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Open access Aug 2026

Stimulating maize growth under different water regimes through foliar application of micronutrients

Drought and nutrient stress constrain maize growth, production and productivity. Precision agricultural tools such as foliar fertilisation and irrigation enhance maize growth through mitigation of stresses at different growth stages. The study conducted at Látókép Crop Production Experimental Site, University of Debrecen, Hungary during year 2023, assessed the physiological and growth performance of two maize hybrids exposed to foliar fertilisation under precision drip irrigation and non-irrigated conditions. Foliar fertilizers composed of; nitrogen (10 g/l), zinc (8 g/l), K2O (8.5 g/l), P2O5 (0.83 g/l), and S (8.93 g/l). Data on plant height, leaf area index (LAI), normalized difference vegetation index (NDVI), and relative chlorophyll content (SPAD) were collected at the V12, R1, R4, and R6 analysed using a T-test in Genstat software (12th edition). Results showed significant increases in all parameters at the V12, R1, and R4, but NDVI, LAI, and SPAD declined at R6 under both irrigation and non-irrigated conditions. Foliar fertilisation of the two maize hybrids under precision drip irrigation conditions showed that NDVI and plant height had a significant difference (p < 0.05) while no significance difference was noted for SPAD and leaf area index. The percentage LAI due to foliar fertilisation under irrigated conditions was 16.86% and 18.39% for FAO490 and FAO290 respectively however the effect was slightly higher for FAO290 than FAO490. FAO490 and FAO290 hybrids recorded a 23.45% and 10.05% foliar fertilisation effect respectively over control under non-irrigated conditions. FAO490 hybrid consistently performed better under irrigated conditions compared to FAO290 which performed better under non-irrigated conditions. FAO490 hybrid consistently performed better under irrigated conditions compared to FAO290 which performed better under non-irrigated conditions.

Brian Ssemugenze, Ocwa Akasairi, Fred Peter Kabaale et al. · 0 citations
Review Open access Sep 2026

Precision Agriculture in Maize Production: A Systematic Review of Technologies, Applications, and Yield Optimisation

Precision agriculture (PA) has emerged as a data-driven approach for improving maize (Zea mays L.) production through the integration of remote sensing, Geographic Information Systems (GISs), Global Navigation Satellite Systems (GNSSs), the Internet of Things (IoT), and machine learning (ML). This systematic review evaluates the application of PA for yield optimisation and resource-use efficiency in maize production between 2020 and 2026. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, 464 records were identified from Scopus and Web of Science, of which 129 studies met the inclusion criteria. The reviewed literature comprised field experiments (28.6%), remote sensing and PA integration studies (25.5%), machine learning applications (19.4%), climate-informed PA strategies (23.4%), and soil degradation studies (3.1%). Remote sensing and integrated multi-technology systems were the most extensively investigated approaches, followed by ML-based models for yield prediction and crop monitoring. Overall, PA technologies enhanced maize productivity, nutrient-use efficiency, water-use efficiency, and yield prediction accuracy through site-specific management and data-driven decision support. Despite its considerable potential, the adoption of PA remains constrained by high implementation costs, technical complexity, and limited data availability. Collectively, these findings demonstrate that precision agriculture provides an effective framework for sustainable maize intensification by improving productivity, optimising resource-use efficiency, and strengthening resilience to climate variability.

M. Osman, Ronald Kuunya, Rania Alrasheed et al. · 0 citations

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