آمار
| تعداد نشریات | 13 |
| تعداد شمارهها | 657 |
| تعداد مقالات | 6,849 |
| تعداد مشاهده مقاله | 9,974,442 |
| تعداد دریافت فایل اصل مقاله | 9,288,621 |
Comparing the Accuracy of Artificial Neural Networks in Estimating the Weight of Cobb, Ross, and Arbor Acres Chicks using Video Image Processing Technology | ||
| Poultry Science Journal | ||
| دوره 9، شماره 1، شهریور 2021، صفحه 19-26 اصل مقاله (490.82 K) | ||
| نوع مقاله: Original Paper | ||
| شناسه دیجیتال (DOI): 10.22069/psj.2021.18335.1625 | ||
| نویسندگان | ||
| M Khojastehkey1؛ SM Hashemi* 1؛ SA Hosseini2؛ R Dianat3 | ||
| 1Animal Science Research Department, Qom Agriculture and Natural Resources Research and Education Center, AREEO, Qom, Iran | ||
| 2Animal Science Research Institute of Iran, Agriculture Research, Education and Extension Organization (AREEO), Karaj, Iran | ||
| 3Department of Computer Engineering, University of Qom, Qom, Iran | ||
| چکیده | ||
| This study aimed to compare the accuracy of artificial neural networks (ANNs) in estimating the weight of broilers using video image processing technology. A total number of 900 broiler chicks from three different strains (Ross 308, Cobb 500, and Arbor Acres) were fed on commercial diets and reared under standard situations for 42 days. Thirty male and female chicks from each strain were weighed randomly using digital scales every day while simultaneously filmed from top view using a Xenon camera (2MP 1080IP lens). In image processing, digital images initially were extracted from films and then each image was processed using GUI of MATLAB software. Sixteen morphological features extracted from images that significantly correlated with the chicks' weight, were used as inputs of the artificial neural network, and multilayer perceptron ANN was trained to predict the weight of chickens of each strain via an error propagation algorithm. The procedure was the same for all three strains. The accuracy of ANN models to predict the weight of chicks were 98.4% (with an average error of 7.9 g), 99.54% (with an average error of 0.37 g), and 99.67% (with an average error of 2 g) for Ross, Cobb, and Arbor Acres strains, respectively. In conclusion, a comprehensive intelligent model can be designed based on artificial neural networks and video image processing technology to estimate the weight of broiler chickens regardless of their strain type. | ||
| کلیدواژهها | ||
| Growth rate؛ Broiler chick؛ Artificial intelligence | ||
| مراجع | ||
|
Amraei S, Abdanan Mehdizadeh S & Salari S. 2017. Broiler weight estimation based on machine vision and artificial neural network. British Poultry Science, 58: 200-205. DOI: 10.1080/ 00071668.2016.1259530
Arivazhagan S, Shebiah RN, Sudharsan H, Kannan RR & Ramesh R. 2013. External and internal defect detection of egg using machine vision. Journal of Emerging Trends in Computing and Information Sciences, 4: 257-262.
Bhuvaneshwari MM & Scholar P. 2015. Improvement in detection of chicken egg fertility using image processing techniques. International Journal on Engineering Technology and Sciences, 2: 64-67.
Buzala M & Janicki B. 2016. Effects of different growth rates in broiler breeder and layer hens on some productive traits. Poultry Science, 95: 2151-2159. DOI: 10.3382/ps/pew173
Chmiel M, Słowiński M & Dasiewicz K. 2011. Application of computer vision systems for estimation of fat content in poultry meat. Food Control, 22:1424-1427. DOI: 10.1016/j. foodcont. 2011.03.002
De Wet L, Vranken E, Chedad A, Aerts JM, Ceunen J & Berckmans D. 2003. Computer-assisted image
analysis to quantify daily growth rates of broiler chickens. British Poultry Science, 44: 524-532. DOI: 10.1080/00071660310001616192
Durosaro S, Oyetade M, Ilori B, Adenaike A, Olowofeso O, Wheto M, Amusan S, Osho S & Ozoje M. 2013. Estimation of body weight of Nigerian local turkeys from zoometrical measurements at 48 and 12 weeks of age. Global Journal of Science Frontier Research, 13: 1-4
Fernandes JIM, Bortoluzzi C, Triques GE, Garcez Neto AF & Peiter DC. 2013. Effect of strain sex and age on carcass parameters of broilers. Acta Scientiarum. Animal Sciences, 35:99-105. DOI: 10.4025/actascianimsci.v35i1.13354.
Gonzalez, R., and Woods, R. E.2002. Digital Image Processing. 2nd eddition. Addison-Wesley.ISBN-13: 978-0201180756
Hashemi SM. 2013. Growth performance and intestinal morphology of broilers fed low protein and low methionine diets supplemented with putrescine. PhD thesis. University Putra Malaysia. Putra, Malysia. 210 Pages.
Jawasreh K, Al Athamneh S, Al-Zghoul MB, Al Amareen A, AlSukhni I & Aad P. 2019. Evaluation of growth performance and muscle marker genes expression in four different broiler strains in Jordan. Italian Journal of Animal Science, 18: 766-776. DOI: 10.1080/ 1828051X. 2019.1573647
Khojastehkey M, Aslaminejad AA, Shariati MM & Dianat R. 2015. Pelt Pattern Classification of New Born Lambs Using Image Processing and Artificial Neural Network. Global Journal of Animal Scientific Research, 3: 321-328.
MATLAB. 2015. MATLAB and Statistics Toolbox Release. The MathWorks, Inc., Natick, Massachusetts, United States.
Mollah MBR, Hasan MA, Salam MA & Ali MA. 2010. Digital image analysis to estimate the live weight of broiler. Computers and Electronics in Agriculture, 72:48-52. DOI: 10.1016/j.compag. 2010.02.002
Negretti P, Bianconi G, Bartocci S & Terramoccia S. 2007. Lateral Trunk Surface as a new parameter to estimate live body weight by Visual Image Analysis. Italian Journal of Animal Science, 6: 1223-1225. DOI: 10.4081/ijas.2007.s2.1223 Nogueira B, Reis M, Carvalho A, Mendoza E, Oliveira B, Silva V & Bertechini A. 2019. Performance Growth Curves and Carcass Yield of Four Strains of Broiler Chicken. Brazilian Journal of Poultry Science, 21:1-8. DOI: 10.1590/1806-9061-2018-0866
Souza CDF, Mogami CA, Tinôco IDFF, Pinto FDAC, Inoue KRA & Savastano Júnior H. 2013. Methodology for determination of body mass gain of broilers in commercial aviaries via digital image analysis. American Society of Agricultural and Biological Engineers, annual international meeting, Kansas city, Missouri, USA. DOI: 10.13031/aim.20131620477
Yanagi Júnior T, Silva E, Braga Júnior RA, Lopes MA, Damasceno FA & Silva GCDA. 2011. Digital surface area assessment of broiler chickens. Engenharia Agrícola, 31: 468-476. DOI: 10.1590/S0100-69162011000300007 | ||
|
آمار تعداد مشاهده مقاله: 1,323 تعداد دریافت فایل اصل مقاله: 1,181 |
||
سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب