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.

With the escalation of global warming, the shrinkage of mountain glaciers has accelerated globally, the water volume from glaciers has changed, and relative disasters have increased in intensity and frequency (for example, ice avalanches, surging glaciers, and glacial lake outburst floods). However, the wireless monitoring of glacial movements cannot currently achieve omnidirectional, high-precision, real-time results, since there are some technical bottlenecks. Based on wireless networks and sensor application technologies, this study designed a wireless monitoring system for measuring the internal parameters of mountain glaciers, such as temperature, pressure, humidity, and power voltage, and for wirelessly transmitting real-time measurement data. The system consists of two parts, with a glacier internal monitoring unit as one part and a glacier surface base station as the second part. The former wirelessly transmits the monitoring data to the latter, and the latter processes the received data and then uploads the data to a cloud data platform via 4G or satellite signals. The wireless system can avoid cable constraints and transmission failures due to breaking cables. The system can provide more accurate field-monitoring data for simulating glacier movements and further offers an early warning system for glacial disasters.