Please use this identifier to cite or link to this item: http://cdr.uum.edu.my/jspui/handle/123456789/209
Title: Utilizing AI to Enhance Health and Productivity at Suffhil Garden Rabbit Farm: Overcoming Data Collection and Management Challenges
Authors: Mohamad Mohsin, Mohamad Farhan
Saip, Mohamed Ali
Mohd Pozi, Muhammad Syafiq
Ismail, Mohammad Hafiz
Mohd Abu Bakar, Mohd Faez
Keywords: Artificial Intelligence;Data Collection;Agricultural Technology;Rabbit Farm Productivity;Machine Learning
Issue Date: 2025
Abstract: This teaching case focused on Mr. Faiz, the owner of Suffhil Garden Rabbit Farm (SGRF), who sought to implement Artificial Intelligence (AI) to improve his rabbits' health monitoring, breeding, and productivity. His immediate goal was to build a rabbit recognition system capable of identifying different breeds, a crucial step towards developing a predictive analytics system for the farm. However, Mr. Faiz faced several challenges, including the lack of a sufficient dataset of rabbit images, the logistics of data collection, and the technical expertise required to implement AI. This problem case allowed students to explore issues such as data collection strategies, team coordination, AI model development, and the practicalities of managing large datasets. Through the case, students discussed machine learning, data management, and decision-making concepts in a real-world business context. The case was particularly suited for illustrating how technological innovations could be applied in agriculture to optimize productivity and animal welfare. It highlighted the challenges faced by small-scale enterprises looking to integrate advanced technologies like AI into their operations, making it a valuable learning tool for discussions on innovation and technology adoption in niche agricultural sectors.
URI: http://cdr.uum.edu.my/jspui/handle/123456789/209
Payment Link: http://epay.uum.edu.my/go.php?billcode=CDRCASE&productid=TC254-1
Appears in Collections:Cases in CDR

Files in This Item:
There are no files associated with this item.


Items in CDR Repository are protected by copyright, with all rights reserved, unless otherwise indicated.