Using state-of-the-art object detection and image segmentation techniques we are able to infer the yield on different plant experiments. We demonstrate and produce an effective proof of concept computer vision model employing transfer learning that is capable of accurate predictions on a variety of Tomato and Basil plant species across 4 different experimental datasets. Finally, we visualize our yield predictions and results in an interactive dashboard combining the predictions with the operational experiment data (sensor data), showing the accuracy of the predictions and providing a solid base for future work.