The discovery of valuable phenotypic traits and determination of their genetic factors are significantly affected by the types and quantities of phenotyping information obtained, as well as information processing and analysis methods. The traditional method of acquisition of filed-based plant phenotyping information relys on manual sampling and measurement by researchers, which is time-consuming, laborious, inefficient and subjective. Therefore, the acquisition and analysis technology of high-throughput phenotypic information of field plants has been researched as a hotspot. There are three types of high-throughput phenotyping platforms, i.e., ground-based platform, air-based platform and space-based platform, which are distinguished by system loading modes. The research of filed-based phenotyping mainly focuses on three fields: platform, sensors and information analysis methods. The latest research results of field crop high-throughput phenotyping information acquisition and analysis technology at home and abroad were described from these three aspects. The application scope and limitations of commonly used sensors in phenotyping information acquisition technology and the advantages and disadvantages of different phenotyping information acquisition platforms were analyzed. The methods of phenotyping information analysis were summarized, and it was proposed that the application of high-throughput phenotyping information acquisition and analysis technology should be based on the specific situations and considered the actual needs and economic rationality of the selection and designing. There were no advantages or disadvantages in the data analysis method, but there were differences on their applicability. The determination of the specific method needed to be determined according to the acquired data type, data magnitude and analysis purpose. The principle was simple, fast and accurate. It was not appropriate to use complex machine learning methods for any information, because advanced algorithms meant higher computing performance requirements, and it was not easy to achieve real-time online detection in the field. In the future, phenotyping research would focus on multi-type data fusion, data standardization management, and multi-disciplinary knowledge integration.