Supervisors: Elmar Rückert, Nils Rottmann
Based on the intention to build an autonomous lawn mower robot, this work examines the viability of a sensor and microprocessor for onboard plant classification using machine learning. Usually, some sort of fencing is required to keep the robot in its intended processing area, so such a sensor would allow the robot to differentiate between grass and e.g. flowers. Also, the drive and blade speed can be adjusted for certain species or plant densities, etc.
For this, a data set was collected utilizing a specific method called chlorophyll fluorescence induction. A series of narrowband LEDs are used to drive this process, while a spectrometer measures the spectral intensity. The plant will fluoresce in specific wavelengths when sufficiantly illuminated. With this data, machine learning algorithms are trained to explore if they are capable to classify these plants without further information.
With accuracies up to 98% for three plants commonly present on a lawn and up to 86% for eight plants, the results show that chlorophyll fluorescence is a viable method for classification, even under sunlight using the random forest machine learning algorithm