Abstract: An intelligent monitoring system was developed to continuously, rapidly and accurately identify the grassland vegetation cover and the key phenological periods. The fixed monitoring, mobile monitoring and cloud platforms were also combined to provide a solution. The innovative system consisted of three main components: the fixed and mobile monitoring subsystem, as well as the cloud platform of grassland phenology intelligent monitoring. The fixed monitoring subsystem included various components, such as phenology cameras, power modules, communication modules, edge computing controllers, and supporting poles. The mobile monitoring subsystem consisted of handheld devices with capabilities, such as dustproof, waterproof, and shockproof, along with the necessary applications. The survey plot frames of the grassland field were required for the on-site image collection. The cloud-based grassland phenology intelligent monitoring platform was designed with a browser/server architecture, and then deployed on the Alibaba Cloud’s lightweight application server. As such, the platform was connected seamlessly to a MySQL database, with a user-friendly front-end built on the LayUI framework and a robust back-end using the Spring Boot framework. The efficient storage of collected images and periodic processing was obtained from the comprehensive data, such as the grassland vegetation index, vegetation cover, and phenological parameters. The cloud platform offered a wide range of functionalities, including data querying, processing, displaying, and sharing. Both the fixed and mobile monitoring subsystem were utilized to capture the grassland vegetation images. The fixed and mobile monitoring subsystem were operated and uploaded independently to the cloud platform using a cloud server. A series of processing steps were used to extract the regions of interest, and automatically calculate the grassland vegetation index using advanced techniques of image processing. The vegetation index was then converted into a grayscale space for the single-channel grayscale image. Binarization segmentation was performed to accurately extract all vegetation pixels using the maximum interclass variance method, according to the grayscale value differences between vegetation and non-vegetation areas. This process enabled the system to identify the grassland vegetation cover, and the obtained data was stored in a dedicated database. A double logistic function was used to fit the time series of the vegetation index for the vegetation growth pattern. A precise curve of vegetation growth was formed after fitting. Furthermore, a dynamic threshold method was selected to extract the important phenological parameters suitable for the growth pattern of black oatgrass using TIMESAT software. The innovative system demonstrated that an impressive accuracy rate of 90% was achieved for the grassland vegetation cover identification using the combination of the excess green index (EXG) with the maximum interclass variance method. This performance fully met the high demands of automated and bulk extraction of grassland vegetation cover. Additionally, the extraction of vegetation indices with GCC, EXG, and NGRDI, followed by fitting the vegetation growth curve with a double logistic function, can be expected to accurately identify the crucial phenological key periods, in accordance with the growth patterns of black oatgrass. The system can provide reliable technological and data support for the intelligent monitoring and management of grassland vegetation.