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The demand for improved decision‐making products for cereal production systems has placed added emphasis on using plant sensors in‐season, and that incorporate real‐time, site specific, growing environments. The objectives of this work were to describe validated in‐season sensor‐based algorithms presently being used in cereal grain production systems for improving nitrogen use efficiency (NUE) and cereal grain yields. A review of research programs in the central Great Plains that have developed sensor‐based N recommendations for cereal crops was performed. Algorithms included multiple land‐grant university, government, and industry programs. A common thread in this review is the use of active sensors, particularly those using the normalized difference vegetation index (NDVI) for quantifying differences in fertilized and non‐fertilized areas, within a specific cropping season. In‐season prediction of yield potential over different sites and years is possible using NDVI, planting date, sensing date, cumulative growing degree days (GDD), and rainfall. Other in‐season environment‐specific inputs have also been used. Early passive sensors have advanced to by‐plant N fertilization using active NDVI and by‐plant statistical properties. Most recently, sensor‐based algorithm research has focused on the development of generalized mathematical models for determining optimal crop N application. The development and promotion of fee‐based modeling approaches for nutrient management continues. Nonetheless, several algorithms using active sensors for in‐season N management are available from state and government sources at no cost and that have been extensively field tested and can be modified by producers. Core Ideas Normalized difference vegetation index algorithms can improve fertilizer N efficiency. Normalized difference vegetation index sensors currently sold employ these algorithms. Algorithms rely on knowledge that increased yields increase fertilizer N demand. Yield potential and N response are independent. Nitrogen‐rich strips help to predict in‐season grain yields.