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Table of Contents

1. Introduction
2. About the Violet Go 2018 Hackathon
3. Problem Statement
4. The Winning Case: Tries to Agra
5. Optimization of Feeding Plan
6. Saving Time for Farmers
7. Customer Satisfaction
8. Implementation of AI in Agriculture
9. Customer Commitment
10. Educating the Customer on Machine Learning and AI
11. Closing the Loop in Solution Presentation
12. Utilizing the Team's Skills
13. Conclusion

The Winning Case: Tries to Agra

In this article, we will discuss the winning case of the Violet Go 2018 Hackathon, titled "Tries to Agra." The hackathon was a competition organized with the aim of solving AI-related problems faced by customers. Teams were invited to participate and tackle various challenges over a period of eight weeks. One of the winning teams, consisting of Ashwin and Lhasa, worked on a case for a company called RiceGrow.

Problem Statement

RiceGrow is a company that sells feed for dairy farmers. Their customers use milking robots to feed the cows, which Collect data on the cows' behavior during milking. However, RiceGrow faced a challenge in utilizing this data effectively. They needed guidance on how to leverage the collected data for optimizing feeding plans and saving time for farmers.

Optimization of Feeding Plan

The first solution developed by the winning team focused on optimizing the feeding plan for the cows. The team discovered that the amount and quality of feed consumed by the cows greatly impacts their milk production. By getting the feeding amounts just right, the team aimed to improve milk production and prevent health issues caused by overfeeding or underfeeding.

Saving Time for Farmers

The Second solution aimed to save time for farmers who have multiple responsibilities, including taking care of the animals on their farms. With hundreds of cows to manage, it becomes challenging to identify which cows require the most Attention. The team developed a system that enables farmers to identify cows with unusual behavioral Patterns, making it easier to provide them with additional care.

Customer Satisfaction

Unlike other cases in the hackathon that focused on increasing revenue, RiceGrow was primarily interested in improving customer satisfaction. The company wanted to ensure that the farmers using their products were satisfied with the health and productivity of their cows. The team worked closely with the customer to understand their specific needs and implemented solutions to address them.

The customer satisfaction levels were measured through surveys, which gave RiceGrow concrete feedback on the overall satisfaction and health of the cows.

Implementation of AI in Agriculture

The winning case of the hackathon showcases the implementation of AI in agriculture, highlighting the versatility of AI technology. While AI is often associated with self-driving cars and financial applications, this case demonstrates how it can be applied to enhance the well-being of animals and improve customer satisfaction.

Customer Commitment

The success of the winning team can be attributed to the commitment shown by RiceGrow. They were dedicated to the project and understood the importance of leveraging AI to improve the health of their animals. This commitment fostered effective collaboration between the team and the customer, resulting in a successful solution.

Educating the Customer on Machine Learning and AI

One of the key factors in the team's success was the initial investment of time in educating the customer about machine learning and AI. The team recognized the customer's domain knowledge and understood that they possessed valuable insights. By educating the customer on the basics of AI, the team received useful ideas and guidance from them, which greatly improved the models developed.

Closing the Loop in Solution Presentation

A common challenge in AI projects is demonstrating how the solution will be used in practice. The winning team ensured that they closed the loop by presenting a solution that went beyond just making predictions. They showcased how the solution would be implemented and the benefits it would provide to the customer. This clear presentation played a significant role in their success.

Utilizing the Team's Skills

The winning team effectively utilized the skills of each member. They had a diverse team that included individuals with expertise in various areas, including web development. This allowed them to Create a user-friendly web interface that demonstrated the functionality of their solution. By involving all team members and utilizing their skills, the team maximized their chances of success.

Conclusion

The winning case of the Violet Go 2018 Hackathon, "Tries to Agra," showcased the successful implementation of AI in agriculture. Through the optimization of feeding plans and the development of systems to save time for farmers, the team effectively addressed the customer's challenges. The close collaboration with the customer, commitment to their satisfaction, and the utilization of team skills played a significant role in the team's victory. This case highlights the potential of AI technology in improving animal welfare and customer satisfaction in the agricultural industry.

Highlights

• The winning case of the Violet Go 2018 Hackathon, titled "Tries to Agra," focused on improving customer satisfaction in the agricultural industry.
• The team developed solutions for optimizing feeding plans and saving time for farmers using AI technology.
• Close collaboration with the customer and their commitment to the project contributed to the team's success.
• Educating the customer on machine learning and AI allowed for valuable insights and improved models.
• Presenting a clear and practical solution played a significant role in the team's victory.

FAQ

Q: What was the main focus of the winning case in the Violet Go 2018 Hackathon? A: The main focus of the winning case was to improve customer satisfaction in the agricultural industry.

Q: What were the two solutions developed by the winning team? A: The team developed solutions for optimizing feeding plans for cows and saving time for farmers.

Q: How did the team ensure customer satisfaction in their solution? A: The team closely collaborated with the customer to understand their specific needs and implemented solutions accordingly.

Q: How did the team leverage the customer's domain knowledge? A: The team invested time in educating the customer on machine learning and AI, which resulted in valuable insights and guidance from the customer.

Q: What role did the presentation of the solution play in the team's success? A: The team presented a clear and practical solution, showcasing how it would be implemented and the benefits it would provide to the customer.