MUMBAI, India, Sept. 28 -- Intellectual Property India has published a patent application (202641111008 A) filed by Pragati Engineering College on September 16, 2026, for An Iot-Based Livestock Monitoring System For Disease Detection And Veterinary Alert Generation.
Inventors include Dr. M. Radhika Mani; Dr. Darapu Uma; Mrs. V. Venkata Lakshmi Dadala; and Mr. D Prakasa Rao.
The application for the patent was published on September 25, 2026, under issue no. 39/2026.
Abstract: Livestock health management is essential for improving animal welfare, farm productivity, food security, and the economic stability of farmers. Conventional livestock monitoring mainly depends on periodic manual observation, which may fail to identify early symptoms of disease, particularly in large farms and remote locations. This work proposes an IoT-based livestock monitoring system for continuous disease detection and automatic veterinary alert generation. Each animal is identified using an RFID tag and monitored through wearable sensors that measure body temperature, heart rate, physical activity, feeding behaviour, rumination, and geographical location. Environmental sensors installed in the livestock shed measure humidity, ambient temperature, air quality, and harmful gas concentrations that may influence animal health. The collected sensor readings are processed by an edge controller and transmitted to an IoT gateway using wireless technologies such as LoRa, Zigbee, Wi-Fi, or cellular communication. The gateway securely forwards the information to a cloud platform, where it is stored in an individual electronic health record. A machine-learning-based disease detection module analyses physiological, behavioural, and environmental data to identify abnormal patterns. Data preprocessing techniques are applied to remove noise, handle missing values, and normalize measurements before feature extraction and classification. The system compares current readings with normal health ranges and historical records to classify each animal’s condition as normal, observation required, suspected disease, or emergency. When a potential illness is identified, the platform automatically generates an alert for the farmer and veterinarian through a mobile application, SMS, or web dashboard. The alert contains the animal identification number, location, detected abnormalities, predicted health condition, risk level, and recommended immediate action. This information enables veterinarians to prioritize critical cases, provide preliminary advice, and arrange timely examination and treatment. The farmer can also view real- time health status, movement history, previous illnesses, vaccination details, and treatment records through the dashboard. Following veterinary intervention, treatment information is added to the database for continuous follow-up monitoring. The proposed system supports early disease detection, reduces delayed treatment, limits infection transmission, and lowers livestock mortality. It also minimizes manual monitoring effort and improves data-driven farm management. Therefore, the system provides a scalable and practical approach for intelligent livestock healthcare, particularly for large, rural, and geographically distributed farming environments.
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