P. Prema, B Sivasankari, N Aananthi, M Marimuthu, S Mary Arokia, J Stephen Raj Arockia, K P Kishore
Precision irrigation is critical for improving water productivity in maize under conditions of increasing water scarcity and climate variability. This study evaluated the impact of internet of things (IoT)-based smart drip irrigation and irrigation scheduling strategies on yield, water-use efficiency and economic returns of hybrid maize. Eight irrigation treatments, including IoT-based sensor-controlled drip irrigation, pan evaporation (PE)-based scheduling, irrigation water/cumulative pan evaporation ratio (IW/CPE) methods, conventional drip irrigation, surface irrigation and flood irrigation, were evaluated under field conditions. In the IoT-based treatments, soil moisture sensors continuously monitored root-zone moisture and automatically initiated irrigation when soil water depletion reached either 60 % or 80 % of the available soil water (ASW), representing moderate and high allowable depletion levels respectively. In contrast, conventional drip irrigation was operated using fixed irrigation schedules without real-time soil moisture sensing or automated feedback control. Results indicated significant differences (p ≤ 0.05) among treatments. The IoT-based drip irrigation at 60 % ASW depletion recorded the highest grain and straw yields, with grain yield increasing by 39 % compared with flood irrigation. The same treatment achieved the highest water-use efficiency, producing 2.62 times more grain per unit of water applied and reducing irrigation water use by 47 %. Economic analysis showed that IoT-based irrigation generated the highest profitability, with net returns increasing by 88 % over flood irrigation. The results demonstrate that sensor-based automated drip irrigation scheduled at 60 % allowable soil water depletion is an efficient and economically viable strategy for improving maize productivity, water-use efficiency and water conservation under water-limited conditions.