Abstract
The objective of this study was to process digital images to investigate the possibility of broilers body weight estimation based on the dynamic model. For this experiment, 2440 images were recorded by a top-view camera from 30 birds. An ellipse fitting algorithm was applied to localize chickens within the pen, by using generalized Hough transform. Chickens’ head and tail were removed efficiently using the Chan-Vese method. After that, using image processing, six body measures were calculated. Next, they were used to design a Transform Function (TF) model with weight measurements as output. Second-order dynamic models were used to predict the weight of life broiler chicken, without delay, stable and with the highest R2 were predominantly selected according to the Young Identification Criterion (YIC) criterion chosen models. It was observed that predicted values rigorously follow the real values. Moreover, the relative body weight errors of chickens in the early days of grow-out was much more than last days. The accuracy of TF for body weight prediction from a comparison between measured (absolute) and predicted total life body weights were estimated for all studied broiler chicken (R2=0.98).
Highlights
Many scientific researchers have already practiced the technology of monitoring broilers by camera and image processing since it is non-intrusive and costeffective technology which can work automatically in real-time
Schofield (1990) has indicated that removing the head and neck from the top view of the area of the pig resulted in the strongest correlation with body weight
Six body measures were calculated by the image processing
Summary
Many scientific researchers have already practiced the technology of monitoring broilers by camera and image processing since it is non-intrusive and costeffective technology which can work automatically in real-time. Amongst the different features that can be monitored via image analysis, weight is of great importance. Information about body weight offers possibilities in automatic control of the flock, and help supporting farmers in relation to complex biological production processes (e.g., feeding strategies, growth rate control, activity control as proposed by Morag et al (2001), Halachmi et al (2002), and Aerts et al (2003a, b). The standard method to estimate the animal’s weight is laborious, costly, time-consuming, and require direct contact with the animal’s body which is stressful for both animals and farmers. Direct contact may even cause injury to animal
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