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AI4PS Consortium

Our Mission

The Artificial Intelligence for Plant Sciences (AI4PS) consortium aims to establish an open and collaborative global scientific organization. The mission of this consortium is to organize global scientific efforts to promote the development of AI tools and their applications in advancing various aspects of plant sciences, thereby contributing to the well-being of all humanity.

Our Mission

Common Values of AI4PS Consortium

Scientific rigor

Scientific rigor

Upholding rigorous standards of scientific methodology in developing AI tools and promoting their applications in plant science research.

Scientific spirit

Scientific spirit

Fostering a scientific culture that encourages the pursuit of creative thinking, groundbreaking ideas and novel approaches to address grand challenges and unexplored fields in plant sciences.

Ethical and social responsibility

Ethical and social responsibility

Complying with high standards of professional ethics in design, implementation, and benchmarking of AI tools for scientific research; Balancing scientific advancements with the well-being of the society and environment.

Inclusive and equitable

Inclusive and equitable

Valuing equitable and inclusive AI engagement, design and implementation; Promoting the diversity, equity, and inclusion in all interactions and scientific endeavors.

Establish several working groups:

01

AI tools

Yiliang Ding and Marek Mutwil

Developing foundational AI frameworks and tools for plant science research.

02

AI for genome/protein engineering

Xiangfeng Wang and Chuang Ma

Applying AI to genome and protein engineering for crop improvement.

03

AI for phenotyping

Xiaohui Yan and Ji Zhou

Leveraging AI for high-throughput plant phenotyping and analysis.

04

AI for multi-omics research

Rui Xia and Zoran Nikoloski

Integrating multi-omics data through AI-driven approaches.

05

AI for crop improvement

Zhixi Tian and Nanqing Dong

Using AI to accelerate crop breeding and improvement programs.

06

AI for plant-biotic interaction research

Daolong Dou

Studying plant-microbe interactions using AI methods.

07

AI for pathway optimization and synthetic biology

Lin Li

Optimizing metabolic pathways and synthetic biology through AI.

08

AI4PS outreach

Xiqing Wang and Daolong Dou

Coordinating outreach activities and community engagement.

Steering Committee of AI4PS Consortium

Xiaofeng Cui
Xiaofeng Cui

Molecular Plant/Plant Communications

Yiliang Ding
Yiliang Ding

John Innes Centre, UK

Nanqing Dong
Nanqing Dong

Shanghai Artificial Intelligence Laboratory, China

Daolong Dou
Daolong Dou

Nanjing Agricultural University, China

Lexuan Gao
Lexuan Gao

Molecular Plant/Plant Communications

Chuang Ma
Chuang Ma

Northwest A&F University, China

Marek Mutwil
Marek Mutwil

University of Copenhagen, Denmark

Zoran Nikoloski
Zoran Nikoloski

Max Planck Institute of Molecular Plant Physiology, Germany

Lin Li
Lin Li

Huazhong Agricultural University, China

Zhixi Tian
Zhixi Tian

Yazhouwan National Laboratory, China

Xiaohui Yuan
Xiaohui Yuan

Yazhouwan National Laboratory, China

Rui Xia
Rui Xia

South China Agricultural University, China

Xiangfeng Wang
Xiangfeng Wang

China Agricultural University, China

Xiqing Wang
Xiqing Wang

China Agricultural University, China

Ji Zhou
Ji Zhou

Center for Excellence in Molecular Plant Sciences, CAS, China

JOIN THE AI4PS COMMUNITY

AI4PS Community