Growth parameter acquisition and geometric point cloud completion of lettuce.

Abstract

The plant factory is a form of controlled environment agriculture (CEA) which is offers a promising solution to the problem of food security worldwide. Plant growth parameters need to be acquired for process control and yield estimation in plant factories. In this paper, we propose a fast and non-destructive framework for extracting growth parameters. Firstly, ToF camera (Microsoft Kinect V2) is used to obtain the point cloud from the top view, and then the lettuce point cloud is separated. According to the growth characteristics of lettuce, a geometric method is proposed to complete the incomplete lettuce point cloud. The treated point cloud has a high linear correlation with the actual plant height ( R 2 = 0.961), leaf area ( R 2 = 0.964), and fresh weight ( R 2 = 0.911) with a significant improvement compared to untreated point cloud. The result suggests our proposed point cloud completion method have has the potential to tackle the problem of obtaining the plant growth parameters from a single 3D view with occlusion.

Plant phenotyping relevance

Kinectの3D点群取得と幾何学的点群補完を開発し、レタスの草丈・葉面積・生体重推定を検証しており、植物表現型取得法が中心です。

Code and data availability

The paper's lettuce point cloud data and growth-parameter measurements are not publicly deposited; the data availability statement only promises access from the authors upon request. No public code, models, or dataset URLs are provided.

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