The future of Internet of Things in agriculture: Plant high-throughput phenotypic platform

With continuous collaborative research in sensor technology, communication technology, plant science, computer science and engineering science, Internet of Things (IoT) in agriculture has made a qualitative leap through environmental sensor networks, non-destructive imaging, spectral analysis, robotics, machine vision and laser radar technology. Physical and chemical analysis can continuously obtain environmental data, experimental metadata (including text, image and spectral, 3D point cloud and real-time growth data) through integrated automation platform equipment and technical means. Based on data on multi-scale, multi-environmental and multi-mode plant traits that constitute big data on plant phenotypes, genotype-phenotype-envirotype relationship in the omics system can be explored deeply. Detailed information on the formation mechanism of specific biological traits can promote the process of functional genomics, plant molecular breeding and efficient cultivation. This study summarises the development background, research process and characteristics of high-throughput plant phenotypes. A systematic review of the research progress of IoT in agriculture and plant high-throughput phenotypes is conducted, including the acquisition and analysis of plant phenotype big data, phenotypic trait prediction and multi-recombination analysis based on plant phenomics. This study proposes key techniques for current plant phenotypes, and looks forward to the research on plant phenotype detection technology in the field environment, fusion and data mining of plant phenotype multivariate data, simultaneous observation of multi-scale phenotype platform and promotion of a comprehensive high-throughput phenotype technology. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

Through continuous collaborative research in sensor technology, communication technology, plant science, computer science and engineering science, Internet of Things in agriculture has achieved a qualitative leap in environmental sensors, imaging, spectral analysis, robotics, and machine vision, providing data support for exploring plant phenotypes, genotype-phenotype-envirotype relationship, and functional genomics.