Seminar: Advancing Smart Agriculture through Deep Learning

This is a joint hybrid event by the IEEE Computer Society ACT Chapter, the IEEE Geoscience and Remote Sensing Society ACT&NSW Joint Chapter, and the Canberra Data Scientists Meetup. After the talk, there will be free pizzas and soft drinks provided to encourage people to stay after the presentation and socialise with others. RSVP is required, please following instructions below for registration.

Title: Advancing Smart Agriculture through Deep Learning
Speaker: A/Prof Zaiwen Feng, College of Informatics, Huazhong Agricultural University (HZAU), China
Hosts:
- Warren Jin (IEEE CS)
- Yiqing Guo (IEEE GRSS)
- Yanchang Zhao (CDS)
Date: Monday 18 Aug 2025
Times:

• 4:00pm ~ 5:00pm - Presentation
• 5:00pm ~ 5:30pm - Food/Networking

Venue: Stringybark Room, Ground Floor of Synergy Building (B801, corner of North Science Road and Dickson way), CSIRO Black Mountain Science and Innovation Park, Acton ACT

RSVP for In-Person Attendees: Please register here if you are attending in person: Attendance Sheet for In-Person Attendees. To assist in catering, please register by 6pm Saturday 16 Aug 2025. Parking information is also provided at the link.
Sign in at the Synergy Building: When you arrive at the Synergy Building foyer, please sign in using the iPad at the counter. Enter 'Yiqing Guo,' 'Warren Jin' or 'Yanchang Zhao' as the person you are visiting. Attach the printed name tag to your chest and wait to be collected. NO tailgating, please.
Sign out: Please ask one of the event organizers or a CSIRO helper to assist you with leaving the building and signing yourself off at the counter.
RSVP for Online Attendees: Please register here if you are attending online: Attendance Sheet for Online Attendees A Teams meeting link will be sent to your email before the event.
Event Sponsor: SHURA

Abstract: Integrating deep learning technologies into agriculture promises to pave the way for a more comprehensive form of agricultural intelligence—capable of processing diverse inputs, making decisions, and potentially overseeing entire farming systems autonomously. In particular, Large Language Models (LLMs) have introduced a new paradigm in smart agriculture. These models, built on the Transformer architecture, incorporate a vast number of parameters and have undergone extensive training, often demonstrating excellent performance and adaptability. This makes them effective in addressing agricultural challenges, especially where data is limited.
In this talk, the challenges and key tasks of applying LLMs in agriculture are first analyzed. The framework of the ShizishanGPT, developed by Huazhong Agricultural University, is introduced. Then, several core technologies within ShizishanGPT are presented, including data governance, construction of knowledge graph, tool learning, and AI agents. Subsequently, a couple of key scenarios that the ShizishanGPT are employed in the digital production of large fields will be introduced -- for example, precision crop breeding, crop modeling, yield prediction and the identification of crop diseases and pests. Lastly, several scenarios that the ShizishanGPT are used in the intelligent greenhouse will be introduced, such as, agricultural inspection robots and environmental control system.
Bio: Zaiwen Feng is an Associate Professor at the College of Informatics, Huazhong Agricultural University (HZAU). Before joining HZAU in 2020, he worked as a Lecturer at the State Key Laboratory of Software Engineering (SKLSE), Wuhan University, China, from 2009 to 2016, and as a Research Fellow in the STEM unit at the University of South Australia (UniSA) from 2017 to 2019. He also served as a Postdoctoral Visiting Scholar at the School of Information Systems, Queensland University of Technology (QUT) in 2014. He received his ME and PhD degrees in Computer Science from Peking University in 2006 and Wuhan University in 2009, respectively. His current research interests include knowledge fusion, Retrieval-Augmented Generation with Large Language Models, causal inference, and their applications in Smart Agriculture.