A.I. kitchen inventory management app
Martínez Vacirca, Enzo Alexander (2023)
2023
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2023061924186
https://urn.fi/URN:NBN:fi:amk-2023061924186
Tiivistelmä
Food waste is a pervasive problem that impacts various stages of the food distribution chain. Domestic food waste represents a significant portion of global food waste, as families discard substantial amounts of food annually that could have otherwise been consumed.
Households can save money and contribute to waste reduction by improving their food inventory and grocery management practices. With the widespread adoption of smartphones, nearly everyone has access to a range of Kitchen Inventory Management Apps (KIMAs). These apps enable users to manage their food and grocery inventory while keeping track of expiration dates, purchase dates, and usage, thereby making the reduction of household food waste more manageable and effective.
Although these apps provide numerous benefits, there remains ample opportunity for improvement. The industry must enhance these apps' user interface and capabilities to attract and retain more users, empowering more households to leverage these technologies and participate actively in food waste reduction efforts.
The primary objective of this thesis is to investigate the potential of Artificial Intelligence (AI) for enhancing a kitchen inventory management app, focusing on ease of use and, most importantly, efficient food data information retrieval. To achieve this goal, various AI and Machine Learning-powered technologies will be explored and evaluated, aiming to develop and implement a KIMA that minimises the time required for users to input food data into the application.
Kitchen inventory management apps can significantly improve their user interface and capabilities by harnessing the potential of AI technologies like Optical Character Recognition, Natural Language Processing, and ChatGPT. This would attract and retain more users and empower households to actively participate in reducing food waste and making more sustainable consumption choices, contributing to a greener and more efficient future.
Households can save money and contribute to waste reduction by improving their food inventory and grocery management practices. With the widespread adoption of smartphones, nearly everyone has access to a range of Kitchen Inventory Management Apps (KIMAs). These apps enable users to manage their food and grocery inventory while keeping track of expiration dates, purchase dates, and usage, thereby making the reduction of household food waste more manageable and effective.
Although these apps provide numerous benefits, there remains ample opportunity for improvement. The industry must enhance these apps' user interface and capabilities to attract and retain more users, empowering more households to leverage these technologies and participate actively in food waste reduction efforts.
The primary objective of this thesis is to investigate the potential of Artificial Intelligence (AI) for enhancing a kitchen inventory management app, focusing on ease of use and, most importantly, efficient food data information retrieval. To achieve this goal, various AI and Machine Learning-powered technologies will be explored and evaluated, aiming to develop and implement a KIMA that minimises the time required for users to input food data into the application.
Kitchen inventory management apps can significantly improve their user interface and capabilities by harnessing the potential of AI technologies like Optical Character Recognition, Natural Language Processing, and ChatGPT. This would attract and retain more users and empower households to actively participate in reducing food waste and making more sustainable consumption choices, contributing to a greener and more efficient future.