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<p>The emergence of advanced digital technologies such as artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) is revolutionizing the field of plant pathology, particularly in the domain of disease detection and management in trees. This survey paper presents a&#xa0;comprehensive analysis of recent innovations and research trends in leveraging these technologies to enhance tree health monitoring, early disease diagnosis, and precision agriculture practices. This study investigates how these tools are transforming traditional agricultural diagnostics into intelligent, automated, and scalable solutions. We examine a&#xa0;wide spectrum of machine learning models applied in leaf, stem, and fruit disease detection, highlighting their accuracy, training data dependencies, and robustness in diverse environmental settings. Furthermore, the integration of IoT sensors and edge computing devices enables continuous, on-site disease monitoring, thereby reducing response time and limiting disease spread. We also include a&#xa0;synthesized comparison of multiple research studies through a&#xa0;summary table, evaluating methodologies, challenges addressed, and proposed future directions such as federated learning, edge-AI deployment, and drone-assisted image acquisition. We further present a&#xa0;generic technology-driven solution for tree disease management. This survey aims to serve as a&#xa0;reference point for researchers and agritech practitioners by consolidating state-of-the-art approaches and future research directions in smart tree disease management. By bridging the gap between agronomy and digital intelligence, these emerging technologies offer a&#xa0;promising path toward sustainable and resilient forestry and agricultural ecosystems.</p>

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Technology-Driven Solutions for Tree Disease Management: A Comprehensive Survey, Generic Framework, and Future Research Directions

  • 期刊论文

  • 发表日期：

    2026年7月22日

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  • Saniya Zahoor

摘要

The emergence of advanced digital technologies such as artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) is revolutionizing the field of plant pathology, particularly in the domain of disease detection and management in trees. This survey paper presents a comprehensive analysis of recent innovations and research trends in leveraging these technologies to enhance tree health monitoring, early disease diagnosis, and precision agriculture practices. This study investigates how these tools are transforming traditional agricultural diagnostics into intelligent, automated, and scalable solutions. We examine a wide spectrum of machine learning models applied in leaf, stem, and fruit disease detection, highlighting their accuracy, training data dependencies, and robustness in diverse environmental settings. Furthermore, the integration of IoT sensors and edge computing devices enables continuous, on-site disease monitoring, thereby reducing response time and limiting disease spread. We also include a synthesized comparison of multiple research studies through a summary table, evaluating methodologies, challenges addressed, and proposed future directions such as federated learning, edge-AI deployment, and drone-assisted image acquisition. We further present a generic technology-driven solution for tree disease management. This survey aims to serve as a reference point for researchers and agritech practitioners by consolidating state-of-the-art approaches and future research directions in smart tree disease management. By bridging the gap between agronomy and digital intelligence, these emerging technologies offer a promising path toward sustainable and resilient forestry and agricultural ecosystems.