Walk-through weighing of pigs using machine vision and an artificial neural network

Egg volume prediction using machine vision technique based on pappus theorem and artificial neural network

“…ANNs construct a suitable relationship between input data and the target responses without any need for a theoretical model with which to work (Hua et al 2011). ANNs can be applied in almost every aspect of food processing, from raw material assessing (Wang et al 2008a;Pan et al 2009), thermal processing (Houessou et al 2008;Hernandez 2009;Omid et al 2011), fermentation (Wang et al 2008b), enzymatic hydrolysis, antioxidant activity and anthocyanin content (Taghadomi-saberi et al 2013), ultra-filtrating (Sun et al 2004) and drying (Poonnoy et al 2007;Omid et al 2009) to composition detecting (Afkhami et al 2009;Torrecilla et al 2008), quality-assessing (Dutta et al 2003;Sobel and Ballantine 2008;Pan et al 2011) and safety-evaluating (Gupta et al 2004;Panagou et al 2007). …”

Application of Artificial Neural Networks in Meat Production and Technology

“…These involve identification of animal species in ground meat mixtures (Winquist et al, 1993) or fat tissue (Beattie et al, 2007) LD -longissimus dorsi; TB -triceps brachii; R 2 -coefficient of determination; r -correlation coefficient; P -prediction; C -classification; VIS -visible; NIR -near infrared; IR -infrared. (Hwang et al, 1997), detection of RN -phenotype in pigs (Josell et al, 2000), the "walk-through" weighing of pigs (Wang et al, 2008), the efficiency of ANN for visual guidance of pig evisceration at the slaughter line (Christensen et al, 1996) and the use of ANN for the processing control of meat products (Eklöv et al, 1998;Ibarra et al, 2000;Santos et al, 2004). Again, in the majority of studies, ANN approach was an instrument to deal with the complex output signal of novel technologies applied.…”

Foreground detection of group-housed pigs based on the combination of Mixture of Gaussians using prediction mechanism and threshold segmentation

“…Among these, research on the accuracy of foreground detection of pigs is basic work for advancing subsequent research (Kashiha et al, 2013). Foreground detection of individual pigs is mentioned to varying degrees in some literature (Ahrendt, Gregersen, & Karstoft, 2011;Hu & Xin, 2000;Lind, Vinther, Hemmingsen, Hansen, 2005;McFarlane & Schofield, 1995;Navarro-Jover, et al, 2009;Wang, Yang, Winter, Walker, 2008). In that literature, however, foreground detection technology is not the focus; rather, it is used to address requirements of specific applications.…”

“…The key concept of the background subtraction method is to model and update the background. Background modelling methods mainly include statistical average, median filtering, single Gaussian background modelling (Wren, Azarbayejani, Darrell, Pentland, 1997), Mixture of Gaussians(MoG) background modelling, W4 (Haritaoglu, Harwood, & Davis, 2000), ViBe (Barnich & Droogenbroech, 2009), and the SOBS (Maddalena & Petrosino, 2008) methods. Each method has its scope of application, and no general method can accurately handle all complex scenes such as light changes, shadow problems, or long-term motionless foreground objects.…”