Abstract Title
Value of Manual Plant Identification in an Age of Drone Technology
Abstract
In our research, we are training and optimizing a learning algorithm to predict plant species using drone and plant survey data. Drones collect images to produce both RGB photos and multi-spectral data that can be utilized in a variety of ways depending on application. Complimenting the drone imagery, surveying collects basic plant characteristics and GPS locations. When both of these are then given to a learning algorithm as training data it has more information than only a picture to distinguish plants. We have seen promising success in the past getting past the proof of concept. This work serves as an example of how manual plant Identification is still commonly used. With the development of AI, it’s likely that in the near future we could be identifying plants with photos alone. For now, researchers still rely on traditional methods to support these emerging technologies.
Value of Manual Plant Identification in an Age of Drone Technology
In our research, we are training and optimizing a learning algorithm to predict plant species using drone and plant survey data. Drones collect images to produce both RGB photos and multi-spectral data that can be utilized in a variety of ways depending on application. Complimenting the drone imagery, surveying collects basic plant characteristics and GPS locations. When both of these are then given to a learning algorithm as training data it has more information than only a picture to distinguish plants. We have seen promising success in the past getting past the proof of concept. This work serves as an example of how manual plant Identification is still commonly used. With the development of AI, it’s likely that in the near future we could be identifying plants with photos alone. For now, researchers still rely on traditional methods to support these emerging technologies.