Evaluation of the efficiency of artificial intelligence in estimating the body dimensions of Sistani cows

Document Type : Research Paper

Authors

sistan agriculture and natural resources research and education center

Abstract

This research was conducted to investigate the possibility of using visual machine technology in measuring the body dimensions of Sistani cows. For this purpose, the record of body dimensions including length, shoulder height, hip height and chest circumference of 179 heads of livestock in Zahak Sistani cow breeding station, were measured at different time points, using a tape meter. At recording time, digital images were taken using the canon camera from the lateral view of cattle from distance of 2 meters. Digital image processing and feature extraction were performed using Graphical Unit Interference of MATLAB software. The feature of digital images as input and different body dimensions of cows as output of Artificial Neural Network(ANN) were used in the training and modeling phase. The results showed that, out of 22 features extracted from the images of Sistani cows, the 15 effective features, such as Equivalent Diameter, Major Axis Length, Minor Axis Length, Bounding Box, Convex Area, Filled Area, Area, Perimeter, and the number of white pixels of image (NNZ) had a significant correlation with the body dimensions of the Sistani cow(p<0.01). Body dimensions of Sistani cows including body length, shoulder height, hip height, and chest girths were estimated with accuracy of 0.98, 0.97, 0.97and 0.98%, by the ANNs model, respectively. The results of the present research showed that Artificial Intelligence Technology can act as a suitable alternative to biometric evaluation of Sistani cows and save time and relevant costs.

Keywords

References

بزی، ح.، راشکی، م.، نقض علی، ا. و کیخاسالار، ع. (1385). مقدمه‌ای بر شناسائی و وضعیت گاو سیستانی در منطقه سیستان-نشریه ترویجی- شماره ثبت ۸۵/۱۲۳۸. مرکز تحقیقات کشاورزی و منابع طبیعی سیستان.
خجسته کی، م.، عباسی، م.ع.، آکبری شریف، ع. و حسنی، ا. م.)1395(. تخمین وزن بره های نوزاد با استفاده از پردازش تصاویر دیجیتال. نشریه علوم دامی(پژوهش و سازندگی). دوره29، شماره 112، ص ص. 99-104.
سرگلزهی، ا. ر. (1377). بررسی اقتصادی پرواربندی توده گاو سیستانی در دو شیوه سنتی و صنعتی در سطح سیستان -پایان‌نامه کارشناسی ارشد- دانشکده اقتصاد دانشگاه علامه طباطبایی- تهران- ایران.
منهاج، م . ب.(1391).،هوش محاسباتی(جلد اول) مبانی شبکه‌های عصبی. چاپ اول. مرکز نشر دانشگاه صنعتی امیر کبیر.
 
Bewley. J.M., Peacock. A.M., Lewis. O., Boyce. R.E., Roberts. D.J., Coffey. M.P. et al. (2008). Potential for estimation of body condition scores in dairy cattle from digital images. Journal of Dairy Science. 91: 3439-3453.
Cannas. A. and Boe. F. (2003). Prediction of the relationship between body weight and body condition score in sheep. Italian Journal of Animal Sciences. 2:527-529.
Fioretti. M., Negrini. R. and Biondi. A. (2012).A new tool for beef performance recording in Italy.http://www.icar.org/cork_2012/Manuscripts/Published/Fioretti.pdf.
Forbes. K.(2000). Volume Estimation of Fruit from Digital Profile Images. A dissertation submitted to the Department of Electrical Engineering, University of Cape Town, in fulfillment of the requirements for the degree of Master of Science in Engineering.
Gonzalez. R. and Woods. R. E.(2002). Digital Image Processing.2nd edition. Addison-Wesley.
Hao. M., Yu. H. and Li. D.(2016). The Measurement of Fish Size by Machine Vision-A Review.IFIP International Federation for Information Processing. IFIP AICT 479(15–32), DOI: 10.1007/978-3-319-48354-2_2
Khojastehkey. M., Aslaminejad. A.A., shariati. M.M. and Dianat. R.(2015).Body size estimation of new born lambs using image processing and its effect on the genetic gain of a simulated population .Journal of Applied Animal Research. DOI: 10.1080/09712119.2015.1031789.
Negretti. P., Bianconi,G. and Finzi, A.(2007).Visual image analysis to estimate the morphological and weight measurement in Rabbits. World Rabbit Science.15:37– 41.
Onder. H., Arl. A., Ocak. S., Eker. S. and Tufekci. H. (2011) .Use of Image Analysis in Animal Science. Journal of Information Technology in Agriculture. 1:1-4.
Ozkaya.S.(2012). The prediction of live weight from body measurements on female Holstein calves by digital image analysis. Journal of agricultural research. 151(4):570-576.
Petersen. M.E., de Ridder. D. and Handels. H. (2002). Image processing with neural networks: a review. Pattern Recognition. 35: 2279–2301.
Salau. J., Haas. J., Junge. W., Bauer. U., Harms. J. and Bieletzki. S. (2014). Feasibility of automated body trait determination using the SR4K time-of-flight camera in cow barns.Springer Plus. 3, 225.
Shelley. A. N. (2016). Incorporating machine vision in precision dairy farming technologies. A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the College of Engineering at the University of Kentucky.
Tasdemir.S., Urkmez. A. and Inal. S. (2011). A fuzzy rule-based system for predicting the live weight of Holstein cows whose body dimensions were determined by image analysis. Turkish Journal of Electronic Engineering and Computer Science. 19(4):689-703.
Tsegaye. D., Belay. B. and Haile. A.(2013). Linear body measurement as predictor of body weight in Haraghe Highland goat under farmers environment Ethiopia. Glob.Veterinaria.11(5):649-656.
Verma. D., Sankhyan.V., Katoch. S. and Thakur.Y.P.(2015). Principal Component analysis of biometric traits to reveal body conformation in local hill cattle of Himalayan state of Himalapredesh . India Veterinaria World.8 (12):1453-1470.
Animal Sciences Journal
Volume 35, Issue 135
September 2022
Pages 15-26

Files

Share

How to cite

Statistics