Conversational AI System for Genetic Data Analysis in Agriculture

The aim of this project is to revolutionise how farmers interact with their genetic data by leveraging the power of a Large Language Model (LLM), such as ChatGPT. This system will serve as an intuitive front end, enabling farmers to ask questions in natural language about their genetic data. The LLM will translate these questions into queries, run them on the back end, and relay the interpreted data back to the farmers in an easily understandable format. This approach will enhance decision-making processes in agriculture, particularly in breeding and livestock management.

Objectives Enhance Data Accessibility: Enable farmers to access and interpret complex genetic data effortlessly through natural language interactions. Improve Decision-Making: Provide actionable insights based on genetic data to help farmers make informed decisions about breeding, animal health, and productivity. Streamline Operations: Reduce the time and effort required for farmers to analyse genetic data, allowing them to focus on other critical aspects of farm management.

Key Features

Natural Language Interface: Farmers can ask questions in plain language, such as "Which animal will produce the best lean meat offspring " or "Which sires have produced the most offspring ". Automated Query Translation: The LLM translates natural language questions into specific queries that can be executed on the back end. Data Interpretation and Presentation: The system interprets the results of the queries and presents them in a clear, concise manner that is easy for farmers to understand. Personalised Insights: Provide recommendations and insights tailored to the specific genetic profiles and objectives of each farm.

Client


Contact: Mark Castalanelli
Phone: 0409102576
Email: [email protected]
Preferred contact: Email
Location: Bentley

IP Exploitation Model


The IP exploitation model requested by the Client is: GNU General Public License (open source) http://opensource.org/licenses/GPL-3.0


Department of Computer Science & Software Engineering
The University of Western Australia
Last modified: 18 July 2024
Modified By: Michael Wise 		UWA