New paper out #growthdynamics #yieldprediction #ML #bioenergycrops #research mdpi.com/2072-4292/13/9… Remote Sensing MDPI

A #CABBI study led by Sebastian Varela used high temporal resolution images from drones to understand the relative importance of dynamic and static information throughout the season to predict final above-ground biomass for sorghum. This could benefit work to improve bioenergy crops.

SUMMER INTERNSHIP OPPORTUNITY! Join us for RISE, our paid summer internship program in bioenergy research!🌱🔬 We are seeking undergrads nationwide from underrepresented groups to work with research mentors! 🔶 Deadline to apply: Feb. 24 Learn more!➡️ cabbi.bio/news/outreach/…

#Landsat 9 data is officially available to the public! Since the satellite launched, there have been many steps before we could call it fully operational. ⬇️ [1/5]

Happy to share new machine-learning, UAV, high-throughput phenotyping study on lodging in biomass sorghum demonstrating the power of 3D-CNN and time-course data mdpi.com/2072-4292/14/3… CABBI @iBioIllinois Crop Sciences Genomic Biology North American Plant Phenotyping Network

We're celebrating the International Day of Women and Girls in Science! Let's look back on the life of Marie Skłodowska Curie: a Nobel Prize laureate who dedicated her life to science and became one of the world's greatest scientists. #WomenInScience #NobelPrize

A new study by CABBI #Feedstock Production & #Sustainability researchers shows that using spatio-temporal 3D-convolution neural networks & UAV time series imagery helped rapidly predict lodging damage in sorghum & could enhance field plant phenotyping. 📰mdpi.com/2072-4292/14/3…

New preprint: Deep convolutional neural networks exploit high spatial and temporal resolution aerial imagery to predict key traits in miscanthus. Sebastian Varela CABBI North American Plant Phenotyping Network DOE Office of Science ASPB Agronomy, Crop, and Soil Science Societies TaylorGeospatial Mark J. Lara cabidigitallibrary.org/doi/10.31220/a…

Deep Convolutional Neural Networks Exploit High-Spatial- and -Temporal-Resolution Aerial Imagery to Phenotype Key Traits in Miscanthus mdpi.com/1905090 #mdpiremotesensing via Remote Sensing MDPI Seth ISU Biomass TaylorGeospatial Mark J. Lara

CABBI researchers combined machine learning & 3-D drone imaging to create a powerful new tool to measure growth in #miscanthus. The result is a faster and cheaper way to evaluate the most promising varieties of this next-generation #bioenergy crop! 🌱 ➡️ cabbi.bio/cabbi-team-add…

Team adds powerful new dimension to phenotyping next-gen bioenergy crop University of Illinois doi.org/gq6m58 phys.org/news/2022-11-t…

A team led by CABBI Director Andrew Leakey & Postdoc Sebastian Varela created a machine-learning tool that can teach itself to differentiate between aerial images of flowering & nonflowering grasses—greatly accelerating agricultural field research. News ➡️news.illinois.edu/new-approach-m…

Climate Deal Would Commit Every Nation to Limiting Emissions nyti.ms/1xgmIXt

Everything is a harmonic oscillator wired.com/2016/07/everyt…

#KSUCROPS UPDATE: #New PAPER, use of #drones x #corn #plant height K-State Agronomy Kansas Corn PrecisionHawk OPEN ACCESS: goo.gl/gkKJuC

Global innovation rankings: The innovation game buff.ly/2xaBANd

Check out our new article @KSUCROPS #mdpiremotesensing Early-Season Stand Count Determination in Corn via Integration of Imagery from Unmanned Aerial Systems (UAS) and Supervised Learning Techniques mdpi.com/2072-4292/10/2… Remote Sensing MDPI