Optimizing forage harvesting terms involves producing forage with an optimal ratio of nutrients recommended for livestock—specifically, the combination of crude fiber and crude protein in plants. At early plant growth stages, crude protein predominates, while fiber content (plant mass) is insufficient. Large agricultural enterprises and agroholdings with their own forage analysis laboratories analyze crude protein content daily to prevent livestock productivity losses, which is labor-intensive and costly. Thus, we propose implementing an information system for remote monitoring of digestible protein content in grasses. Remote monitoring systems for plant nitrogen content are widely applied. Digestible protein content functionally depends on nitrogen content. Therefore, by determining plant nitrogen content remotely, digestible protein and fiber contents can be calculated via regression equations. We propose an information system architecture, software and hardware infrastructure, as well as data transmission processes. The basis is a high-performance application server running Ubuntu 22.04 LTS, equipped with over 8 processor cores and 32 GB RAM. This system also aims to manage auxiliary forage production tasks. Intelligent solutions are generated in response to data warehouse queries containing retrospective local weather data (temperature, humidity) and current crude protein and fiber content in grasses. Data will flow from satellites to the server, which interfaces with the agricultural enterprise’s MES system.