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This paper presents an approach towards the autonomous collaboration between unmanned aerial vehicle (UAV) and an unmanned ground vehicle (UGV) for weed classification and detection. The paper outlines the development of an infrastructure for continued work towards a sustainable solution. RGB and multispectral data of a strawberry crop field infested with weeds were collected from UAVs. Fully convolutional neural network (U-Net v2) was employed in an effort to create a real-time capable onsite segmentation engine. Using a combination of images collected from UAVs and highly accurate location information, target species were identified and isolated to efficiently generate a large dataset of images, which were then used to train a robust and state-of- the-art classifier. A highly sanitized dataset was used to effectively extract and augment a large amount of data without the need for manually labeling or outsourcing. The trained bounding box classifier were hosted onsite for real-time inferencing capabilities. In the proposed solution, a UAV surveys the areas of interest and transmits the images to the computing unit to detect and determine the presence of invasive weeds using the trained classifier. Positively identified locations by the UAV is stored for further investigation by the UGV. The UGV is equipped with high a precision IMU/GPS for the autonomous routing to the target location. After arriving at the target location, the UGV utilizes the onboard camera to confirm the presence of the invasive weed species. The UGV is equipped with a highly maneuverable robotic manipulator. |
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