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Smart Agriculture ›› 2020, Vol. 2 ›› Issue (1): 23-42.doi: 10.12133/j.smartag.2020.2.1.202003-SA002

• Topic--Agricultural Remote Sensing and Phenotyping Information Acquisition Analysis • Previous Articles     Next Articles

Indoor phenotyping platforms and associated trait measurement: Progress and prospects

Xu Lingxiang1, Chen Jiawei1, Ding Guohui1, Lu Wei2, Ding Yanfeng1, Zhu Yan3, Zhou Ji1,4()   

  1. 1.Plant Phenomics Research Center/China-UK Plant Phenomics Research Center/Jiangsu Collaborative Innovation Center for Modern Crop Production/Collaborative Innovation Center for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
    2.Electrical Engineering, College of Engineering, Jiangsu Key Laboratory of Modern Facility Agricultural Technology and Equipment Engineering, Nanjing Agricultural University, Nanjing 210095, China
    3.National Engineering and Technology Center for Information Agriculture/Ministry of Agriculture and Rural Affairs (MARA ) Key Laboratory for Crop System Analysis and Decision Making/Engineering Research Center for Smart Agriculture (Ministry of Education)/Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
    4.Data Sciences, National Institute of Agricultural Botany, Cambridge Crop Research, Cambridge CB3 0LE, Cambridge shire, UK
  • Received:2020-03-02 Revised:2020-03-26 Online:2020-03-30
  • corresponding author: Ji Zhou, Email:(中),(英)
  • About author:Xu Lingxiang, Email:midway2005@163.com; Chen Jiawei, Email:chenjiawei@njau.edu.cn
  • Supported by:
    Central University Basic Research Special Fund (JCQY201902); Jiangsu Province Basic Research Program General Project (BK20191311)


Plant phenomics is under rapid development in recent years, a research field that is progressing towards integration, scalability, multi-perspectivity and high-throughput analysis. Through combining remote sensing, Internet of Things (IoT), robotics, computer vision, and artificial intelligence techniques such as machine learning and deep learning, relevant research methodologies, biological applications and theoretical foundation of this research domain have been advancing speedily in recent years. This article first introduces the current trends of plant phenomics and its related progress in China and worldwide. Then, it focuses on discussing the characteristics of indoor phenotyping and phenotypic traits that are suitable for indoor experiments, including yield, quality, and stress related traits such as drought, cold and heat resistance, salt stress, heavy metals, and pests. By connecting key phenotypic traits with important biological questions in yield production, crop quality and Stress-related tolerance, we associated indoor phenotyping hardware with relevant biological applications and their plant model systems, for which a range of indoor phenotyping devices and platforms are listed and categorized according to their throughput, sensor integration, platform size, and applications. Additionally, this article introduces existing data management solutions and analysis software packages that are representative for phenotypic analysis. For example, ISA-Tab and MIAPPE ontology standards for capturing metadata in plant phenotyping experiments, PHIS and CropSight for managing complicated datasets, and Python or MATLAB programming languages for automated image analysis based on libraries such as OpenCV, Scikit-Image, MATLAB Image Processing Toolbox. Finally, due to the importance of extracting meaningful information from big phenotyping datasets, this article pays extra attention to the future development of plant phenomics in China, with suggestions and recommendations for the integration of multi-scale phenotyping data to increase confidence in research outcomes, the cultivation of cross-disciplinary researchers to lead the next-generation plant research, as well as the collaboration between academia and industry to enable world-leading research activities in the near future.

Key words: plant phenomics, indoor phenotyping platform, yield-related traits, quality-related traits, resistance-related phenotypes, phenotyping data management and phenotypic analysis

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