Providing Expert Support for Farmers through Conversational AI
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This is a paid opportunity. In order to be eligible to apply for this project, you need to be part of the Omdena community and have finished at least one AI Innovation Challenge.
You can find our upcoming AI Innovation Challenges at https://omdena.com/projects.
The problem
This project seeks to address a significant challenge faced by pig farmers: the difficulty in accessing and navigating extensive documentation on pork reproduction. Currently, farmers require reliable, accurate information to optimize their operations and improve productivity. However, the vast amount of available data, combined with the complexity of navigating through detailed documentation from sources like the US Pork Center of Excellence, can be overwhelming. This situation creates a barrier to accessing critical information, which is essential for making informed decisions regarding the care, breeding, and overall management of pigs. The lack of an efficient, user-friendly system for information retrieval hampers operational efficiency and can lead to suboptimal farming practices.
Impact of the Problem
On Farmers:
- Operational Inefficiency: Farmers spend excessive time searching for specific information within extensive documents, reducing their overall operational efficiency.
- Reduced Productivity: The inability to quickly access relevant information can lead to less informed decisions and practices, directly impacting the productivity and health of the pigs.
- Increased Stress and Workload: Navigating through complex documentation adds to the farmers’ workload and stress, potentially leading to errors and oversight in pig management.
On the Agriculture Sector:
- Slowed Innovation Adoption: The difficulty in accessing up-to-date information and best practices can slow the adoption of innovative breeding and management techniques in the pork industry.
- Impact on Food Supply: Inefficiencies and reduced productivity in pork production can have downstream effects on the availability and cost of pork, impacting the broader food supply chain.
On Animal Welfare:
- Compromised Animal Health: Without easy access to comprehensive guidelines and best practices, farmers may not implement optimal care and breeding practices, potentially compromising animal welfare.
To mitigate these challenges, the project aims to develop a Conversational AI Solution, leveraging a Large Language Model (LLM) system, designed to provide pig farmers with easy and immediate access to comprehensive pork reproduction documentation. This AI-driven system will be capable of delivering precise answers and referencing specific sections of the documentation, significantly streamlining the information retrieval process. By simplifying access to vital information, the solution will directly enhance operational efficiency, improve productivity, and reduce the stress associated with managing pig farms, ultimately contributing to more informed farming practices, better animal welfare, and a more resilient pork production sector.
The project goals
The overarching aim of this project is to transform the way pig farmers access and utilize information on pork reproduction by developing a Conversational AI Solution. This initiative is critical for simplifying the information retrieval process, directly enhancing operational efficiency and productivity in pig farming. The project is organized around a series of focused objectives:
- Knowledge Extraction: The project will leverage existing Large Language Models (LLMs) to parse and comprehend the extensive documentation provided by the US Pork Center of Excellence on pork reproduction. This involves creating an indexed database that allows for efficient information retrieval by the LLM, ensuring farmers can access the knowledge they need promptly.
- Custom Model Development: A custom interface layer will be developed atop pre-existing LLMs, specifically designed to query the indexed database. This layer will accurately understand and match farmer inquiries with the most relevant sections of the documentation, utilizing advanced techniques such as Retrieval-Augmented Generation (RAG), prompt engineering, and few-shot prompting to refine model performance. The selection of these techniques will be based on their relevance and effectiveness in enhancing the AI’s ability to deliver precise information.
- Document Retrieval: The system will not only answer queries but also retrieve and present specific sections of the documentation relevant to the inquiry. This feature will include links to particular parts of PDFs or web pages, increasing the transparency and reliability of the AI’s responses.
- Feedback Loop: An innovative feedback mechanism will be introduced, allowing users to rate their interactions with the AI through a like/dislike system. In cases of dissatisfaction, users can specify their reasons from a set of predefined options, such as incorrect information or inadequate citation. This feedback loop will involve integrating interactive buttons and response pop-up windows within the chat interface, facilitating continuous improvement of the AI solution.
By concentrating on these strategic objectives, this project aims to deliver a Conversational AI Solution that not only showcases the potential of AI in agricultural knowledge dissemination but also lays the groundwork for further advancements in AI-driven informational tools. This initiative promises to significantly improve the accessibility and usability of critical agricultural knowledge, marking a substantial leap forward in support for pig farmers.
**More details will be shared with the designated team.
Why join? The uniqueness of Omdena Top Talent Projects
Top Talent opportunities come as a natural next step after participating in Omdena’s AI Innovation Challenges.
Everyone in the community is eligible to participate once they have shown the relevant skills based on the merit of involvement in past Omdena challenges and the community.
If you are looking for the next challenge after participating in one or more Omdena AI Innovation Challenges, then we believe you have made the right choice! With a healthy, pressured environment, you will be pushed to contribute, learn and grow even more.
Find more information on how an Omdena Top Talent Program works
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Eligibility to join an Omdena Top Talent project
Finished at least one AI Innovation Challenge
Received a recommendation from the Omdena Core Team Member/ Project Owner (PO) is a plus
Skill requirements
Good English
Machine Learning Engineer
Experience working with NLP and/or LLMs is a plus.
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