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<p>Lumpy Skin Disease (LSD) presents a global threat to cattle populations, with over 110,000 cattle deaths reported in India alone. Lumpy skin disease presents a significant economic challenge to India's cattle industry, impacting farmers and agricultural productivity. A timely and accurate diagnosis is essential for effective disease management. A novel methodology has been developed to address this problem by using stacking ensemble approaches. This innovative approach combines various foundational models, including capsule networks and extreme learning machines. Capsule networks represent spatial hierarchies, while extreme learning machines handle feature spaces with high dimensions. During the ultimate forecasting stage, a level 2 model employing Random Forest is utilized. This complete approach has an impressive accuracy rate of 98.30%, significantly enhancing the efficacy of identifying lumpy skin disease in cattle. By integrating advanced machine learning techniques into diagnostic procedures, stakeholders may minimize human errors, reduce misdiagnosis, minimize economic losses, and safeguard the health and productivity of cattle, thereby improving agricultural resilience and sustainability in the face of this disease threat.</p>

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The strategic utilization of machine learning insights to optimize the management of skin health in cattle

  • 期刊论文

  • 发表日期：

    2025年1月20日

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  • Goddeti Mallikarjun,
  • V. A. Narayana

摘要

Lumpy Skin Disease (LSD) presents a global threat to cattle populations, with over 110,000 cattle deaths reported in India alone. Lumpy skin disease presents a significant economic challenge to India's cattle industry, impacting farmers and agricultural productivity. A timely and accurate diagnosis is essential for effective disease management. A novel methodology has been developed to address this problem by using stacking ensemble approaches. This innovative approach combines various foundational models, including capsule networks and extreme learning machines. Capsule networks represent spatial hierarchies, while extreme learning machines handle feature spaces with high dimensions. During the ultimate forecasting stage, a level 2 model employing Random Forest is utilized. This complete approach has an impressive accuracy rate of 98.30%, significantly enhancing the efficacy of identifying lumpy skin disease in cattle. By integrating advanced machine learning techniques into diagnostic procedures, stakeholders may minimize human errors, reduce misdiagnosis, minimize economic losses, and safeguard the health and productivity of cattle, thereby improving agricultural resilience and sustainability in the face of this disease threat.