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<p>Livestock diseases continue to pose significant challenges for smallholder dairy farmers, particularly in regions with limited access to veterinary services and real-time health monitoring infrastructure. To address this, the present study proposes a cost-effective IoT-enabled framework for early disease detection in dairy cattle, targeting smallholder farmers in resource-limited regions. A custom smart collar was developed to monitor body temperature, pulse rate, and activity levels in 150 cows across seven districts of Punjab. To analyse the collected data, a novel hybrid model, SM-GBoost-LSTM, was implemented, combining Gradient Boosting (GBoost) for structured features, Long Short-Term Memory (LSTM) networks for temporal patterns, and SMOTE for handling class imbalance. The model achieved high performance, attaining 93.56% accuracy, 91.42% precision, 77.77% recall, and 84.02% F1-score. The system also integrates real-time cloud analytics and a bilingual mobile application (English/Punjabi) to deliver timely health alerts. Overall, the proposed framework provides a scalable, field-validated, and affordable AI-driven solution for improving livestock healthcare in smallholder dairy farming.</p>

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An IoT-Driven hybrid AI model for health monitoring of cows

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月21日

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  • Devinder Kaur,
  • Amandeep Kaur Virk

摘要

Livestock diseases continue to pose significant challenges for smallholder dairy farmers, particularly in regions with limited access to veterinary services and real-time health monitoring infrastructure. To address this, the present study proposes a cost-effective IoT-enabled framework for early disease detection in dairy cattle, targeting smallholder farmers in resource-limited regions. A custom smart collar was developed to monitor body temperature, pulse rate, and activity levels in 150 cows across seven districts of Punjab. To analyse the collected data, a novel hybrid model, SM-GBoost-LSTM, was implemented, combining Gradient Boosting (GBoost) for structured features, Long Short-Term Memory (LSTM) networks for temporal patterns, and SMOTE for handling class imbalance. The model achieved high performance, attaining 93.56% accuracy, 91.42% precision, 77.77% recall, and 84.02% F1-score. The system also integrates real-time cloud analytics and a bilingual mobile application (English/Punjabi) to deliver timely health alerts. Overall, the proposed framework provides a scalable, field-validated, and affordable AI-driven solution for improving livestock healthcare in smallholder dairy farming.