Automated dairy farm management system powered by IoT and ML

• Web Portal for environment monitoring. • Automation of Feed and Waste management using conveyors and sensors. • Automatic Fire Extinguisher in farm and Automation of roof based on weather • GPS and RFID based security. • Machine Learning Model for cattle disease detection with public access on any device. • Prediction Results in multiple languages for users across the regions. This paper presents an Automated Dairy Farm Management System that uses Internet of Things (IoT) and Machines Learning (ML) to implement an intelligent, real-time monitoring and automation system for dairy farms of the future. The proposed system enables monitoring of temperature, relative humidity, air quality, fire detection and rainfall through interconnected sensors. The data is uploaded to a cloud for data collection, monitoring and analysis. There are automatic technology solutions for cattle feeding, managing waste, lighting, fire safety and shed operation based on the weather. An important feature is the integration of a machine-learning-based cattle disease identification model that can classify cattle's skin condition as healthy, lumpy skin disease or mastitis from image-based input with high classification accuracy. To maintain reproducibility, three-fold cross-validation was applied. For IoT elements, sensor calibration against reference devices, and sensor responses to a triggered stimulus, were evaluated with repeated trials. The results indicate the system has ML accuracy of 92%, actuator response latency < 0.001 seconds and sensor error margin ±5% verifying both accuracy and robustness of the design. The model is installed on an open access website and supports output in all Indian languages along with information on the disease, precautions, and treatment. The system uses geo-fencing and zonal classification while tracing the location of cattle and status in real-time with distance from the barn. The complete system provides a scalable, inexpensive, and efficient solution for dairy farm management, allowing a step change improvement in terms of animal welfare, human effort, and operational transparency.

Institutions

Publication Details

Journal
Results in Engineering
Published
2025-10-01
DOI
https://doi.org/10.1016/j.rineng.2025.107508
Citations
2
Primary Topic
Genomics and Phylogenetic Studies
Type
article
Field-Weighted Citation Impact
1.61
Controls
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article

Automated dairy farm management system powered by IoT and ML

2 citations
Results in Engineering
Genomics and Phylogenetic Studies
1.61
article

Automated dairy farm management system powered by IoT and ML

article en
2 citations

Abstract

• Web Portal for environment monitoring. • Automation of Feed and Waste management using conveyors and sensors. • Automatic Fire Extinguisher in farm and Automation of roof based on weather • GPS and RFID based security. • Machine Learning Model for cattle disease detection with public access on any device. • Prediction Results in multiple languages for users across the regions. This paper presents an Automated Dairy Farm Management System that uses Internet of Things (IoT) and Machines Learning (ML) to implement an intelligent, real-time monitoring and automation system for dairy farms of the future. The proposed system enables monitoring of temperature, relative humidity, air quality, fire detection and rainfall through interconnected sensors. The data is uploaded to a cloud for data collection, monitoring and analysis. There are automatic technology solutions for cattle feeding, managing waste, lighting, fire safety and shed operation based on the weather. An important feature is the integration of a machine-learning-based cattle disease identification model that can classify cattle's skin condition as healthy, lumpy skin disease or mastitis from image-based input with high classification accuracy. To maintain reproducibility, three-fold cross-validation was applied. For IoT elements, sensor calibration against reference devices, and sensor responses to a triggered stimulus, were evaluated with repeated trials. The results indicate the system has ML accuracy of 92%, actuator response latency < 0.001 seconds and sensor error margin ±5% verifying both accuracy and robustness of the design. The model is installed on an open access website and supports output in all Indian languages along with information on the disease, precautions, and treatment. The system uses geo-fencing and zonal classification while tracing the location of cattle and status in real-time with distance from the barn. The complete system provides a scalable, inexpensive, and efficient solution for dairy farm management, allowing a step change improvement in terms of animal welfare, human effort, and operational transparency.

Results in EngineeringVol. 28
Vellore Institute of Technology University (IN)
Zero hunger
Openalex Percentile: Top 14%
Genomics and Phylogenetic Studies
1.61
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