Active Optics for Hyperspectral Imaging of Reflective Agricultural Leaf Sensors

2025-12-10

AI Summary

To address the challenges of efficient leaf localization and in-situ hyperspectral imaging under complex field conditions, this study proposes an adaptive optical sensing system for precision agriculture. The method integrates LiDAR-based reflectance feature recognition, fast steering mirror (FSM)-enabled high-speed beam steering, electrowetting liquid lens-based continuous autofocus, and programmable filter wheel-assisted spectral band separation, establishing a closed-loop, autonomous perception–control–imaging architecture. This approach overcomes limitations of fixed field-of-view and manual focusing, enabling real-time, multi-depth, multi-target spectral acquisition. Experimental results demonstrate ≥98% leaf detection accuracy, millisecond-scale FSM response time, sub-pixel tracking precision, stable acquisition of 64-band hyperspectral data across 400–1000 nm, and a signal-to-noise ratio exceeding 32 dB.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Remote Sensing / Geospatial AI
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Autonomously detects reflective leaf sensors in agriculture
• Dynamically targets sensors for hyperspectral imaging
• Maintains focus across depths for precise spectral measurements

Innovation

• LiDAR identifies reflective sensor signatures
• Fast Steering Mirror redirects camera for targeting
• Liquid lens adjusts focus for sharp spectral imaging