MUMBAI, India, April 17 -- Intellectual Property India has published a patent application (202641043423 A) filed by Chaitanya Alapati, Guntur, Andhra Pradesh, on April 5, for 'a multi-modal surveillance system and method for autonomous livestock monitoring and health analytics using laser and vision data.'
Inventor(s) include Chaitanya Alapati; Maneesh Reddy Bhavanam; Kanna Pavan Suraj; Tamma Tarun Sai Reddy; Dr. Boddu L V Siva Rama Krishna; and Dr. Arunkumar Sivapuram.
The application for the patent was published on April 17, under issue no. 16/2026.
According to the abstract released by the Intellectual Property India: "The invention relates to a multi-modal surveillance system and method for autonomous livestock monitoring and health analytics using laser and vision data. Conventional livestock monitoring approaches are constrained by manual observation, static camera deployments, and single-sensor architectures that fail to provide continuous, scalable, and non-invasive health assessment across diverse farm environments. The present invention overcomes these limitations by disclosing an integrated rotating surveillance apparatus comprising a wide-angle computer vision camera module, a compact precision laser tracking module with independent 360-degree horizontal rotation and vertical tilt capability, an IR and ultrasonic multi-modal sensor array, and an MCU-based edge and cloud processing unit enclosed within a single rotatable housing deployable across all categories of livestock farm environments. The system integrates a computer vision -based real-time object detection pipeline with a computer vision processing engine and a multi-sensor fusion algorithm to enable continuous individual animal detection, spatial localization, and persistent behavioral tracking. The system monitors a comprehensive set of health parameters including movement patterns, postural deviations, activity levels, thermal signatures, and early indicators of disease. The end-to-end architecture comprises a multi-modal data acquisition layer integrating laser and vision data streams, an edge inference layer executing computer vision-based detection and sensor fusion, a health analytics layer performing continuous anomaly scoring against individual animal baselines, a hybrid communication layer for local and remote data transmission, and a farmer-facing interface delivering real-time health alerts and analytics. The proposed system and method collectively enable early disease detection, non-invasive individual animal health profiling, and fully autonomous farm surveillance, significantly enhancing livestock productivity, reducing mortality rates, and eliminating dependency on manual monitoring across all livestock farm categories."
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