Can we efficiently and accurately identify grape clusters from remotely sensed point clouds throughout the growing season? Using a combination of SLAM (simultaneous localization and mapping) LIDAR, SfM, and machine learning, we can distinguish grape clusters while they're still on the vine. While technology has improved significantly in the past decade, there's still more work to be done with mobile LIDAR. However, SfM-derived point clouds offer us the ability to push forward with developing more advanced object detection and segmentation methods from AI/ML. In this case, the model developed is relatively simple, but it's very efficient and provides a jumping off point for future work. This is just one example of what's coming to RS&GIS and what we can offer. Augmenting existing workflows with AI/ML is not the future, it's the now and we're prepared to address the challenge. Michigan State University - Department of Geography, Environment, & Spatial Sciences Michigan State University Agisoft Metashape TensorFlow User Group (TFUG)
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