Intelligent Weighting and Marking System in Poultry, Based on Machine Vision

Document Type : Research Paper


1 Assistant Professor, Department of Electrical and Computer Engineering, School of Shariaty, Technical University of Tehran Province, Iran

2 Electronics Technology Engineer, Department of Electrical and Computer Engineering, School of Shariaty, Technical University of Tehran Province, Iran

3 phd student amir kabir university


The goal of this investigation is weighting and selecting chickens in a determined weight by using machine vision. The images were taken by Raspberrypi cameras and then processed using the Raspberrypi 3 board, and finally the weight was estimated. The marking system was also used to mark chickens in the ideal weight range. For this purpose, 300 Rass chickens were examined in 3 poultry farms by a portable system and from about 500 pictures, 4 features were extracted: length, width, area and circumference. Chickens weight calculated with mixing features (length, width, area) that had been gained. Average percentage error of each one was also measured separately. Average percentage error in this system was 6% (minimum error percentage 2% and maximum error percentage 12%) that indicates the ability of image processing to determine the weight of the chicken. For check system accuracy, one place similar to poultry farms has designed with the same light, temperature, food and water for chickens, and they had access freely, they have been checked for 4 days, then marked chickens were putted up on a digital scale. Finally, the results showed the complete accuracy of the system and the accuracy of the image processing and weighting process


Main Subjects

Z.H.Pradana, B.Hidayat, S.Darana , beef cattle weight determine by using digital image processing(2016), in: international Conference on Control, Electronics, Renewable Energy and Communication.
D.D.Burdescu,L.Stanescu,M.Brezovan,C.S.Spahiu,D.C.Ebanca(2016),A Method for Image Processing from Planar Color Images, In:IEEE-3rd International Conference on Control, Decision and Information Technologies, April 6-8, 2016,Malta
V. Pereira, V.A.Fernandes, J.Sequeira(2014), Low Cost Object Sorting Robotic Arm using Raspberry Pi, In: 2014 IEEE GlobalHumanitation Technology, South Asia Satellite(GHTC-SAS),Sep 26-27,2014.
S.Amraee, S.A.Mehdizadeh, S.Salari(2017), The system for estimating the weight of broiler chicks individually using image processing and multiple regression analysis, Journal of Biotechnology Engineering, 47(4),615-623.(In Farsi)
M.Rajaee,M.Larimonfared(2012) Recognize eaten apple drop from healthy using Image Processing Techniques using MATLAB Software, In: The 15th Iranian Student Conference, August 28-30,2012, Kashan University, Isfahan, Iran.
S.Viazzi, S.VanHoestenberghe, B. M.Goddeeris, &D. Berckmans, (2015) Automatic mass estimation of Jade perch Scortumbarcoo by computer vision. Aquacultural Engineering, 64, 42-48.
Tillet, R.D., Onyango, C.M. &Marchant, J.A. (1997) Using model-based-image processing to track animal movements. Computers and Electronics in Agriculture, 17, 249-261
Schofield, C.P., Marchant, J.A., White, R.P., Brandl, N., & Wilson, M. (1999). Monitoring pig growth using a prototype imaging system. Journal of Agricultural Engineering Research, 72, 205–210.
H.Bipembi, J. B. Hayfron-Acquah, Joseph K. Panford, Obed Appiah(2016),Calculation of Body Mass Index using Image Processing Techniques,International Journal of Artificial Intelligence and Mechatronics ,4(1),ISSN 2320 – 5121.
M.Jafarlo, R.F.Teimorlo(2014),Estimation of apple volume and indentations, using image processing and neural network, Journal of Agricultural Machinery,4(1),57-64(In Farsi).
M.Kashiha, C.Bahr, S.Ott, C. P.Moons, T. A. Niewold, F. O.Ödberg, & D. Berckmans, (2014). Automatic weight estimation of individual pigs using image analysis. Computers and Electronics in Agriculture, 107, 38-44.
S.Tasdemir,A.Urkmez, &S.Inal(2011). Determination of body measurements on the Holstein cows using digital image analysis and estimation of live weight with regression analysis. Computers and electronics in agriculture, 76(2), 189-197.
T. F.Cootes, C. J. Taylor,D. H. Cooper, & J. Graham(1995). Active shape models-their training and application. Computer vision and image understanding, 61(1), 38-59.
Y.Wang, W.Yang, P.Winter, & L. Walker (2008) Walk-through weighing of pigs using machine vision and an artificial neural network. Biosystems Engineering, 100(1), 117-125.
M.Saadat (2016), Chicken meat demand function in Iran. Economic Jurnal,11&12, 101_107(In Farsi).