Publication

EVALUATION OF POSTHARVEST SENESCENCE IN BROCCOLI VIA HYPERSPECTRAL IMAGING

Abstract: Fresh fruits and vegetables are invaluable for human health, but their quality deteriorates before reaching consumers due to ongoing biochemical processes and compositional changes. The current lack of any objective indices for defining “freshness” of fruits or vegetables limits… Read More

A BENCHMARK DATASET FOR INDIVIDUAL TREE CROWN DELINEATION IN CO-REGISTERED AIRBORNE RGB, LIDAR AND HYPERSPECTRAL IMAGERY FROM THE NATIONAL ECOLOGICAL OBSERVATION NETWORK

Abstract: Broad scale remote sensing promises to build forest inventories at unprecedented scales. A crucial step in this process is designing individual tree segmentation algorithms to associate pixels into delineated tree crowns. While dozens of tree delineation algorithms have been… Read More

MIL-CAM ACCEPTED TO ECCV 2020 WORKSHOP ON COMPUTER VISION PROBLEMS IN PLANT PHENOTYPING!

Congratulations to our labmates and collaborators: Guohao Yu, Alina Zare, Weihuang Xu, Roser Matamala, Joel Reyes-Cabrera, Felix B. Fritschi and Thomas E. Juenger!  Their paper, “Weakly Supervised Minirhizotron Image Segmentation with MIL-CAM” was recently accepted to the 16th European Conference… Read More

STATE ESTIMATOR AND MACHINE LEARNING ANALYSIS OF RESIDUAL DIFFERENCES TO DETECT AND IDENTIFY FDI AND PARAMETER ERRORS IN SMART GRIDS

Abstract: In the modern Smart Grid (SG), cyber-security is an increasingly important topic of research. An attacker can mislead the State Estimation (SE) process through a False Data Injection (FDI) on real-time measurement values or they can attack the parameters… Read More