MUMBAI, India, July 24 -- Intellectual Property India has published a patent application (202641085216 A) filed by Nevedha K; Kambham Pratap Joshi; Muzamil Amin; Sunkara Swapna; V. Balasubramanian; Dr. Bhanu K N; Dr. S. Chidambaram; Mrs. M. Anitha; Pavithra S; Dr. Dhanusha. C; Dr. P. Sumalatha; and Dr. Sreekanth Rallapalli on July 11, 2026, for Machine Learning-Based Smart Irrigation Device For Optimized Water Resource Management And Enhanced Crop Yield.
Inventors include Nevedha K; Kambham Pratap Joshi; Muzamil Amin; Sunkara Swapna; V. Balasubramanian; Dr. Bhanu K N; Dr. S. Chidambaram; Mrs. M. Anitha; Pavithra S; Dr. Dhanusha. C; Dr. P. Sumalatha; and Dr. Sreekanth Rallapalli.
The application for the patent was published on July 17, 2026, under issue no. 29/2026.
Abstract: The increasing demand for sustainable agriculture has led to the development of automated irrigation systems to assist farmers in monitoring field conditions and managing water more efficiently. Existing irrigation technologies have been improved with soil moisture sensors, weather monitoring devices, and basic automation. However, many existing systems are still based on fixed irrigation schedules, predefined moisture thresholds or isolated sensor readings, which often do not represent the overall condition of an agricultural field. Such approaches typically only react after soil moisture has dropped below a certain level and are limited in their ability to anticipate changing environmental conditions, seasonal variations or crop water needs. Hence, there may still be unnecessary irrigation, uneven water distribution, excessive water consumption and reduced crop productivity. The present invention relates to a machine learning-based smart irrigation device for optimized water resource management and enhanced crop yield which integrates the concepts of IoT-enabled sensing, intelligent feature generation, predictive analytics, and automated irrigation control into a single comprehensive system. The system constantly collects field information from soil moisture sensors, temperature sensors, humidity sensors, rainfall sensors, water flow sensors and weather monitoring devices. The data collected are converted into useful agricultural indices such as Soil Moisture Index (SMI), Crop Water Requirement Score (CWRS), Irrigation Efficiency Index (IEI), Water Availability Factor (WAF), and Field Stress Indicator (FSI). A machine learning prediction engine analyses these indicators to estimate future irrigation needs, identify changing field conditions and support intelligent irrigation decisions. The framework also provides cloud-based control, automated irrigation management, decision support and continuous learning that improve the performance of the system over time. As described, the invention allows for effective use of water, avoiding unnecessary watering, improving crop health and yield, reducing operational costs and promoting sustainable management of water resources in long run across diverse agricultural scenarios. FIG.1
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