Automated visual vegetation detection for weed management on transportation infrastructure

Christoph S. Werner; Simon Frey; Alexander Reiterer

doi:10.1117/12.2673849

11 August 2023 Automated visual vegetation detection for weed management on transportation infrastructure

Christoph S. Werner, Simon Frey, Alexander Reiterer

Proceedings Volume 12623, Automated Visual Inspection and Machine Vision V; 126230G (2023) https://doi.org/10.1117/12.2673849
Event: SPIE Optical Metrology, 2023, Munich, Germany

Abstract

Vegetation on traffic routes is not only an aesthetic problem. On railways and roads, it poses a safety risk by reducing the elasticity of track beds or damaging road surfaces. Therefore, complex weed management is indispensable. This is currently achieved mainly through the extensive use of herbicides or manual removal, which pollutes the environment and incurs high costs. These negative impacts can be mitigated by an automated vegetation detection which allows efficient, targeted treatment and preventive long-term monitoring. A reliable method to achieve this is to exploit the characteristic spectral fingerprint of vegetation: Chlorophyll shows a high reflectivity in the green and infrared spectral region while strongly absorbing red and blue light. We present such a visual monitoring system comprising multiple cameras and an active illumination which is employed on railroads. The individual cameras address different spectral regions and are superimposed through a position-synchronized triggering to obtain a multi-spectral image. Multi-pixel binning greatly extends the dynamic range of the cameras and, in combination with active illumination and high-speed dark frame recording, allows operation during day and night without degradation from ambient light conditions. The system achieves about 5 mm effective resolution and can operate up to a speed of 100 km/h. This is possible through embedded real-time pre-processing and data reduction already in the camera units. A processing delay of less than 100 ms is the consequence which allows targeted actuation of weed-treatment methods (e.g., spray nozzles) during movement. In combination with GNSS-sensors geo-referenced documentation of the coverage rate is possible.

Conference Presentation

Citation Download Citation

Christoph S. Werner, Simon Frey, and Alexander Reiterer "Automated visual vegetation detection for weed management on transportation infrastructure", Proc. SPIE 12623, Automated Visual Inspection and Machine Vision V, 126230G (11 August 2023); https://doi.org/10.1117/12.2673849

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