Automated Recipe Generation AI Tool from Food Images using Machine Learning. Approach for Ingredient Extraction and Cooking Instructions
This report represents a crucial step towards fulfilling the requirements for a Bachelor's degree in Computer Science from Kabale University. It is structured into three main chapters, each playing a distinct role in advancing the understanding and implementation of the proposed project. Chapt...
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| Format: | Thesis |
| Language: | English |
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Kabale University
2025
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| Online Access: | http://hdl.handle.net/20.500.12493/2887 |
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| author | Lwamaza, Eddy Nuwasiima, Happy |
| author_facet | Lwamaza, Eddy Nuwasiima, Happy |
| author_sort | Lwamaza, Eddy |
| collection | KAB-DR |
| description | This report represents a crucial step towards fulfilling the requirements for a Bachelor's degree in Computer Science from Kabale University. It is structured into three main chapters, each playing a distinct role in advancing the understanding and implementation of the proposed project.
Chapter one served as the foundational section of the report, encompassing essential elements such as the introduction, background of the study, problem statement, objectives (both general and specific), research questions, study scope (including time, content, and geographical scope), justification, and theoretical framework. This chapter provided an overview of the research, contextualizes it within existing literature, identifies the specific issue or challenge being addressed, identifies the objectives and research questions, and establishes the theoretical foundation guiding the investigation.
Chapter two delves into the literature review, a critical component of the research process that provides a comprehensive analysis of existing recipe generation AI tools. This chapter evaluates the strengths and weaknesses of current systems, aiming to identify areas for improvement and address any shortcomings. Additionally, it examines image processing techniques used in recipe generation AI tools, sourced from reputable platforms such as Google Scholar and IEEE. The insights gained from the literature review serve as a basis for future research and provide criteria for evaluating case studies in subsequent chapters.
Chapter three focuses on the research methodology, detailing the approach used to achieve the objectives outlined in chapter one. This chapter describes the data collection approaches, tools, and techniques employed in system design and development. Furthermore, it analyzes the validation approach used to assess the effectiveness and reliability of the proposed system. By providing transparency and clarity regarding the research process, chapter three ensures validity in the study's findings, laying the groundwork for a robust and impactful research.
Chapter four presents a detailed analysis of the system requirements, architecture, and design considerations essential for the successful implementation of the proposed recipe generation AI tool. It outlines the systematic approach taken to analyze user needs, define functional and non-functional requirements, and develop a robust system architecture. Additionally, this chapter discusses the design methodologies and tools utilized to translate conceptual requirements into tangible system specifications.
Chapter five elucidates the implementation phase of the project, providing insights into the practical realization of the designed system. It discusses the selection of programming languages, frameworks, and technologies employed in building the recipe generation AI tool. Furthermore, this chapter elaborates on the development process, including code implementation, testing procedures, and integration efforts, culminating in the deployment ready version of the system.
Chapter six synthesizes the findings from the preceding chapters, offering a comprehensive discussion on the project's outcomes, implications, and contributions to the field of recipe generation AI. It critically evaluates the achieved results against the defined objectives, addressing any discrepancies and identifying areas of success. Moreover, this chapter presents concluding remarks, recommendations for further research, and outlines avenues for future work, ensuring the project's continuity and potential for ongoing improvement. |
| format | Thesis |
| id | oai:idr.kab.ac.ug:20.500.12493-2887 |
| institution | KAB-DR |
| language | English |
| publishDate | 2025 |
| publisher | Kabale University |
| record_format | dspace |
| spelling | oai:idr.kab.ac.ug:20.500.12493-28872025-02-15T00:00:55Z Automated Recipe Generation AI Tool from Food Images using Machine Learning. Approach for Ingredient Extraction and Cooking Instructions Lwamaza, Eddy Nuwasiima, Happy Automated Recipe Generation AI Tool Food Images Machine Learning Ingredient Extraction Cooking Instructions This report represents a crucial step towards fulfilling the requirements for a Bachelor's degree in Computer Science from Kabale University. It is structured into three main chapters, each playing a distinct role in advancing the understanding and implementation of the proposed project. Chapter one served as the foundational section of the report, encompassing essential elements such as the introduction, background of the study, problem statement, objectives (both general and specific), research questions, study scope (including time, content, and geographical scope), justification, and theoretical framework. This chapter provided an overview of the research, contextualizes it within existing literature, identifies the specific issue or challenge being addressed, identifies the objectives and research questions, and establishes the theoretical foundation guiding the investigation. Chapter two delves into the literature review, a critical component of the research process that provides a comprehensive analysis of existing recipe generation AI tools. This chapter evaluates the strengths and weaknesses of current systems, aiming to identify areas for improvement and address any shortcomings. Additionally, it examines image processing techniques used in recipe generation AI tools, sourced from reputable platforms such as Google Scholar and IEEE. The insights gained from the literature review serve as a basis for future research and provide criteria for evaluating case studies in subsequent chapters. Chapter three focuses on the research methodology, detailing the approach used to achieve the objectives outlined in chapter one. This chapter describes the data collection approaches, tools, and techniques employed in system design and development. Furthermore, it analyzes the validation approach used to assess the effectiveness and reliability of the proposed system. By providing transparency and clarity regarding the research process, chapter three ensures validity in the study's findings, laying the groundwork for a robust and impactful research. Chapter four presents a detailed analysis of the system requirements, architecture, and design considerations essential for the successful implementation of the proposed recipe generation AI tool. It outlines the systematic approach taken to analyze user needs, define functional and non-functional requirements, and develop a robust system architecture. Additionally, this chapter discusses the design methodologies and tools utilized to translate conceptual requirements into tangible system specifications. Chapter five elucidates the implementation phase of the project, providing insights into the practical realization of the designed system. It discusses the selection of programming languages, frameworks, and technologies employed in building the recipe generation AI tool. Furthermore, this chapter elaborates on the development process, including code implementation, testing procedures, and integration efforts, culminating in the deployment ready version of the system. Chapter six synthesizes the findings from the preceding chapters, offering a comprehensive discussion on the project's outcomes, implications, and contributions to the field of recipe generation AI. It critically evaluates the achieved results against the defined objectives, addressing any discrepancies and identifying areas of success. Moreover, this chapter presents concluding remarks, recommendations for further research, and outlines avenues for future work, ensuring the project's continuity and potential for ongoing improvement. 2025-02-14T14:42:20Z 2025-02-14T14:42:20Z 2024 Thesis Lwamaza, E., & Nuwasiima, H. (2024). Automated recipe generation AI tool from food images using machine learning: Approach for ingredient extraction and cooking instructions. Kabale University. http://hdl.handle.net/20.500.12493/2887 en Attribution-NonCommercial-NoDerivs 3.0 United States http://creativecommons.org/licenses/by-nc-nd/3.0/us/ application/pdf Kabale University |
| spellingShingle | Automated Recipe Generation AI Tool Food Images Machine Learning Ingredient Extraction Cooking Instructions Lwamaza, Eddy Nuwasiima, Happy Automated Recipe Generation AI Tool from Food Images using Machine Learning. Approach for Ingredient Extraction and Cooking Instructions |
| title | Automated Recipe Generation AI Tool from Food Images using Machine Learning. Approach for Ingredient Extraction and Cooking Instructions |
| title_full | Automated Recipe Generation AI Tool from Food Images using Machine Learning. Approach for Ingredient Extraction and Cooking Instructions |
| title_fullStr | Automated Recipe Generation AI Tool from Food Images using Machine Learning. Approach for Ingredient Extraction and Cooking Instructions |
| title_full_unstemmed | Automated Recipe Generation AI Tool from Food Images using Machine Learning. Approach for Ingredient Extraction and Cooking Instructions |
| title_short | Automated Recipe Generation AI Tool from Food Images using Machine Learning. Approach for Ingredient Extraction and Cooking Instructions |
| title_sort | automated recipe generation ai tool from food images using machine learning approach for ingredient extraction and cooking instructions |
| topic | Automated Recipe Generation AI Tool Food Images Machine Learning Ingredient Extraction Cooking Instructions |
| url | http://hdl.handle.net/20.500.12493/2887 |
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