用自适应光学系统自动定位并采集植株传感器的光谱数据

Monitoring plant health increasingly relies on leaf-mounted sensors that provide real-time physiological data, yet efficiently locating and sampling these sensors in complex agricultural environments remains a major challenge. We present an integrated, adaptive, and scalable system that autonomously detects and interrogates plant sensors using a coordinated suite of low-cost optical components including a LiDAR, liquid lens, monochrome camera, filter wheel, and Fast Steering Mirror (FSM). The system first uses LiDAR to identify the distinct reflective signatures of sensors within the field, then dynamically redirects the camera s field of view via the FSM to target each sensor for hyperspectral imaging. The liquid lens continuously adjusts focus to maintain image sharpness across varying depths, enabling precise spectral measurements. We validated the system in controlled indoor experiments, demonstrating accurate detection and tracking of reflective plant sensors and successful acquisition of their spectral data. To our knowledge, no other system currently integrates these sensing and optical modalities for agricultural monitoring. This work establishes a foundation for adaptive, low-cost, and automated plant sensor interrogation, representing a significant step toward scalable, real-time plant health monitoring in precision agriculture.