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Application of Internet of Things to Agriculture - the LQ-FieldPheno Platform: a High-throughput Platform for Obtaining Crop Phenotypes in Field

文献类型： 外文期刊

作者： Fan, Jiangchuan 1 ; Li, Yinglun 1 ; Yu, Shuan 1 ; Gou, Wenbo 1 ; Guo, Xinyu 1 ; Zhao, Chunjiang 1 ;

作者机构： 1.Natl Engn Res Ctr Informat Technol Agr, Beijing Key Lab Digital Plant, Beijing 100097, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

3.China Natl Engn Res Ctr Informat Technol Agr NERCI, Beijing 100097, Peoples R China

关键词： plant phenotype; high-throughput phenotyping; Internet of Things; maize; point cloud

期刊名称：RESEARCH （ 影响因子：11.0； 五年影响因子：10.2 ）

ISSN： 2096-5168

年卷期： 2023 年 2023 卷

页码：

收录情况： SCI

摘要： 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 multi-sensor 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. Further, it combines an RGB camera, a six-band multispectral camera, a thermal infrared camera, a three-dimensional laser radar and a deep camera. Special software is developed to control motions and sensors and 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 multi-source 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.

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