MUMBAI, India, Feb. 27 -- Intellectual Property India has published a patent application (202541129903 A) filed by M. Madhan Kumar; P. T. Santhiya; S. Saritha; P. Tamilselvi; T. Nishalini; C. Nithya; S. Pandiyan; P. Tharathi; S. Vishva; and P. Devadharshini, Salem, Tamil Nadu, on Dec. 22, 2025, for 'smart irrigation twin: a predictive water optimization system using soil plant atmosphere digital twins.'

Inventor(s) include M. Madhan Kumar; P. T. Santhiya; S. Saritha; P. Tamilselvi; T. Nishalini; C. Nithya; S. Pandiyan; P. Tharathi; S. Vishva; and P. Devadharshini.

The application for the patent was published on Feb. 27, under issue no. 09/2026.

According to the abstract released by the Intellectual Property India: "Water scarcity and inefficient irrigation practices pose major challenges to sustainable agriculture and food security. Conventional irrigation systems rely on fixed schedules or basic sensor thresholds, which often result in over-irrigation, under-irrigation, water wastage, and reduced crop yield. These systems fail to account for the complex interactions between soil conditions, plant physiology, and atmospheric factors The present invention proposes a Smart Irrigation Twin: A Predictive Water Optimization System using Soil-Plant-Atmosphere Digital Twins. The system creates a real-time digital twin of the agricultural field by integrating soil moisture, nutrient levels, plant growth parameters, and atmospheric conditions. .Predictive analytics and machine learning models simulate future water demand and optimize irrigation decisions dynamically. The invention enables precise water usage, improved crop health, increased yield, and sustainable agricultural resource management. The present invention proposes a Smart Irrigation Twin: A Predictive Water Optimization System using Soil-Plant-Atmosphere Digital Twins. The system creates a real-time digital twin of the agricultural field by integrating soil moisture, nutrient levels, plant growth parameters, and atmospheric conditions. .Predictive analytics and machine learning models simulate future water demand and optimize irrigation decisions dynamically. The invention enables precise water usage, improved crop health, increased yield, and sustainable agricultural resource management."

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