Mask R-CNN-based feature extraction and three-dimensional recognition of rice panicle CT images.

Abstract

The rice panicle seed setting rate is extremely important for calculating rice yield and performing genetic analysis. Unlike machine vision, X-ray computed tomography (CT) imaging is a nondestructive technique that provides direct information on the internal and external structure of rice panicles. However, occlusion and adhesion of panicles and grains in a CT image sequence make these objects difficult to identify, which in turn hinders accurate determination of the seed setting rate of rice panicles. Therefore, this paper proposes a method based on a mask region convolutional neural network (Mask R-CNN) for feature extraction and three-dimensional (3-D) recognition of CT images of rice panicles. X-ray CT feature characterization was combined with the Mask R-CNN algorithm to perform feature extraction and classification of a panicle and grains in each layer of the CT sequence. The Euclidean distance between adjacent layers was minimized to extract the features of a 3-D panicle and grains. The results were used to calculate the rice panicle seed setting rate. The proposed method was experimentally verified using eight sets of different rice panicles. The results showed that the proposed method can efficiently identify and count plump grains and blighted grains to achieve an accuracy above 99% for the seed setting rate.

Plant phenotyping relevance

X線CT画像とMask R-CNNを用いてイネ穂の粒を3次元抽出・認識し、登熟歩合という植物形質を推定する手法を開発・検証しており、フェノタイピング手法が研究の中心である。

Code and data availability

The paper describes a Mask R-CNN pipeline for rice panicle CT images, but no public deposit of the CT dataset, annotations, code, or trained model is provided. The only URL mentioned (cocodataset.org) refers to the generic COCO pre-training dataset, not a paper-specific asset.

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