Under‐canopy RGB imaging of differential leaf pigment expression for detecting nitrogen deficiency in maize

Abstract Detecting plant nitrogen (N) deficiency is important for enhancing crop yield and nutrient use efficiency. Leaf color, a key indicator of relative pigment expression and crop N status, can be monitored using red, green, and blue (RGB) under‐canopy images of maize ( Zea mays L.). This study evaluated RGB indices collected from under‐canopy images against applied N rates ranging from 0 to 271 kg ha −1 in maize trials conducted in Iowa, Minnesota, and New York (2019–2020). Digital RGB camera images were processed for leaf identification, filtered, and analyzed for RGB band averages. Results showed strong correlations between RGB indices and the applied N rates, especially in indices involving the B band, like (R − B)/(R + B), with R 2 values up to 0.75 and p < 0.001. Power analysis showed high probabilities of detecting significant effect sizes using rapid multi‐image capturing approaches that overcome image‐to‐image variability. In conclusion, under‐canopy imaging can be an inexpensive approach for measuring in‐season maize N status, and among the indices tested, (R − B)/(R + B) was the most successful at identifying N stress.