Year 2019,
Volume: 4 Issue: 1, 36 - 44, 01.02.2019
Sakir Tasdemir
,
İlker Ali Ozkan
References
- Abdel-Aziz, Y.I. ve Karara, H.M. (1971). Direct Linear Transformation from Comparator Coordinates into Object Space Coordinates in Close-Range Photogrammetry, Proceedings of the Symposium on Close-Range Photogrammetry, Urbana, Illinois, 1-8.
- Adamczyk, K., Molenda, K., Szarek, J., & Skrzyński, G. (2005). Prediction of Bulls’slaughter Value From Growth Data Using Artificial Neural Network. Journal of Central European Agriculture, 6(2), 133-142.
- Aguilar, M. A., Aguilar, F. J., Agüera, F., & Carvajal, F. (2005). The evaluation of close-range photogrammetry for the modelling of mouldboard plough surfaces. Biosystems Engineering, 90(4), 397-407.
- Akçay, Ö., Erenoğlu, R , Avşar, E . (2017). The Effect of Jpeg Compression in Close Range Photogrammetry. International Journal of Engineering and Geosciences, 2 (1), 35-40. DOI: 10.26833/ijeg.287308
- Akilli, A., & Atil, H. (2014). Artificial Intelligence Technologies in Dairy Science: Fuzzy Logic and Artificial Neural Network. Hayvansal Üretim, 55(1), 39- 45.
- Akkol, S., Akilli, A., & Cemal, İ. (2017). Comparison of Artificial Neural Network and Multiple Linear Regression for Prediction of Live Weight in Hair Goats.YYU Yüzüncü Yıl Üniversitesi Journal of Agricultural Sciences, 27(1), 21-29.
- Ali, M., Eyduran, E., Tariq, M. M., Tirink, C., Abbas, F., Bajwa, M. A., ... & Shah, S. H. (2015). Comparison of artificial neural network and decision tree algorithms used for predicting live weight at post weaning period from some biometrical characteristics in Harnai sheep. Pakistan J. Zool., vol. 47(6), pp. 1579-1585.
- Atkinson, K.B. (1996). Close Range Photogrammetry and Machine Vision, Whittles Publishing, Scotland, 1996.
- Altan, Ö., Gölcü, M. (2009). Daily means ambient temperature prediction using artificial neural network method: A case study of Turkey. Renewable Energy 34, 1158-1161.
- Cohen, J., Cohen, P., West, S.G., Aiken, L.S. (2013). Applied multiple regression/correlation analysis for the behavioral sciences. Routledge.
- Cooper, M. A. R. and Robson, (1996). S.Theory of Close Range Photogrammetry, Close Range Photogrammetry and Machine Vision, 9-51.
- Doğan, Y., Yakar, M. (2018). GIS and three-dimensional modeling for cultural heritages. International Journal of Engineering and Geosciences, 3 (2), 50-55.
- Fang-Jenq, C. (1997). Application of Least-Squares Adjustment Technique to Geometric Camera Calibration and Photogrammetric Flow Visualization, ISA 43rd International Instrumentation Symposium, Orlando, Florida.
- Goktepe, A. (2010). A study on the calibration of stereo photogrammetric systems used in motion analysis. Scientific Research and Essays, 5(17), 2491- 2496.
- Gurney, K. (2014). An introduction to neural networks. CRC press.
- Hagan, M.T., Demuth, H.B., Beale, M.H. (1996). Neural network design. Pws Pub. Boston.
- Hasni, A., Sehli, A., Draoui, B., Bassou, A., Amieur, B. (2012). Estimating Global Solar Radiation Using Artificial Neural Network and Climate Data in the South- western Region of Algeria. Energy Procedia 18, 531-537.
- Idris, N.H., Salim, N.A., Othman, M.M., Yasin, Z.M. (2017). Prediction of Cascading Collapse Occurrence due to the Effect of Hidden Failure of a Protection System using Artificial Neural Network. Journal of Electrical Systems 13.
- Jain, A.K., Jianchang, M., Mohiuddin, K.M. (1996). Artificial neural networks: a tutorial. Computer 29, 31-44.
- Karsli, E., Ayhan, E., Tunc, E. (2003). Genelleştirilmiş DLT metodu ile Dijital Kamera Geometrik Kalibrasyonu, TMMOB Harita ve Kadastro Mühendisleri Odası 9. Türkiye Harita Bilimsel ve Teknik Kurultayı, Ankara.
- Maind, S.B., Wankar, P. (2014). Research paper on basic of artificial neural network. International Journal on Recent and Innovation Trends in Computing and Communication 2, 96-100.
- Mortensen, A. K., Lisouski, P., & Ahrendt, P. (2016). Weight prediction of broiler chickens using 3D computer vision. Computers and Electronics in Agriculture, 123, 319-326.
- Raja, T. V., Ruhil, A. P., & Gandhi, R. S. (2012). Comparison of connectionist and multiple regression approaches for prediction of body weight of goats. Neural Computing and Applications, 21(1), 119-124.
- Salawu, E. O., Abdulraheem, M., Shoyombo, A., Adepeju, A., Davies, S., Akinsola, O., & Nwagu, B. 2014. Using artificial neural network to predict body weights of rabbits. Open Journal of Animal Sciences, 4(04), 182.
- Saritas, I., Ozkan, I. A., & Sert, I. U. (2010). Prognosis of prostate cancer by artificial neural networks. Expert Systems with Applications, 37(9), 6646-6650.
- Szyndler-Nędza, M., Eckert, R., Blicharski, T., Tyra, M., & Prokowski, A. (2016). Prediction of carcass meat percentage in young pigs using linear regression models and artificial neural networks. Annals of Animal Science, 16(1), 275-286.
- Tasdemir, S., Yakar, M., Urkmez, A., & Inal, S. (2008, June). Determination of body measurements of a cow by image analysis. In Proceedings of the 9th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing (p. 70). ACM.
- Tasdemir, S., Urkmez, A., Yakar, M. ve Inal, S, 13-15 May 2009. Determination of camera calibration parameters at digital image analysis. IATS’09.
- Tasdemir, S. (2010). Determination of Body Measurements On the Holstein Cows by Digital Image Analysis Method and Estimation of Their Live Weight. Ph. D. thesis, Selcuk University, Konya, Turkey.
- Tudes, T., Yer Fotogrametrisi, (1986). KTÜ Basımevi, Mühendislik Fakültesi yayını, No: 105, Trabzon, 1996.
- Tumer, A. E., & Edebali, S. (2015). An Artificial Neural Network Model for Wastewater Treatment Plant of Konya. International Journal of Intelligent Systems and Applications in Engineering, 3(4), 131-135.
- Wang, Y., Yang, W., Winter, P., & Walker, L. (2008). Walk-through weighing of pigs using machine vision and an artificial neural network. Biosystems Engineering, 100(1), 117-125.
- Wu, J., Tillett, R., McFarlane, N., Ju, X., Siebert, J. P., & Schofield, P. (2004). Extracting the three-dimensional shape of live pigs using stereo photogrammetry. Computers and Electronics in Agriculture, 44(3), 203-222.
- Wongsriworaphon, A., Arnonkijpanich, B., & Pathumnakul, S. (2015). An approach based on digital image analysis to estimate the live weights of pigs in farm environments. Computers and Electronics in Agriculture, 115, 26-33.
- Yilmaz, H. M., Yakar, M., Yildiz, F. 2008. Digital Photogrammetry in Obtaining of 3D Model Data of Irregular Small Surfaces Objects. ISPRS Congress, Beijing, 125-130.
- Yilmaz, O., Cemal, I., & Karaca, O. (2013). Estimation of mature live weight using some body measurements in Karya sheep. Tropica.
ANN approach for estimation of cow weight depending on photogrammetric body dimensions
Year 2019,
Volume: 4 Issue: 1, 36 - 44, 01.02.2019
Sakir Tasdemir
,
İlker Ali Ozkan
Abstract
Computer technology and software are widely used in every multi-discipline field. Geomatics engineering can be seen as a pioneer of these disciplines especially in photogrammetry and image processing. Photogrammetry is a method where geometric parameters of objects on digitally captured images are determined and make measurements on them. Capturing the digital images and photogrammetric processing include several fully defined stages, which allows to generate three-dimension or two-dimension digital models of the body as an end product. The aim of this study is to predict Holstein cows’ live weight via artificial neural network whose body dimensions were determined with photogrammetry method. The body dimensions to be used in this study are obtained metric from analysis of cows’ images captured by synchronized three-dimension camera environment from different aspects. Wither height, hip height, body length, hip width of cows determined with photogrammetry. Artificial neural network prediction model was developed by using these body measurements. Dataset is divided into two after preprocessing as training and testing dataset. Different structured artificial neural network models are generated and the artificial neural network model which has the best performance is determined. Then with this artificial neural network model live weight of animals is estimated by using measurements obtained from images. After comparison of estimated live weights and weights obtained from scale, correlation coefficient is found (R=0.995). The statistical analysis shows that both groups are meaningful and artificial neural network can be used in live weight prediction safely.
References
- Abdel-Aziz, Y.I. ve Karara, H.M. (1971). Direct Linear Transformation from Comparator Coordinates into Object Space Coordinates in Close-Range Photogrammetry, Proceedings of the Symposium on Close-Range Photogrammetry, Urbana, Illinois, 1-8.
- Adamczyk, K., Molenda, K., Szarek, J., & Skrzyński, G. (2005). Prediction of Bulls’slaughter Value From Growth Data Using Artificial Neural Network. Journal of Central European Agriculture, 6(2), 133-142.
- Aguilar, M. A., Aguilar, F. J., Agüera, F., & Carvajal, F. (2005). The evaluation of close-range photogrammetry for the modelling of mouldboard plough surfaces. Biosystems Engineering, 90(4), 397-407.
- Akçay, Ö., Erenoğlu, R , Avşar, E . (2017). The Effect of Jpeg Compression in Close Range Photogrammetry. International Journal of Engineering and Geosciences, 2 (1), 35-40. DOI: 10.26833/ijeg.287308
- Akilli, A., & Atil, H. (2014). Artificial Intelligence Technologies in Dairy Science: Fuzzy Logic and Artificial Neural Network. Hayvansal Üretim, 55(1), 39- 45.
- Akkol, S., Akilli, A., & Cemal, İ. (2017). Comparison of Artificial Neural Network and Multiple Linear Regression for Prediction of Live Weight in Hair Goats.YYU Yüzüncü Yıl Üniversitesi Journal of Agricultural Sciences, 27(1), 21-29.
- Ali, M., Eyduran, E., Tariq, M. M., Tirink, C., Abbas, F., Bajwa, M. A., ... & Shah, S. H. (2015). Comparison of artificial neural network and decision tree algorithms used for predicting live weight at post weaning period from some biometrical characteristics in Harnai sheep. Pakistan J. Zool., vol. 47(6), pp. 1579-1585.
- Atkinson, K.B. (1996). Close Range Photogrammetry and Machine Vision, Whittles Publishing, Scotland, 1996.
- Altan, Ö., Gölcü, M. (2009). Daily means ambient temperature prediction using artificial neural network method: A case study of Turkey. Renewable Energy 34, 1158-1161.
- Cohen, J., Cohen, P., West, S.G., Aiken, L.S. (2013). Applied multiple regression/correlation analysis for the behavioral sciences. Routledge.
- Cooper, M. A. R. and Robson, (1996). S.Theory of Close Range Photogrammetry, Close Range Photogrammetry and Machine Vision, 9-51.
- Doğan, Y., Yakar, M. (2018). GIS and three-dimensional modeling for cultural heritages. International Journal of Engineering and Geosciences, 3 (2), 50-55.
- Fang-Jenq, C. (1997). Application of Least-Squares Adjustment Technique to Geometric Camera Calibration and Photogrammetric Flow Visualization, ISA 43rd International Instrumentation Symposium, Orlando, Florida.
- Goktepe, A. (2010). A study on the calibration of stereo photogrammetric systems used in motion analysis. Scientific Research and Essays, 5(17), 2491- 2496.
- Gurney, K. (2014). An introduction to neural networks. CRC press.
- Hagan, M.T., Demuth, H.B., Beale, M.H. (1996). Neural network design. Pws Pub. Boston.
- Hasni, A., Sehli, A., Draoui, B., Bassou, A., Amieur, B. (2012). Estimating Global Solar Radiation Using Artificial Neural Network and Climate Data in the South- western Region of Algeria. Energy Procedia 18, 531-537.
- Idris, N.H., Salim, N.A., Othman, M.M., Yasin, Z.M. (2017). Prediction of Cascading Collapse Occurrence due to the Effect of Hidden Failure of a Protection System using Artificial Neural Network. Journal of Electrical Systems 13.
- Jain, A.K., Jianchang, M., Mohiuddin, K.M. (1996). Artificial neural networks: a tutorial. Computer 29, 31-44.
- Karsli, E., Ayhan, E., Tunc, E. (2003). Genelleştirilmiş DLT metodu ile Dijital Kamera Geometrik Kalibrasyonu, TMMOB Harita ve Kadastro Mühendisleri Odası 9. Türkiye Harita Bilimsel ve Teknik Kurultayı, Ankara.
- Maind, S.B., Wankar, P. (2014). Research paper on basic of artificial neural network. International Journal on Recent and Innovation Trends in Computing and Communication 2, 96-100.
- Mortensen, A. K., Lisouski, P., & Ahrendt, P. (2016). Weight prediction of broiler chickens using 3D computer vision. Computers and Electronics in Agriculture, 123, 319-326.
- Raja, T. V., Ruhil, A. P., & Gandhi, R. S. (2012). Comparison of connectionist and multiple regression approaches for prediction of body weight of goats. Neural Computing and Applications, 21(1), 119-124.
- Salawu, E. O., Abdulraheem, M., Shoyombo, A., Adepeju, A., Davies, S., Akinsola, O., & Nwagu, B. 2014. Using artificial neural network to predict body weights of rabbits. Open Journal of Animal Sciences, 4(04), 182.
- Saritas, I., Ozkan, I. A., & Sert, I. U. (2010). Prognosis of prostate cancer by artificial neural networks. Expert Systems with Applications, 37(9), 6646-6650.
- Szyndler-Nędza, M., Eckert, R., Blicharski, T., Tyra, M., & Prokowski, A. (2016). Prediction of carcass meat percentage in young pigs using linear regression models and artificial neural networks. Annals of Animal Science, 16(1), 275-286.
- Tasdemir, S., Yakar, M., Urkmez, A., & Inal, S. (2008, June). Determination of body measurements of a cow by image analysis. In Proceedings of the 9th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing (p. 70). ACM.
- Tasdemir, S., Urkmez, A., Yakar, M. ve Inal, S, 13-15 May 2009. Determination of camera calibration parameters at digital image analysis. IATS’09.
- Tasdemir, S. (2010). Determination of Body Measurements On the Holstein Cows by Digital Image Analysis Method and Estimation of Their Live Weight. Ph. D. thesis, Selcuk University, Konya, Turkey.
- Tudes, T., Yer Fotogrametrisi, (1986). KTÜ Basımevi, Mühendislik Fakültesi yayını, No: 105, Trabzon, 1996.
- Tumer, A. E., & Edebali, S. (2015). An Artificial Neural Network Model for Wastewater Treatment Plant of Konya. International Journal of Intelligent Systems and Applications in Engineering, 3(4), 131-135.
- Wang, Y., Yang, W., Winter, P., & Walker, L. (2008). Walk-through weighing of pigs using machine vision and an artificial neural network. Biosystems Engineering, 100(1), 117-125.
- Wu, J., Tillett, R., McFarlane, N., Ju, X., Siebert, J. P., & Schofield, P. (2004). Extracting the three-dimensional shape of live pigs using stereo photogrammetry. Computers and Electronics in Agriculture, 44(3), 203-222.
- Wongsriworaphon, A., Arnonkijpanich, B., & Pathumnakul, S. (2015). An approach based on digital image analysis to estimate the live weights of pigs in farm environments. Computers and Electronics in Agriculture, 115, 26-33.
- Yilmaz, H. M., Yakar, M., Yildiz, F. 2008. Digital Photogrammetry in Obtaining of 3D Model Data of Irregular Small Surfaces Objects. ISPRS Congress, Beijing, 125-130.
- Yilmaz, O., Cemal, I., & Karaca, O. (2013). Estimation of mature live weight using some body measurements in Karya sheep. Tropica.