Plant-parasitic nematodes pose a silent yet devastating threat to global agriculture, causing significant yield losses and economic damage. Traditional detection methods such as soil sampling, microscopy, and molecular diagnostics are slow, labor-intensive, and often ineffective in early-stage infestations. Nano biosensors: cuttingedge analytical tools that leverage nanomaterials like carbon nanotubes, graphene, and quantum dots to detect nematode-specific biochemical markers such as volatile organic compounds (VOCs) and oesophageal gland secretions, with unprecedented speed and accuracy. The real breakthrough lies in the fusion of artificial intelligence (AI) and nano-biosensor technology, forging a new frontier in precision agriculture. By integrating AI's powerful data analysis, pattern recognition, and predictive capabilities with the extraordinary sensitivity and specificity of nano-biosensors, it becomes possible to detect biomolecular changes in real-time, even at the earliest stages of disease progression. AI-driven nano biosensors can analyze real-time data, enhance detection precision, and provide actionable insights for farmers, enabling proactive and targeted pest management. This synergy revolutionizes nematode monitoring, paving the way for smarter, more sustainable agricultural practices. This review explores the transformative potential of AI-powered nano-biosensors in advancing precision agriculture. By integrating these technologies with smart farming systems, we move closer to real-time, costeffective, and field-deployable solutions, ushering in a new era of high-tech, eco-friendly crop protection.

Offshore wind turbines (OWTs) empoly various foundation types, among which Jacket-type offshore wind turbines (JOWTs) are often used in shallow waters with challenging soil conditions due to their lattice framework foundations and multiple anchoring points. However, prolonged exposure to harsh marine environments (e.g. storms) and age-related degradation issues like corrosion, fatigue cracking, and mechanical damage increases failure risks. To address these issues, this paper introduces a Digital Healthcare Engineering (DHE) framework, which provides a proactive strategy for enhancing the safety and sustainability of JOWTs: (1) Real-time health monitoring using IoT; (2) Data transmission via advanced communication technologies; (3) Analytics and simulations using digital twins; (4) AI-powered diagnostics and recommendations; as well as (5) Predictive analysis for maintenance planning. The paper reviews recent technological advances that support each DHE module, assesses the framework's feasibility. Additionally, a prototype DHE system is proposed to enable continuous, early fault detection, and health assessment.