Robotic Monitoring of Colorimetric Leaf Sensors for Precision Agriculture

TL;DR

This paper presents a novel passive, bio-degradable leaf sensor system for real-time crop health monitoring, utilizing a mobile robot with hyperspectral imaging to autonomously detect and analyze plant stress indicators in precision agriculture.

Contribution

It introduces a co-designed sensor-detector system combining passive colorimetric leaf sensors with a low SWaP mobile robot for autonomous crop monitoring, advancing direct plant stress detection methods.

Findings

Successfully demonstrated autonomous navigation and sensor detection indoors and outdoors.

Achieved 80% spectral resonance accuracy in identifying crop health signals.

Enabled real-time, non-intrusive crop monitoring with low-cost, biodegradable sensors.

Abstract

Common remote sensing modalities (RGB, multispectral, hyperspectral imaging or LiDAR) are often used to indirectly measure crop health and do not directly capture plant stress indicators. Commercially available direct leaf sensors are bulky, powered electronics that are expensive and interfere with crop growth. In contrast, low-cost, passive and bio-degradable leaf sensors offer an opportunity to advance real-time monitoring as they directly interface with the crop surface while not interfering with crop growth. To this end, we co-design a sensor-detector system, where the sensor is a passive colorimetric leaf sensor that directly measures crop health in a precision agriculture setting, and the detector autonomously obtains optical signals from these leaf sensors. The detector comprises a low size weight and power (SWaP) mobile ground robot with an onboard monocular RGB camera and object detector to localize each leaf sensor, as well as a hyperspectral camera with a motorized mirror and halogen light to acquire hyperspectral images. The sensor's crop health-dependent optical signals can be extracted from the hyperspectral images. The proof-of-concept system is demonstrated in row-crop environments both indoors and outdoors where it is able to autonomously navigate, locate and obtain a hyperspectral image of all leaf sensors present, and acquire interpretable spectral resonance with 80 $\%$ accuracy within a required retrieval distance from the sensor.