Development and validation of a precision nitrogen management app for enhancing sustainability in maize production

Real-time nitrogen (N) management tools like GreenSeeker™ (GS™), SPAD meters, and leaf colour chart (LCC) predict crop N requirements using leaf reflectance and greenness. However, the high cost of GS™ and SPAD meters restricts their adoption by farmers, while LCC provides only qualitative guidance and depends on subjective colour perception. Digital imaging-based measurement of leaf greenness using RGB values converted to Hue-Saturation-Brightness (HSB) for calculating the Dark Green Colour Index (DGCI) offers a cost-effective alternative for assessing crop N status. In this study, we developed and validated a DGCI-based Android application, Pusa N Doctor, for precision N prescription. Data on DGCI, NDVI, SPAD, LCC, and leaf nitrogen concentration (N%) were collected at 10-day intervals from 25 to 65 days after sowing during 2020-2022. DGCI at 35 and 45 DAS exhibited the strongest correlation with N%, followed by NDVI. While N% served as the absolute indicator of leaf N status, DGCI, NDVI, SPAD, and LCC acted as relative indicators. Calibration of N% against relative measures and DGCI against NDVI, SPAD, and LCC showed DGCI to be the most accurate predictor of N% (R² = 0.69; MAPE = 16.96 %) and NDVI (R² = 0.85; MAPE = 8.76 %). Applying 50 kg N ha⁻¹ as basal plus two DGCI-guided splits reduced N use by 18.6 % and improved nitrogen use efficiency indicators by 20-23 % compared to the recommended dose, with comparable yields and 15 % lower N losses. The app provides a scalable, farmer-friendly, and environmentally sustainable solution aligned with SDG 12 and SDG 13.