Phenotiki is an affordable, easy-to-use, maintain and deploy image-based plant phenotyping platform. With one day setup time, and about €200 of equipment, which is easily installed, plants can be imaged and analyzed automatically (based on powerful machine learning algorithms) on a desktop workstation or via a web browser on the iPlant Collaborative cloud. And the best part is open in design and freely available to the academic community. Made simple so you can focus on doing just science.
Our solution is affordable and easy to deploy. Detailed documentation, software packages, and demos of our image-based phenotyping system are available for download. Find out in the Getting Started section about enabling your lab to phenotype rosette plants with minimal cost and effort.
Obtain hardware components and assemble the Phenotiki device.
Connect the Phenotiki device to the Internet and launch image acquisition from a web browser.
Download images from the device to your computer or browse them on the web-based BisQue platform.
Analyze the image data using the Phenotiki software on a workstation or on the cloud via web-based applications running on BisQue.
Visualize or export phenotyping results for further analysis.
We are delighted to inform you the Special Issue Acquire and Perceive: Novel Approaches for Imaging-based Plant Phenotyping on Remote Sensing (MPDI) is now live. More info here!
JUL 22, 2020The Phenotiki team has published a new paper entitled “Affordable and robust phenotyping framework to analyse root system architecture of soilâgrown plants.”
JUN 12, 2020CVPPP 2020 Workshop announced. This time our workshop will be held in conjunction with ECCV (Glasgow, UK). This workshop will be online due to the conseguences of the COVID-19 global pandemic. More info here!
MAY 10, 2020The Phenotiki team has published a new paper entitled “Doing More With Less: A Multi-Task Deep Learning Approach in Plant Phenotyping.” Code available here!
FEB 20, 2020We got a special session at the World Congress on Computational Intelligence (WCCI 2020) entitled Deep Learning for Crop Science. More info here!
DEC 18, 2019