Open Access

ARTICLE

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

Shamim Ara Bagum¹, Mahbub Ul Islam², M Shalim Uddin^2,*, Sripati Sikder³, Ahmed Gaber⁴, Akbar Hossain^5,*

1 Seed Technology Division, Bangladesh Agricultural Research Institute, Joydebpur, Gazipur, 1701, Bangladesh
2 Oilseeds Research Center, Bangladesh Agricultural Research Institute, Joydebpur, Gazipur, 1701, Bangladesh
3 Department of Crop Physiology and Ecology, Hajee Mohammad Danesh Science & Technology University, Dinajpur, 5200, Bangladesh
4 Department of Biology, Faculty of Science, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia
5 Soil Science Division, Bangladesh Wheat and Maize Research Institute, Dinajpur, 5200, Bangladesh

* Corresponding Authors: M Shalim Uddin. Email: ; Akbar Hossain. Email:

(This article belongs to the Special Issue: Integrated Nutrient Management in Cereal Crops)

Phyton-International Journal of Experimental Botany 2025, 94(6), 1897-1919. https://doi.org/10.32604/phyton.2025.065607

Received 18 March 2025; Accepted 27 May 2025; Issue published 27 June 2025

Abstract

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.

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Keywords

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Supplementary Material

Supplementary Material File

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