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.

Plant phenotyping relevance

作物RGB画像から植生を分割し、キャノピー形質推定に用いる注釈付きデータセットを作成・ベンチマークする研究であり、フェノタイピング用データ基盤が中心である。

Code and data availability

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