Dinosty Fossils seeks to enhance the accuracy and efficiency of its ammolite grading process by developing an automated system that minimizes human error. Ammolite, a rare and valuable gemstone, requires precise grading to determine its quality and market value. Currently, the grading process is subjective and prone to inconsistencies due to human judgment. The goal of this project is to design a system that uses image recognition and machine learning algorithms to assess the quality of ammolite based on predefined criteria such as color, clarity, and iridescence. This project will allow learners to apply their knowledge of computer science and data analysis to solve a real-world problem. The tasks include researching existing grading systems, developing a prototype, and testing its accuracy against human graders.

The project deliverables include a functional prototype of the automated grading system, a comprehensive report detailing the system's design and development process, and a presentation of the findings. The prototype should be capable of analyzing images of ammolite and providing consistent grading results. Additionally, the report should include recommendations for further improvements and potential implementation strategies.