Underground winter bamboo shoots, prized for their high nutritional value and economic significance, face harvesting challenges owing to inefficient manual methods and the lack of specialized detection technologies. This review systematically evaluates current detection approaches, including manual harvesting, microwave detection, resistivity methods, and biomimetic techniques. While manual methods remain dominant, they suffer from labor shortages, low efficiency, and high damage rates. Microwave-based technologies demonstrate high accuracy and good depths but are hindered by high costs and soil moisture interference. Resistivity methods show feasibility in controlled environments but struggle with field complexity and low resolution. Biomimetic approaches, though innovative, face limitations in odor sensitivity and real-time data processing. Key challenges include heterogeneous soil conditions, performance loss, and a lack of standardized protocols. To address these, an integrated intelligent framework is proposed: (1) three-dimensional modeling via multi-sensor fusion for subsurface mapping; (2) artificial intelligence (AI)-driven harvesting robots with adaptive excavation arms and obstacle avoidance; (3) standardized cultivation systems to optimize soil conditions; (4) convolution neural network-transformer hybrid models for visual-aided radar image analysis; and (5) aeroponic AI systems for controlled growth monitoring. These advancements aim to enhance detection accuracy, reduce labor dependency, and increase yields. Future research should prioritize edge-computing solutions, cost-effective sensor networks, and cross-disciplinary collaborations to bridge technical and practical gaps. The integration of intelligent technologies is poised to transform traditional bamboo forestry into automated, sustainable smart forest farms, addressing global supply demands while preserving ecological integrity.

This study focuses on the underground shallow gas detection project in the Lingkun Island area of the northern entrance tunnel of the Wenzhou City Light Rail S2 line. Based on geological exploration data of shallow gas, we chose the technique of controlled-release gas with static pressure as the experimental foundation, integrating various technologies such as multifunctional in-situ probing, electrical methods, and seismic waves, comprehensively researching shallow gas detection technology in the Lingkun Island area. We conducted field probing experiments to accurately obtain the physical and mechanical properties of gas-rich soil layers and further studied the possibility of determining gas-rich locations. By applying parallel electrical methods, we can accurately identify and distinguish areas of anomalous resistivity in shallow geological structures. Based on abnormal changes in acoustic impedance in strata, we used seismic wave methods, including seismic CT and seismic wave scattering technology, to accurately reveal the presence and depth of shallow gas, providing reliable basis for accurate determination of shallow gas. Finally, we summarized a comprehensive plan for underground shallow gas detection technology, covering on-site data collection, data processing, and image interpretation of results, which will provide valuable references for future shallow gas exploration in relevant areas.