、Phenomobiles表型车

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