MUMBAI, India, Aug. 31 -- Intellectual Property India has published a patent application (202641099785 A) filed by Dr. Seeni Mohamed Aliar Maraikkayar; Dr. Tamil Selvi; and Dr. Parisa Beham on August 18, 2026, for Iot And Ai-Based Precision Irrigation And Crop Disease Prediction System.
Inventors include Dr. Seeni Mohamed Aliar Maraikkayar; Dr. Tamil Selvi; and Dr. Parisa Beham.
The application for the patent was published on August 28, 2026, under issue no. 35/2026.
Abstract: The present invention relates to an IoT and artificial intelligence based precision agriculture system configured to optimize irrigation and predict crop diseases using real-time field sensing, environmental monitoring, image analysis, and intelligent decision-making. The system comprises a plurality of distributed Internet of Things sensors positioned within an agricultural field for continuously acquiring soil moisture, soil temperature, ambient temperature, relative humidity, light intensity, rainfall, leaf wetness, and other crop- related parameters. The acquired data are transmitted through a wireless communication network to an edge computing unit or cloud- based processing platform. An artificial intelligence module processes the sensor data together with crop images captured by imaging devices to determine crop water requirements, identify abnormal plant conditions, and predict potential diseases at an early stage. A machine- learning based irrigation decision engine evaluates soil, weather, crop growth stage, and historical irrigation information to calculate an optimized irrigation requirement and selectively control one or more irrigation valves, pumps, or drip irrigation zones. A disease prediction module applies image-processing and machine-learning or deep-learning algorithms to detect disease-related visual characteristics and correlate them with environmental conditions favorable for pathogen development. The system generates alerts, disease-risk scores, irrigation recommendations, and crop-health information through a user interface or mobile application. The proposed system reduces excessive water consumption, minimizes crop stress, supports early disease intervention, reduces unnecessary chemical application, and improves agricultural productivity. The integration of IoT sensing, artificial intelligence, automated irrigation control, crop-image analysis, and predictive analytics provides a scalable, adaptive, and data-driven agricultural management solution capable of supporting precision irrigation and proactive crop disease management across different crops and field conditions.
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