Authors :
Adarsh Pandey; Dr. Prabhash Chandra Pathak
Volume/Issue :
Volume 11 - 2026, Issue 10 - October
DOI :
https://doi.org/10.38124/ijisrt/26Oct728
Note : A published paper may take 4-5
working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and
ResearchGate.
Abstract :
Cow shelters need coordination between animal identification, health observations, activity tracking, environmental factors, feed and water supplies, vaccination schedules, medical records, and operational actions. Precision livestock farming has created useful tools using sensors, identification systems and artificial intelligence. Recent reviews still show problems with combining information, checking data quality, making systems work together, and supporting decisions [1] [2] [7] [10] [24]. This paper introduces a cow-shelter system that connects RFID animal tagging, different types of IoT sensors, ESP32-based data collection at the edge, Wi-Fi communication, Flask web services, MySQL databases, clear rule-based alerts, and machine learning—all within a single management process. The system links animal data with shelter-zone details and keeps track of time, data quality, management actions, and past alerts. The analysis is done in stages: simple rules give alerts, and machine learning comes later once enough labelled and checked data is available. Features used in the analysis include sensor readings, changes over time, average trends, activity summaries, environmental data, and feed and water status. A repeatable way to test the system is created, measuring sensing delay, alert timing, data transmission reliability, dashboard speed, recovery after failure, and how well predictions work. The main contribution is not in technology but in the full system design: an information-to-action process built around animal identity, multiple sensors, resource tracking, records, analysis, alerts, and human input. The paper does not give accuracy or timing results and does not claim the individual parts are new.
Keywords :
Artificial Intelligence, Internet of Things, Smart Cow Shelter, Machine Learning, Precision Livestock Farming, Decision Support, Multimodal Sensing.
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Cow shelters need coordination between animal identification, health observations, activity tracking, environmental factors, feed and water supplies, vaccination schedules, medical records, and operational actions. Precision livestock farming has created useful tools using sensors, identification systems and artificial intelligence. Recent reviews still show problems with combining information, checking data quality, making systems work together, and supporting decisions [1] [2] [7] [10] [24]. This paper introduces a cow-shelter system that connects RFID animal tagging, different types of IoT sensors, ESP32-based data collection at the edge, Wi-Fi communication, Flask web services, MySQL databases, clear rule-based alerts, and machine learning—all within a single management process. The system links animal data with shelter-zone details and keeps track of time, data quality, management actions, and past alerts. The analysis is done in stages: simple rules give alerts, and machine learning comes later once enough labelled and checked data is available. Features used in the analysis include sensor readings, changes over time, average trends, activity summaries, environmental data, and feed and water status. A repeatable way to test the system is created, measuring sensing delay, alert timing, data transmission reliability, dashboard speed, recovery after failure, and how well predictions work. The main contribution is not in technology but in the full system design: an information-to-action process built around animal identity, multiple sensors, resource tracking, records, analysis, alerts, and human input. The paper does not give accuracy or timing results and does not claim the individual parts are new.
Keywords :
Artificial Intelligence, Internet of Things, Smart Cow Shelter, Machine Learning, Precision Livestock Farming, Decision Support, Multimodal Sensing.