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Strategic Study of CAE >> 2023, Volume 25, Issue 4 doi: 10.15302/J-SSCAE-2023.04.015

Technology and Equipment of Big Data on Crop Phenomics

1. Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China;

2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China;

3. Beijing Key Laboratory of Digital Plant, Beijing 100097, China

Funding project:National Key R&D Program of China (2022YFD2002300); Chinese Academy of Engineering projects “Strategic Research on the Digital Development of Biological Breeding” (2021-JJZD-04), “Strategic Research on Smart Agriculture Development in Anhui Province” (2021-DFZ-17) Received: 2023-04-21 Revised: 2023-06-12 Available online: 2023-07-24

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Abstract

Automatic equipment and information technologies make it possible to acquire multi-scale and multi-source heterogeneous data of crops under different growth conditions, forming big data on crop phenomics. This will greatly promote the research progress of crop functional genomics, digital breeding, and smart cultivation. In this paper, the demand for and industrial development of technology and equipment of big data on crop phenomics are analyzed. Then, the current situation of research and development in this area is summarized from five aspects: data acquisition hardware, data transmission, data analysis, knowledge formation, and applications. The problems and developmental trends of relevant technologies, equipment, and industrial application in China are analyzed from the perspectives of high-throughput acquisition and intelligent analysis of big data on crop phenomics. At last, the following suggestions are proposed: achieving breakthroughs regarding key crop phenotyping sensor technologies from the underlying chip level, forming an autonomous phenotyping extraction technology system on the basis of controllable open source, strengthening the standards system construction for big data on crop phenomics, creating a new model of genotype‒phenotype‒environment big data-driven digital breeding and smart cultivation, and building a talent pool and collaborative network for crop phenomics.

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References

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