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Lee DS, Kwon SK. Amount Estimation Method for Food Intake Based on Color and Depth Images through Deep Learning. SENSORS (BASEL, SWITZERLAND) 2024; 24:2044. [PMID: 38610258 PMCID: PMC11014128 DOI: 10.3390/s24072044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 03/19/2024] [Accepted: 03/20/2024] [Indexed: 04/14/2024]
Abstract
In this paper, we propose an amount estimation method for food intake based on both color and depth images. Two pairs of color and depth images are captured pre- and post-meals. The pre- and post-meal color images are employed to detect food types and food existence regions using Mask R-CNN. The post-meal color image is spatially transformed to match the food region locations between the pre- and post-meal color images. The same transformation is also performed on the post-meal depth image. The pixel values of the post-meal depth image are compensated to reflect 3D position changes caused by the image transformation. In both the pre- and post-meal depth images, a space volume for each food region is calculated by dividing the space between the food surfaces and the camera into multiple tetrahedra. The food intake amounts are estimated as the difference in space volumes calculated from the pre- and post-meal depth images. From the simulation results, we verify that the proposed method estimates the food intake amount with an error of up to 2.2%.
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Affiliation(s)
- Dong-seok Lee
- AI Grand ICT Center, Dong-Eui University, Busan 47340, Republic of Korea;
| | - Soon-kak Kwon
- Department of Computer Software Engineering, Dong-Eui University, Busan 47340, Republic of Korea
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2
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Liu C, Wang Q, Ma M, Zhu Z, Lin W, Liu S, Fan W. Single-View Measurement Method for Egg Size Based on Small-Batch Images. Foods 2023; 12:foods12050936. [PMID: 36900453 PMCID: PMC10000608 DOI: 10.3390/foods12050936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 02/07/2023] [Accepted: 02/08/2023] [Indexed: 02/25/2023] Open
Abstract
Egg size is a crucial indicator for consumer evaluation and quality grading. The main goal of this study is to measure eggs' major and minor axes based on deep learning and single-view metrology. In this paper, we designed an egg-carrying component to obtain the actual outline of eggs. The Segformer algorithm was used to segment egg images in small batches. This study proposes a single-view measurement method suitable for eggs. Experimental results verified that the Segformer could obtain high segmentation accuracy for egg images in small batches. The mean intersection over union of the segmentation model was 96.15%, and the mean pixel accuracy was 97.17%. The R-squared was 0.969 (for the long axis) and 0.926 (for the short axis), obtained through the egg single-view measurement method proposed in this paper.
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Affiliation(s)
- Chengkang Liu
- College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
| | - Qiaohua Wang
- College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
- Ministry of Agriculture Key Laboratory of Agricultural Equipment in the Middle and Lower Reaches of the Yangtze River, Wuhan 430070, China
- National Research and Development Center for Egg Processing, Huazhong Agricultural University, Wuhan 430070, China
- Correspondence: ; Tel.: +86-1870-2768-307
| | - Meihu Ma
- College of Food Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Zhihui Zhu
- College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
| | - Weiguo Lin
- College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
| | - Shiwei Liu
- College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
| | - Wei Fan
- College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
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Siswantoro J, Asmawati E, Siswantoro MZ. A rapid and accurate computer vision system for measuring the volume of axi-symmetric natural products based on cubic spline interpolation. J FOOD ENG 2022. [DOI: 10.1016/j.jfoodeng.2022.111139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
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Meenu M, Kurade C, Neelapu BC, Kalra S, Ramaswamy HS, Yu Y. A concise review on food quality assessment using digital image processing. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2021.09.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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Nyalala I, Okinda C, Makange N, Korohou T, Chao Q, Nyalala L, Jiayu Z, Yi Z, Yousaf K, Chao L, Kunjie C. On-line weight estimation of broiler carcass and cuts by a computer vision system. Poult Sci 2021; 100:101474. [PMID: 34742122 PMCID: PMC8577095 DOI: 10.1016/j.psj.2021.101474] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 08/16/2021] [Accepted: 08/26/2021] [Indexed: 11/28/2022] Open
Abstract
In a broiler carcass production conveyor system, inspection, monitoring, and grading carcass and cuts based on computer vision techniques are challenging due to cuts segmentation and ambient light conditions issues. This study presents a depth image-based broiler carcass weight prediction system. An Active Shape Model was developed to segment the carcass into 4 cuts (drumsticks, breasts, wings, and head and neck). Five regression models were developed based on the image features for each weight estimation (carcass and its cuts). The Bayesian-ANN model outperformed all other regression models at 0.9981 R2 and 0.9847 R2 in the whole carcass and head and neck weight estimation. The RBF-SVR model surpassed all the other drumstick, breast, and wings weight prediction models at 0.9129 R2, 0.9352 R2, and 0.9896 R2, respectively. This proposed technique can be applied as a nondestructive, nonintrusive, and accurate on-line broiler carcass production system in the automation of chicken carcass and cuts weight estimation.
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Affiliation(s)
- Innocent Nyalala
- College of Engineering, Nanjing Agricultural University, Jiangsu 210031, PR China
| | - Cedric Okinda
- College of Engineering, Nanjing Agricultural University, Jiangsu 210031, PR China
| | - Nelson Makange
- College of Engineering, Nanjing Agricultural University, Jiangsu 210031, PR China
| | - Tchalla Korohou
- College of Engineering, Nanjing Agricultural University, Jiangsu 210031, PR China
| | - Qi Chao
- College of Engineering, Nanjing Agricultural University, Jiangsu 210031, PR China
| | - Luke Nyalala
- Department of Computer Science, Cornell University, Ithaca, NY 14853-7501, USA
| | - Zhang Jiayu
- College of Engineering, Nanjing Agricultural University, Jiangsu 210031, PR China
| | - Zuo Yi
- College of Engineering, Nanjing Agricultural University, Jiangsu 210031, PR China
| | - Khurram Yousaf
- College of Engineering, Nanjing Agricultural University, Jiangsu 210031, PR China
| | - Liu Chao
- College of Engineering, Nanjing Agricultural University, Jiangsu 210031, PR China
| | - Chen Kunjie
- College of Engineering, Nanjing Agricultural University, Jiangsu 210031, PR China.
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Nyalala I, Okinda C, Chao Q, Mecha P, Korohou T, Yi Z, Nyalala S, Jiayu Z, Chao L, Kunjie C. Weight and volume estimation of single and occluded tomatoes using machine vision. INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2021. [DOI: 10.1080/10942912.2021.1933024] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Innocent Nyalala
- Department of Agricultural Machinery, College of Engineering, Nanjing Agricultural University, Jiangsu, P.R. China
| | - Cedric Okinda
- Department of Agricultural Machinery, College of Engineering, Nanjing Agricultural University, Jiangsu, P.R. China
| | - Qi Chao
- Department of Agricultural Machinery, College of Engineering, Nanjing Agricultural University, Jiangsu, P.R. China
| | - Peter Mecha
- Department of Agricultural Machinery, College of Engineering, Nanjing Agricultural University, Jiangsu, P.R. China
| | - Tchalla Korohou
- Department of Agricultural Machinery, College of Engineering, Nanjing Agricultural University, Jiangsu, P.R. China
| | - Zuo Yi
- Department of Agricultural Machinery, College of Engineering, Nanjing Agricultural University, Jiangsu, P.R. China
| | - Samuel Nyalala
- Faculty of Agriculture, Department of Crops, Horticulture and Soil Sciences, Egerton University, Njoro, Kenya
| | - Zhang Jiayu
- Department of Agricultural Machinery, College of Engineering, Nanjing Agricultural University, Jiangsu, P.R. China
| | - Liu Chao
- Department of Agricultural Machinery, College of Engineering, Nanjing Agricultural University, Jiangsu, P.R. China
| | - Chen Kunjie
- Department of Agricultural Machinery, College of Engineering, Nanjing Agricultural University, Jiangsu, P.R. China
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Poonnoy P, Asavasanti S. Implementation of coupled pattern recognition and regression artificial neural networks for mass estimation of headless‐shell‐on shrimp with random postures. J FOOD PROCESS ENG 2021. [DOI: 10.1111/jfpe.13747] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Poonpat Poonnoy
- Faculty of Engineering and Agro‐Industry Maejo University Chiangmai Thailand
| | - Suvaluk Asavasanti
- Food Technology and Engineering Laboratory Pilot Plant Development and Training Institute, King Mongkut's University of Technology Thonburi Bangkok Thailand
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Weight and volume estimation of poultry and products based on computer vision systems: a review. Poult Sci 2021; 100:101072. [PMID: 33752071 PMCID: PMC8010860 DOI: 10.1016/j.psj.2021.101072] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 01/14/2021] [Accepted: 02/04/2021] [Indexed: 01/10/2023] Open
Abstract
The appearance, size, and weight of poultry meat and eggs are essential for production economics and vital in the poultry sector. These external characteristics influence their market price and consumers' preference and choice. With technological developments, there is an increase in the application and importance of vision systems in the agricultural sector. Computer vision has become a promising tool in the real-time automation of poultry weighing and processing systems. Owing to its noninvasive and nonintrusive nature and its capacity to present a wide range of information, computer vision systems can be applied in the size, mass, volume determination, and sorting and grading of poultry products. This review article gives a detailed summary of the current advances in measuring poultry products' external characteristics based on computer vision systems. An overview of computer vision systems is discussed and summarized. A comprehensive presentation of the application of computer vision-based systems for assessing poultry meat and eggs was provided, that is, weight and volume estimation, sorting, and classification. Finally, the challenges and potential future trends in size, weight, and volume estimation of poultry products are reported.
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Meng Y, Yu S, Qiu Z, Zhang J, Wu J, Yao T, Qin J. Modeling and optimization of sugarcane juice clarification process. J FOOD ENG 2021. [DOI: 10.1016/j.jfoodeng.2020.110223] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Non-Destructive Identification and Estimation of Granulation in Honey Pomelo Using Visible and Near-Infrared Transmittance Spectroscopy Combined with Machine Vision Technology. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10165399] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Granulation is a physiological disorder of juice sacs in citrus fruit, causing juice sacs to become hard and dry and resulting in decreased internal quality of citrus fruit. Honey pomelo is a thick-skinned citrus fruit, and it is difficult to identify the extent of granulation by observation of the outer peel and fruit shape. In this study, a rapid and non-destructive testing method using visible and near-infrared transmittance spectroscopy combined with machine vision technology was applied to identify and estimate granulation inside fruit. A total of 600 samples in different growth periods was harvested, and fruit were divided into five classes according to five granulation levels. Spectral data were obtained for two ranges of 400–1100 nm and 900–1700 nm by visible and near-infrared transmittance spectroscopy. In addition, chemometrics were used to measure the chemical changes of soluble solid content (SSC), titratable acidity (TA), and moisture content (MC) caused by different granulation levels. Machine vision technology can rapidly estimate the external characteristics of samples and measure the physical changes in mass and volume caused by different granulation levels. Compared with using a single or traditional methods, the predictive performances of multi-category classification models (PCA-SVM and PCA-GRNN) were significantly enhanced. In particular, the model accuracy rate (ARM) was 99% for PCA-GRNN, with classification accuracy (CA), classification sensitivity (CS), and classification specificity (CSP) of 0.9950, 0.9750, and 0.9934, respectively. The results showed that this method has great potential for the identification and estimation of granulation. Multi-source data fusion and application of a multi-category classification model with the smallest number of input layers and acceptable high predictive performances are proposed for on-line applications. This method can be effectively used on-line for the non-destructive detection of fruits with granulation.
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