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<p>This paper explores the transformative impact of Artificial Intelligence (AI) on agricultural healthcare, focusing on its current applications, associated challenges, and future trends. AI technologies including Convolutional Neural Networks (CNNs) and other machine learning models have shown exceptional performance in tasks such as crop disease identification, weed detection, soil condition assessment, and livestock health monitoring. Notably, AI-based systems have reached up to 99.35% accuracy in diagnosing crop diseases and nutrient imbalances, greatly enhancing the precision and efficiency of modern farming practices. AI also enables intelligent irrigation and nutrient management by combining real-time analytics with IoT sensor data, resulting in optimized resource use and a&#xa0;40% reduction in water consumption. In animal husbandry, AI tools utilize video analytics for continuous health monitoring, allowing for early disease detection and improved livestock welfare. Despite these promising advancements, key challenges remain—including high implementation costs, data privacy concerns, and insufficient digital infrastructure in rural regions. The paper further examines the potential role of AI in addressing global issues such as climate change, food insecurity, and sustainable agricultural development. Future developments are expected to focus on enhancing AI model capabilities, advancing sensor technologies, and strengthening data protection measures, along with ensuring inclusive access to AI tools for small-scale farmers. Overcoming these hurdles will be critical for AI to fully transform agricultural systems into more resilient, efficient, and sustainable models globally.</p>

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Revolutionizing Agricultural Healthcare with Artificial Intelligence: Applications, Challenges, and Future Prospects

  • 期刊论文

  • 发表日期：

    2026年6月1日

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  • Mohammad Ahsan,
  • Ananya Aditi Singh,
  • Desh Iyer,
  • Bam Bahadur Sinha

摘要

This paper explores the transformative impact of Artificial Intelligence (AI) on agricultural healthcare, focusing on its current applications, associated challenges, and future trends. AI technologies including Convolutional Neural Networks (CNNs) and other machine learning models have shown exceptional performance in tasks such as crop disease identification, weed detection, soil condition assessment, and livestock health monitoring. Notably, AI-based systems have reached up to 99.35% accuracy in diagnosing crop diseases and nutrient imbalances, greatly enhancing the precision and efficiency of modern farming practices. AI also enables intelligent irrigation and nutrient management by combining real-time analytics with IoT sensor data, resulting in optimized resource use and a 40% reduction in water consumption. In animal husbandry, AI tools utilize video analytics for continuous health monitoring, allowing for early disease detection and improved livestock welfare. Despite these promising advancements, key challenges remain—including high implementation costs, data privacy concerns, and insufficient digital infrastructure in rural regions. The paper further examines the potential role of AI in addressing global issues such as climate change, food insecurity, and sustainable agricultural development. Future developments are expected to focus on enhancing AI model capabilities, advancing sensor technologies, and strengthening data protection measures, along with ensuring inclusive access to AI tools for small-scale farmers. Overcoming these hurdles will be critical for AI to fully transform agricultural systems into more resilient, efficient, and sustainable models globally.