High-Throughput Legume Seed Phenotyping Using a Handheld 3D Laser Scanner

Xia Huang,Ningning Zhu,Shunyi Zheng

doi:10.3390/rs14020431

Abstract

High-throughput phenotyping involves many samples and diverse trait types. For the goal of automatic measurement and batch data processing, a novel method for high-throughput legume seed phenotyping is proposed. A pipeline of automatic data acquisition and processing, including point cloud acquisition, single-seed extraction, pose normalization, three-dimensional (3D) reconstruction, and trait estimation, is proposed. First, a handheld laser scanner is used to obtain the legume seed point clouds in batches. Second, a combined segmentation method using the RANSAC method, the Euclidean segmentation method, and the dimensionality of the features is proposed to conduct single-seed extraction. Third, a coordinate rotation method based on PCA and the table normal is proposed to conduct pose normalization. Fourth, a fast symmetry-based 3D reconstruction method is built to reconstruct a 3D model of the single seed, and the Poisson surface reconstruction method is used for surface reconstruction. Finally, 34 traits, including 11 morphological traits, 11 scale factors, and 12 shape factors, are automatically calculated. A total of 2500 samples of five kinds of legume seeds are measured. Experimental results show that the average accuracies of scanning and segmentation are 99.52% and 100%, respectively. The overall average reconstruction error is 0.014 mm. The average morphological trait measurement accuracy is submillimeter, and the average relative percentage error is within 3%. The proposed method provides a feasible method of batch data acquisition and processing, which will facilitate the automation in high-throughput legume seed phenotyping.

Highlights

Legumes, such as soybeans, peas, black beans, red beans, and mung beans, have considerable economic importance and value worldwide [1,2]
The scaleshape and shape traits have a smaller deviation compared with the morphological traits
Scale and shape traits have a smaller deviation compared with the morphological traits

Summary

Introduction

Legumes, such as soybeans, peas, black beans, red beans, and mung beans, have considerable economic importance and value worldwide [1,2]. The volume, surface area, length, width, thickness, cross-sectional perimeter and area, scale factor, and shape factor of legume seeds are important in the research for legume seed quality evaluation [3,4], optimization breeding [5], and yield evaluation [6]. High-throughput legume seed phenotyping involves a wide variety of trait types and massive measurement samples, which require automatic and batchbased data acquisition and processing [11]. For this reason, it is necessary to explore a high-throughput phenotyping method to automatically measure legume seeds

Methods

Results

Conclusion