MVS-Pheno: A Portable and Low-Cost Phenotyping Platform for Maize Shoots Using Multiview Stereo 3D Reconstruction

Abstract

<jats:p>Plant phenotyping technologies play important roles in plant research and agriculture. Detailed phenotypes of individual plants can guide the optimization of shoot architecture for plant breeding and are useful to analyze the morphological differences in response to environments for crop cultivation. Accordingly, high-throughput phenotyping technologies for individual plants grown in field conditions are urgently needed, and MVS-Pheno, a portable and low-cost phenotyping platform for individual plants, was developed. The platform is composed of four major components: a semiautomatic multiview stereo (MVS) image acquisition device, a data acquisition console, data processing and phenotype extraction software for maize shoots, and a data management system. The platform’s device is detachable and adjustable according to the size of the target shoot. Image sequences for each maize shoot can be captured within 60-120 seconds, yielding 3D point clouds of shoots are reconstructed using MVS-based commercial software, and the phenotypic traits at the organ and individual plant levels are then extracted by the software. The correlation coefficient (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:msup><mml:mrow><mml:mi>R</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math>) between the extracted and manually measured plant height, leaf width, and leaf area values are 0.99, 0.87, and 0.93, respectively. A data management system has also been developed to store and manage the acquired raw data, reconstructed point clouds, agronomic information, and resulting phenotypic traits. The platform offers an optional solution for high-throughput phenotyping of field-grown plants, which is especially useful for large populations or experiments across many different ecological regions.</jats:p>

Label3DMaize: toolkit for 3D point cloud data annotation of maize shoots

“…In our recent works, the MVS-Pheno platform [ 18 ] was used to obtain high-throughput 3D point cloud data of maize shoots at different ecological sites for various genotypes and growth stages. However, the underlying knowledge about genotypes and the differences in cultivation management have not been fully explored, indicating that high-throughput phenotypic acquisition is far from practical application.…”

“…Morphological representative shoots of each cultivar at sixth leaf (V6), ninth leaf (V9), 13th leaf (V13), and blister (R2) stages [ 47 ] were selected and transplanted into pots. Then multi-view images were acquired using the MVS-Pheno platform [ 18 ], after which 3D point clouds of the shoots were reconstructed. For validation, 12 shoot point clouds at 4 growth stages (V3, V6, V9, and V12) were acquired using a 3D scanner (FreeScan X3, Tianyuan Inc., Beijing, China), to test the segmentation performance of a different data source.…”

“…Owing to the low cost of sensors and better quality of reconstructed point clouds, MVS reconstruction has been widely adopted in many applications. Recently, multi-view image acquisition platforms that can realize semi-automatic and high-throughput 3D data acquisition for individual plants have been developed [ 18–21 ] and enable 3D data acquisition for the phenotypic analysis of large-scale breeding materials [ 22 , 23 ]. However, how to efficiently and automatically process the acquired big data of 3D point clouds is a bottleneck in 3D plant phenotyping.…”

Next-Generation Breeding Strategies for Climate-Ready Crops

“…These tools have been employed to estimate crop adaptability in natural conditions and can determine the canopy photosynthesis rate, leaf area index, plant height, performance, biomass, and disease symptoms (Jimenez-Berni et al, 2018 ). Recently, a cost-effective field-based high-throughput system MVS-Pheno is designed to monitor the shoot size of maize and offers exceptional ability to study large plant populations under diverse ecological zones (Wu et al, 2020 ). Similarly, a robotic field-based phenotyping system, which provide side-view stereo imaging, was developed to capture the plant height of sorghum at regular intervals of time (Bao et al, 2019 ).…”

“…However, despite a recent progress in genomic tools, the phenotyping technology did not grow at a competing rate to integrate the phenotypic data with genomics. The failure to capture phenotypic data effectively has a major pitfall, which hampers crop improvement programs (Harfouche et al, 2019 ; Wu et al, 2020 ; Yang et al, 2020 ). Due to the lack of phenomics data, our ability to measure phenotypic traits lags behind the existing capability to draw genomic data.…”