Automatic Fruits Calories Estimation through Convolutional Neural Networks

Ghazanfar Latif; Batool Alsalem; Wejdan Mubarky; Nazeeruddin Mohammad; Jaafar Alghazo

doi:10.1145/3397125.3397154

Automatic Fruits Calories Estimation through Convolutional Neural Networks

Ghazanfar Latif
, Batool Alsalem
, Wejdan Mubarky
, Nazeeruddin Mohammad
, Jaafar Alghazo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

24 Scopus citations

Abstract

With the increased interest in healthy eating and diet control, the importance of keeping track of daily calorie intake is an important topic nowadays. In this paper, we propose a novel technique to automatically calculate calories based on the picture of various foods on a plate. As a first step, this paper concentrate on food plates with fruit and vegetable content only. A new dataset was developed internally for this research consisting of a total of 41,509 images. In this paper, we propose a Deep Convolutional Neural Network (DCNN) for the automatic recognition process. A custom design application was developed for capturing the image, recognition, and automatic calculation of calories. The results showed that over 92% recognition rates were achieved on most fruits and vegetables.

Original language	English (US)
Title of host publication	Proceedings of the 2020 6th International Conference on Computer and Technology Applications, ICCTA 2020
Publisher	Association for Computing Machinery
Pages	17-21
Number of pages	5
ISBN (Electronic)	9781450377492
DOIs	https://doi.org/10.1145/3397125.3397154
State	Published - Apr 14 2020
Externally published	Yes
Event	6th International Conference on Computer and Technology Applications, ICCTA 2020 - Antalya, Turkey Duration: Apr 14 2020 → Apr 16 2020

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	6th International Conference on Computer and Technology Applications, ICCTA 2020
Country/Territory	Turkey
City	Antalya
Period	4/14/20 → 4/16/20

Bibliographical note

Publisher Copyright:
© 2020 ACM.

Keywords

Agriculture produce
Calories Estimation
Convolutional Neural Networks (CNNs)
Deep Learning
Fruits Classification
Image Processing
Vegetable Classification

Publisher link

10.1145/3397125.3397154

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Cite this

Latif, G., Alsalem, B., Mubarky, W., Mohammad, N., & Alghazo, J. (2020). Automatic Fruits Calories Estimation through Convolutional Neural Networks. In Proceedings of the 2020 6th International Conference on Computer and Technology Applications, ICCTA 2020 (pp. 17-21). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3397125.3397154

Automatic Fruits Calories Estimation through Convolutional Neural Networks. / Latif, Ghazanfar; Alsalem, Batool; Mubarky, Wejdan et al.
Proceedings of the 2020 6th International Conference on Computer and Technology Applications, ICCTA 2020. Association for Computing Machinery, 2020. p. 17-21 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Latif, G, Alsalem, B, Mubarky, W, Mohammad, N & Alghazo, J 2020, Automatic Fruits Calories Estimation through Convolutional Neural Networks. in Proceedings of the 2020 6th International Conference on Computer and Technology Applications, ICCTA 2020. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 17-21, 6th International Conference on Computer and Technology Applications, ICCTA 2020, Antalya, Turkey, 4/14/20. https://doi.org/10.1145/3397125.3397154

Latif G, Alsalem B, Mubarky W, Mohammad N, Alghazo J. Automatic Fruits Calories Estimation through Convolutional Neural Networks. In Proceedings of the 2020 6th International Conference on Computer and Technology Applications, ICCTA 2020. Association for Computing Machinery. 2020. p. 17-21. (ACM International Conference Proceeding Series). doi: 10.1145/3397125.3397154

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abstract = "With the increased interest in healthy eating and diet control, the importance of keeping track of daily calorie intake is an important topic nowadays. In this paper, we propose a novel technique to automatically calculate calories based on the picture of various foods on a plate. As a first step, this paper concentrate on food plates with fruit and vegetable content only. A new dataset was developed internally for this research consisting of a total of 41,509 images. In this paper, we propose a Deep Convolutional Neural Network (DCNN) for the automatic recognition process. A custom design application was developed for capturing the image, recognition, and automatic calculation of calories. The results showed that over 92\% recognition rates were achieved on most fruits and vegetables.",

keywords = "Agriculture produce, Calories Estimation, Convolutional Neural Networks (CNNs), Deep Learning, Fruits Classification, Image Processing, Vegetable Classification",

author = "Ghazanfar Latif and Batool Alsalem and Wejdan Mubarky and Nazeeruddin Mohammad and Jaafar Alghazo",

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doi = "10.1145/3397125.3397154",

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AU - Alghazo, Jaafar

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N2 - With the increased interest in healthy eating and diet control, the importance of keeping track of daily calorie intake is an important topic nowadays. In this paper, we propose a novel technique to automatically calculate calories based on the picture of various foods on a plate. As a first step, this paper concentrate on food plates with fruit and vegetable content only. A new dataset was developed internally for this research consisting of a total of 41,509 images. In this paper, we propose a Deep Convolutional Neural Network (DCNN) for the automatic recognition process. A custom design application was developed for capturing the image, recognition, and automatic calculation of calories. The results showed that over 92% recognition rates were achieved on most fruits and vegetables.

AB - With the increased interest in healthy eating and diet control, the importance of keeping track of daily calorie intake is an important topic nowadays. In this paper, we propose a novel technique to automatically calculate calories based on the picture of various foods on a plate. As a first step, this paper concentrate on food plates with fruit and vegetable content only. A new dataset was developed internally for this research consisting of a total of 41,509 images. In this paper, we propose a Deep Convolutional Neural Network (DCNN) for the automatic recognition process. A custom design application was developed for capturing the image, recognition, and automatic calculation of calories. The results showed that over 92% recognition rates were achieved on most fruits and vegetables.

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KW - Convolutional Neural Networks (CNNs)

KW - Deep Learning

KW - Fruits Classification

KW - Image Processing

KW - Vegetable Classification

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Automatic Fruits Calories Estimation through Convolutional Neural Networks

Abstract

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Bibliographical note

Keywords

Publisher link

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