Application of appropriate models to approximate the performance function warrants more precise prediction and helps to make the best decisions in the poultry industry. This study reevaluated the factors affecting hatchability in laying hens from 29 to 56 wk of age. Twenty-eight data lines representing 4 inputs consisting of egg weight, eggshell thickness, egg sphericity, and yolk/albumin ratio and 1 output, hatchability, were obtained from the literature and used to train an artificial neural network (ANN). The prediction ability of ANN was compared with that of fuzzy logic to evaluate the fitness of these 2 methods. The models were compared using R2, mean absolute deviation (MAD), mean squared error (MSE), mean absolute percentage error (MAPE), and bias. The developed
model was used to assess the relative importance of each variable on the hatchability by calculating the
variable sensitivity ratio. The statistical evaluations showed that the ANN-based model predicted hatchability
more accurately than fuzzy logic. The ANNbased model had a higher determination of coefficient (R2 = 0.99) and lower residual distribution (MAD = 0.005; MSE = 0.00004; MAPE = 0.732; bias = 0.0012) than fuzzy logic (R2 = 0.87; MAD = 0.014; MSE = 0.0004; MAPE = 2.095; bias = 0.0046). The sensitivity analysis revealed that the most important variable in the ANN-based model of hatchability was egg weight (variable sensitivity ratio, VSR = 283.11), followed by yolk/albumin ratio (VSR = 113.16), eggshell thickness (VSR = 16.23), and egg sphericity (VSR = 3.63). The results of this research showed that the universal approximation capability of ANN made it a powerful tool to approximate complex functions such as hatchability in the incubation process.
Modeling of a biological process (e.g., growth rate, egg production, and hatchability) in domestic animals
and poultry is one of the most important steps to approach maximum productivity and make economic decisions.
In some cases, the effect of input variables are not fully understood in the system, and latent relationships
between inputs and output may exist. Therefore, classical methods of data processing (e.g., multiple
linear regression) may not be efficient to elicit the complicated interrelationships of biological responses
and influencing factors. Research has shown that the application of appropriate mathematical models may
release more precise information on the system output along with accurate prediction of performance (Baş and
Boyacı, 2007b; Ahmadi and Golian, 2010, 2011; Savegnago et al., 2011; Mehri, 2012; Peruzzi et al., 2012).
Narushin and Romanov (2002) reviewed the importance of some physical characteristics of the egg including
egg weight, eggshell thickness, sphericity, and yolk/ albumin ratio on hatchability, but the exact contribution
of these factors in the incubation process remains unknown. More recently, Peruzzi et al. (2012) applied
fuzzy logic to model hatchability as a function of egg physical characteristics. They compared the fuzzy
logic-based model with traditional regression analysis and concluded that conventional statistical methods
are not suited for modeling complex systems such as the incubation process. Fuzzy logic is an expert system
that relies on logic, belief, rules of thumb, opinion, and experience. In such systems, part of the data may be
excluded from the modeling process based on if-then rules, resulting in lower interpolation capacity of the
model (Basheer and Hajmeer, 2000).
Publisher: Animal Science Department, Faculty of Agriculture, and Research Center of Special Domestic Animals (RCSDA),University of Zabol, Zabol, Iran 98661-5538
By: M. Mehri
Fil Information: English Language/ 5 page / size : 695 K
ناشر: Animal Science Department, Faculty of Agriculture, and Research Center of Special Domestic Animals (RCSDA),University of Zabol, Zabol, Iran 98661-5538
کاری از: M. Mehri
اطلاعات فایل: زبان انگلیسی/ 5 صفحه/ حجم : 695 کیلوبایت