Integrating Morphological and Digital Traits to Optimize Nitrogen Use Efficiency in Maize Hybrids

The yield of maize (Zea mays L.) is highly influenced by nitrogen fertilization. This study investigated the impact of nitrogen fertilization on morphophysiological traits in maize (Zea mays L.) and developed algorithms to relate manual phenotyping and digital phenotyping of maize with leaf nitrogen and digital field image traits. The experiment included three hybrid maize varieties, V1 (Hybrid 981), V2 (BARI Hybrid maize-9), and V3 (Hybrid P3396), which were evaluated across three nitrogen levels (N1 = 100 kg N ha−1, N2 = 200 kg N ha−1, N3 = 300 kg N ha−1) in a split-plot design with three replications. The results revealed that nitrogen levels (N), varieties (V), and their interactions (V × N) significantly influenced traits such as plant height (PH), leaf area index (LAI), normalized difference vegetation index (NDVI), canopy cover (CC), chlorophyll content (Chl a and Chl b), leaf nitrogen content (LNC), total dry matter (TDM), and grain yield. The hybrid P3396 with 300 kg N ha−1 (V3N3) achieved the highest grain yield of 14.45 t ha−1, which was statistically similar to that of Hybrid 981 and 300 kg N ha−1 (V1N3). Nitrogen significantly improved dry matter accumulation, leaf area, and physiological parameters, with maximum values recorded during flowering. The NDVI, CC, and SPAD were strongly correlated with LNC and TDM, highlighting their potential as indicators for nitrogen management. The digital imaging traits analysed via software effectively differentiated the nitrogen treatments, demonstrating their utility for precise nitrogen application. In conclusion, nitrogen fertilization at 300 kg N ha−1 optimized the growth and yield of hybrid maize, with Hybrid P3396 performing best. This study underscores the role of advanced phenotyping tools in improving nitrogen use efficiency and sustainable maize production.