Optimal plant part segmentation using 3D neural architecture search

Abstract

The automatic, and accurate plant phenotyping plays important role to improve the crop yield through enabling efficient plant analysis and plant breeding studies. The 3d deep learning has allows automatic segmentation of plant parts from point cloud data. However, the network architecture is designed manually and performance is limited to prior experience. The aim of this study is to search for optimal 3d deep networks to perform the plant part segmentation. We perform the 3d neural architecture search by training a super network composed of candidate networks. Using the trained super network, the evolutionary searching is used to search for top performing architecture. The results demonstrate the searched architecture outperforms manually designed architectures by attaining mean IoU and accuracy of more than 90% and 96%, respectively. The searched architecture achieves more than 83% class-wise IoU for all main stem, branches, and boll class. These plant part segmentation method shows promising results and holds potential to be utilized by plant breeders for enhancing the production quality.

Plant phenotyping relevance

植物点群から茎・枝・ボールなどの器官を自動分割する3Dニューラルネットワーク探索手法を開発・評価しており、植物表現型取得が研究の中心です。

Code and data availability

The paper's cotton plant LiDAR point cloud dataset (with plant part annotations) is publicly available via a DOI in the data availability statement. No author analysis code or trained model checkpoints are reported as publicly available.