Germplasm resources have been the important material fundamentals to improve crop breeding. It is a high demand for the accurate and high-throughput acquisition of crop phenotype information to evaluate germplasm resources. The indoor high-throughput germplasm phenotyping platform can be expected to provide precise environmental regulation, and automatic and non-destructive imaging with high efficiency using phenotypic data acquisition, analysis, and management technology. There is also an efficient, integrated, and large-scale solution to the germplasm resource evaluation. A profound impact can be found in crop breeding improvement and high-quality development of the seed industry. According to the structural design and the movement pattern between the sensors, this review aims to evaluate the indoor high-throughput phenotyping platform in terms of four types, including the desktop, conveyor belt, orbital phenotyping platform, and greenhouse plant phenotyping robot. The acquisition of plant phenotype data was only the first step in phenotype research. The screening and identification of germplasm resources were then realized to extract the phenotypic traits from the original image data. The standardized storage of phenotypic data was conducted for the cross-scale and cross-dimensional trait analysis. Simultaneously, a systematic investigation was made on the indoor plant phenotypic data interpretation and management techniques in recent years. The raw image data of plant phenotype and software greatly contributed to the robust, accurate, and automatic phenotype analysis. The commonly-used phenotype raw data analysis mainly included classical statistical analysis, image processing, traditional machine learning, and deep learning. Among them, deep learning presented far-reaching prospects in the resolution and throughput of plant phenotypes, as well as a large number of phenotypic traits. In addition, the phenotypic information also generated large amounts of complex and unstructured data, such as images, point clouds, and spectra. The data often originated in a variety of formats and lengths. An effective solution was to build the database for the unstructured plant phenotype in the standardized and integrated data management. A variety of plant phenotype databases and systems were developed to promote global crop breeding, germplasm resource assessment, and crop yield. Finally, some challenges were summarized for the indoor high-throughput phenotyping platforms and data interpretation. Three perspectives were proposed for the plant phenotype research of germplasm resource evaluation. 1) It was an urgent need to develop the high-precision, diverse, convenient, and low-cost indoor high-throughput germplasm phenotyping platforms. 2) An urgent need for innovation was the fusion analysis to identify, classify, evaluate and predict the plant phenotypic traits. 3) A big data platform was also needed to be constructed to store, process, open, and share the plant phenotypic information. In conclusion, the indoor high-throughput phenotyping platform can greatly contribute to the phenotypic data in genomic data mining, and the evaluation of germplasm. Cross-collaborative research should be implemented in genomics, genetics, and phenomics, in order to promote the phenotyping platform and data analysis.