| 1 | MVS-Pheno:A Portable and Low-Cost Phenotyping Platform for Maize Shoots Using Multiview Stereo 3D Reconstruction显示文摘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(R^(2))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. | Sheng Wu Weiliang Wen Yongjian Wang Jiangchuan Fan Chuanyu Wang Wenbo Gou Xinyu Guo | 2020 | Plant Phenomics2020，2，1： | 11 |
| 2 | Application of Internet of Things to Agriculture—The LQ-FieldPheno Platform:A High-Throughput Platform for Obtaining Crop Phenotypes in Field显示文摘The lack of efficient crop phenotypic measurement methods has become a bottleneck in the field of breeding and precision cultivation.However,high-throughput and accurate phenotypic measurement could accelerate the breeding and improve the existing cultivation management technology.In view of this,this paper introduces a high-throughput crop phenotype measurement platform named the LQ-FieldPheno,which was developed by China National Agricultural Information Engineering Technology Research Centre.The proposed platform represents a mobile phenotypic high-throughput automatic acquisition system based on a field track platform,which introduces the Internet of Things(IoT)into agricultural breeding.The proposed platform uses the crop phenotype multisensor central imaging unit as a core and integrates different types of equipment,including an automatic control system,upward field track,intelligent navigation vehicle,and environmental sensors.Furthermore,it combines an RGB camera,a 6-band multispectral camera,a thermal infrared camera,a 3-dimensional laser radar,and a deep camera.Special software is developed to control motions and sensors and to design run lines.Using wireless sensor networks and mobile communication wireless networks of IoT,the proposed system can obtain phenotypic information about plants in their growth period with a high-throughput,automatic,and high time sequence.Moreover,the LQ-FieldPheno has the characteristics of multiple data acquisition,vital timeliness,remarkable expansibility,high-cost performance,and flexible customization.The LQ-FieldPheno has been operated in the 2020 maize growing season,and the collected point cloud data are used to estimate the maize plant height.Compared with the traditional crop phenotypic measurement technology,the LQ-FieldPheno has the advantage of continuously and synchronously obtaining multisource phenotypic data at different growth stages and extracting different plant parameters.The proposed platform could contribute to the research of crop phenotype,remote sensing,agronomy,and related disciplines. | Jiangchuan Fan Yinglun Li Shuan Yu Wenbo Gou Xinyu Guo Chunjiang Zhao | 2023 | Research2023，，3： | 0 |
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