An End-to-End Deep RNN based Network Structure to Precisely Regress the Height of Lettuce by Single Perspective Sparse Point Cloud

Abstract

Focusing on non-destructive and automated acquisition of plant phenotypic parameters,this extended abstract proposed an end-to-end deep RNN based network structure for single perspective sparse raw point cloud regression task called DRN. It has been proven to achieve accuracy improvements in PointNet++ and PonitCNN when it comes to regression of lettuce plant height. We believe DRN structure is suitable for feature extraction from plant point cloud data and regression of spatial distance related plant phenotypes like plant height.

Plant phenotyping relevance

単一視点の疎な点群からレタスの草丈を非破壊・自動推定する深層RNN手法を開発しており、植物表現型の取得・推定が中心である。

Code and data availability

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