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@article{208534,
        author = {Vinit Masale and Suyog Mamankar and Prathamesh Sawant and Mhaboob Ali and Prof. Jyoti Shrote},
        title = {Edge-AI Based Smart Pet Monitoring Framework: A Rabbit Case Study},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {395-401},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=208534},
        abstract = {Continuous Monitoring of animals is hard when the people who take care of them are not around. Changes in how they are eaten, how much they drink, the way they stand and what they do during the day can show if their health is changing or if their normal routine is different. This paper talks about an Edge AI-based Smart Pet Monitoring Framework. The goal is watching the pet’s behaviour and the environment around them in time. The system keeps privacy in mind. It uses computer vision, IoT, sensors and computing that happens close to the data not in the cloud This reduces the need for internet access. A Raspberry Pi serves as the edge device running a Python and OpenCV image processing chain that uses a YOLOv9 detector to spot feeding, drinking, posture and activity states. Temperature, humidity, ultrasonic distance and water-level sensors give environment data while an Arduino control layer handles alerts using an LCD, buzzer, LEDs and a servo. Local inference gives real-time processing and improves privacy, data ownership and response time. We tested than 200 images in a controlled setting and found it works but the test is still early. This framework offers a flexible base for smart pet monitoring, which could help at home in shelters and, for vets once more tests are done.},
        keywords = {Edge AI, IoT, Smart Pet Monitoring, Animal Behaviour Analysis, Computer Vision, YOLOv9, Raspberry Pi, Multimodal Sensing, Privacy-Aware Monitoring.},
        month = {September},
        }