Sigit Adinugroho, Yuita Arum Sari, Jaya Mahar Maligan, Kartika Sari, Yusuf Gladiensyah Bihanda, Nabila Nuraini, Danial Fatchurrahman


In pandemic conditions, awareness of keeping a healthy balance is necessary. One is considering food consumption and understanding its nutrition content to avert food waste. We have been developing a prototype to estimate the nutrition of leftover food, and the main problem lies in image segmentation. Therefore, we propose the Improved Food Image Segmentation (IFIS) and Contour Based Calculation (CBC) to measure the area of the segmented image instead of pixel-wise. First, the tray box image is acquired and broken down into compartments using an automated cropping algorithm. The first step of this proposed method is tray box image acquisition and dividing the compartment using an automatic cropping algorithm. Then each compartment is treated using IFIS, calculates the result of IFIS by CBC, measures the estimated leftover by Automatic Food Leftover Estimation (AFLE), and then predicts the nutritional content. The evaluation is applied by comparing the actual measurement from the Comstock method and leftover estimation by the proposed algorithm. The result shows that Root Square Means Error (RMSE) reaches 0.48 compared to the actual weighing scale and 96.67% accuracy compared to the Comstock method. Based on the results, the proposed algorithm is sufficient to be applied.


Leftover food estimation; food image segmentation; Comstock; nutrition estimation

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Chavan, S. V., & Sambare, S. (2016). Study and Analysis of Image Segmentation Techniques for Food Images.International Journal of Computer Applications, 136(4).

Ciocca, G., Napoletano, P., & Schettini, R. (2015). Food recognition and leftover estimation for daily diet monitoring. International Conference on Image Analysis and Processing, 334–341.

Dehais, Anthimopoulos, M., & Mougiakakou, S. (2016). Food image segmentation for dietary assessment. Proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management, 23–28.

Harnis, P., Sari, Y. A., & Rahman, M. A. (2019). Segmentation of Traditional Food Images Using Otsu Thresholding in CIE LAB Color Space (Segmentasi Citra Kue Tradisional menggunakan Otsu Thresholding pada Ruang Warna CIE LAB). Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer e-ISSN, 3(7), 6799-6808.

He, Y., Xu, C., Khanna, N., Boushey, C. J., & Delp, E. J. (2013). Food image analysis: Segmentation, identification and weight estimation. IEEE International Conference on Multimedia and Expo (ICME), 1–6.

Leonard W., AurandA, Edwin Woods, and Marion R. Wells, "Sampling and Proximate Analysis," Springer, 19–34.

Maulana, L., Bihanda, Y. G., & Sari, Y. A. (2020). Color space and color channel selection on image segmentation of food images. Jurnal Ilmiah Teknologi Sistem Informasi, 6(2), 141–151.

Mendoza, F., Dejmek, P., & Aguilera, J. M. (2006). Calibrated color measurements of agricultural foods using image analysis. Postharvest Biology and Technology, 41(3), 285–295.

Mezgec, S., Eftimov, T., Bucher, T., & Seljak, B. K. (2019). Mixed deep learning and natural language processing method for fake-food image recognition and standardization to help automated dietary assessment. Public health nutrition, 22(7), 1193–1202.

Patil, N. K., Yadahalli, R. M., & Pujari, J. (2011). Comparison between HSV and YCbCr color model color-texture based classification of the food grains. International Journal of Computer Applications, 34(4), 51–57.

Sari, Y. A., Adinugroho, S., Adikara, P. P., & Izzah, A. (2017). Multiplication of V and Cb color channel using Otsu thresholding for tomato maturity clustering. International Conference on Sustainable Information Engineering and Technology (SIET), 209–214.

Sari, Y. A. & Adinugroho, S. (2017). Tomato ripeness clustering using 6-means algorithm based on v-channel otsu segmentation. 5th International Symposium on Computational and Business Intelligence (ISCBI), 32–36.

Sari, Y. A. & Adinugroho, S. (2018). Preprocessing of tomato images captured by smartphone cameras using color correction and V-channel Otsu segmentation for tomato maturity clustering. 5th International Conference on Electrical and Electronic Engineering (ICEEE), 399–403.

Sari, Y. A., Maligan, J. M., Adinugroho, S., & Bihanda, Y. G. (2019). Multiple Food or Non-Food Detection in Single Tray Box Image using Fraction of Pixel Segmentation for Developing Smart Nutrition Box Prototype. International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol. X, no. X.

Sari, Y. A. et al. (2019). Indonesian Traditional Food Image Identification using Random Forest Classifier based on Color and Texture Features. International Conference on Sustainable Information Engineering and Technology (SIET), 206–211.

Sari, Y. A., Dewi, R. K., Maligan, J. M., Ananta, A. S., & Adinugroho, S. (2019). Automatic Food Leftover Estimation in Tray Box Using Image Segmentation. International Conference on Sustainable Information Engineering and Technology (SIET), 212–216.

Sari, Y. A., Dewi, R. K., Maligan, J. M., Maulana, L., & Adinugroho, S. (2020). Automatic Leftover Weight Prediction in Tray Box Using Improved Image Segmentation Color Lighting Component," Journal of Southwest Jiaotong University, 55(1).

Sari, Y. A., Adinugroho, S., Maligan, J. M., Rahman, M. A., & Bihanda, Y. G. (2020). Multi-Food Recognition In Single Tray Box Image With Scarcity Data Using Convolutional Neural Network. Innovations in Information and Communication Science and Technology, 2627, 63–69.

Singh, K. K., Pal, K., & Nigam, N. (2012). Shadow detection and removal from remote sensing images using NDI and morphological operators. International journal of computer applications, 42(10), 37–40.

Singh, S. & Patnaik, T. (2015). An efficient shadow removal method using HSV color space for video surveillance. International Conference on Advances in Computing, Communications and Informatics (ICACCI), 1454–1460.

Suzuki, T. (2017). Contour estimation of liquid food using temperature information. IEEE/SICE International Symposium on System Integration (SII), 912–917.

Tanuwijaya, L. K., Sembiring, L. G., Dini, C. Y., Arfiani, E. P., & Wani, Y. A. (2018). Leftover Food of Patients: Quantitative Analysis (Sisa Makanan Pasien Rawat Inap: Analisis Kualitatif). Indonesian Journal of Human Nutrition, 5(1), 51–61



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