A Framework to Identify Allergen and Nutrient Content in Fruits and Packaged Food using Deep Learning and OCR

Rohini, B.; Pavuluri, Divya Madhuri; Kumar, LS Naresh; Soorya, V; Aravinth, J

doi:10.1109/icaccs51430.2021.9441800

Cited by 9 publications

(2 citation statements)

References 18 publications

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“…Fruit allergens could cause significant harm for allergic people. Research discusses different ways to protect the humans from the allergens [17,44,47]. We concentrated on the group of people who lack recognition about fruit allergen.…”

Section: Discussionmentioning

confidence: 99%

“…The quality of baby food can be evaluated using an electronic tongue to determine whether the quality is sufficient or not [43]. An application, with CNN model and Fruits 360 dataset, to classify fruit and show the allergen information is mentioned in [44]. Using image segmentation to classify the quality of Manalagi apple on smart agriculture was discussed in [36].…”

Section: Quality Of Foodmentioning

confidence: 99%

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FruitPAL: An IoT-Enabled Framework for Automatic Monitoring of Fruit Consumption in Smart Healthcare

Alkinani

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This research proposes FruitPAL and FruitPAL 2.0. They are full automatic devices that can detect fruit consumption to reduce the risk of disease. Allergies to fruits can seriously impair the immune system. A novel device (FruitPAL) detecting fruit that can cause allergies is proposed in this thesis. The device can detect fifteen types of fruit and alert the caregiver when an allergic reaction may have happened. The YOLOv8 model is employed to enhance accuracy and response time in detecting dangers. The notification will be transmitted to the mobile device through the cloud, as it is a commonly utilized medium. The proposed device can detect the fruit with an overall precision of 86%. FruitPAL 2.0 is envisioned as a device that encourages people to consume fruit. Fruits contain a variety of essential nutrients that contribute to the general health of the human body. FruitPAL 2.0 is capable of analyzing the consumed fruit and then determining its nutritional value. FruitPAL 2.0 has been trained on YOLOv5 V6.0. FruitPAL 2.0 has an overall precision of 90% in detecting the fruit. The purpose of this study is to encourage fruit consumption unless it causes illness. Even though fruit plays an important role in people's health, it might cause dangers. The proposed work can not only alert people to fruit that can cause allergies, but also it encourages people to consume fruit that is beneficial for their health.

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Section: Discussionmentioning

confidence: 99%