Title: Phenotyping and Skeletonization for Agricultural Robotics

Abstract:
Scientific phenotyping of plants is a crucial aspect of experimental plant breeding. By accurately measuring plant characteristics, phenotyping plays a vital role in the development of new plant varieties that are better adapted to specific environments and have improved yield, quality, and resistance to stress and disease. In addition to observing plants, robotic plant manipulation is becoming an increasingly important area of research in agriculture, as it has the potential to revolutionize farming practices.

However, obtaining labeled data for the assessment of phenotype estimates or plant models can be an extremely challenging and time-consuming process in agriculture. We tackle this common problem in agricultural robotics along several avenues. First, we propose an unsupervised assessment method for reconstructed 3D sorghum clouds, which are used to count sorghum seeds for non-destructive phenotyping. Second, we use highly consistent outdoor imagery to simplify vine segmentation with low amounts of training data. Finally, we build 3D skeletal vine models intended for vine pruning, and assess these vine models using an unsupervised approach from previous work. Overall, our work highlights the benefits of 3D plant models for phenotyping and manipulation in agriculture, and presents a new approach to assessing reconstructed point clouds.

Committee:
Prof. George Kantor (advisor)
Prof. Sebastian Scherer
Jason Zhang

Date: Tuesday, June 20th, 2023
Time: 2pm – 3:30pm EST
Location: Gates-Hillman Center (GHC) 4405
Zoom Link: https://cmu.zoom.us/j/92488841908?pwd=NHhLOW8vZkdLdWNJSTRhc2ZXYkVTZz09
Meeting ID: 92488841908
Passcode: 793864