DEVELOPMENT OF A DEEP LEARNING-BASED MOBILE APPLICATION TO DETECT FRESHNESS OF FRUITS
Fruits play a crucial role in our diet as they contain nutrients that are vital for our health. These nutrients are important as they help us protect against chronic diseases. Fruits that are not fresh will not contain as many nutrients than when it was fresh. Thus, it is important to ensure that...
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| Autore principale: | |
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| Natura: | Tesi |
| Lingua: | inglese |
| Pubblicazione: |
Department of Computer Science and Engineering, MIST
2024
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| Accesso online: | http://dspace.mist.ac.bd:8080/xmlui/handle/123456789/804 |
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| Riassunto: | Fruits play a crucial role in our diet as they contain nutrients that are vital for our health.
These nutrients are important as they help us protect against chronic diseases. Fruits that are
not fresh will not contain as many nutrients than when it was fresh. Thus, it is important to
ensure that only fresh fruits are consumed. However, there exist a large number of consumers
that do not know how to select fruits that are fresh when purchasing. Besides that, the fruit
industry uses harmful chemicals in order to perform fruit inspections which makes the fruit to
lose its nutrients. To solve the problems stated above, this paper proposes a mobile
application that can detect freshness in fruits. To do this, this project utilizes Deep Learning
technologies in conjunction with a mobile application in order to predict the freshness of
fruits in real time. Although there are several applications that can perform fruit freshness
prediction, they require the user to have several external devices in order to accurately predict
its freshness. Therefore, this project focused on developing an application that can do fruits
freshness prediction in real time without needing extra devices. |
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