PhD student, College of Science and Engineering, James Cook University
Giving weed control robots sharper eyes
Environmental weeds are plants that invade native ecosystems and adversely affect the survival of indigenous flora and fauna. This can include foreign plants accidentally or intentionally introduced or native plants that have become insidious due to inappropriate management or unsuited inhabitation.
In pastoral lands; weeds invade crops, smother pastures and occasionally poison livestock. In a 2012 survey conducted by Landcare Australia, weed and pest control was ranked as the most significant land management problem by nearly half of Australia’s primary producers.
Weed species recognition remains a major obstacle to the development and industry acceptance of robotic weed control technology. All weed control robots need to find weeds in order to kill them. The focus of my research is to enhance the effectiveness of weed spraying robots by developing new image recognition algorithms and technologies to improve their ability to detect weeds under realistic rangeland conditions.
Detecting weeds using machine vision is simple in the highly controlled environment of intensive cropping where the land is flat, the vegetation is homogeneous, and the light conditions may be controlled with external lighting/shading or time-of-use. However, for rangeland and rough pastures, the problem is far more difficult. Many different species of weeds and native plants may be present in the same scene, all at varying distances from the camera, all experiencing different levels of lighting/shading, with some weeds being occluded. This presents a number of issues for imaging and identification including depth of field and dynamic range limitations of camera systems.
Past experience in these difficult environments has indicated that using conventional image analysis techniques to identify leaf colour, shape or texture are not sufficient and new systems are required. The goal of my research is to develop fully tested recognition systems using a range of imaging and spectrometric properties which can be applied to any robotic platform.
The main contributions of this research will be: the publication of methods to reliably detect significant Australian weeds, adaptable to any agricultural vehicle or terrain; and the creation of the first public image dataset of some important Australian weed species for testing new detection methods in the future.
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