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International Journal of Poultry Science

.: Home > International Journal of Poultry Science > 2008 > Volume 7 Number 10 > D.P. Smith, K.C. Lawrence and G.W. Heitschmidt

Fertility and Embryo Development of Broiler Hatching Eggs Evaluated with a Hyperspectral Imaging and Predictive Modeling System

D.P. Smith, K.C. Lawrence and G.W. Heitschmidt
USDA, Agricultural Research Service, Richard B. Russell Research Center, 950 College Station Road, Athens, GA, 30605, USA
Abstract :

A hyperspectral imaging system and a predictive modeling technique was evaluated for determining fertility and early embryo development of broiler chicken hatching eggs. Twenty-four broiler eggs were collected (12 fertile, 12 infertile) for each of 8 replicate trials (n = 192) and imaged on Days 0, 1, 2 and
3 of incubation for training and model validation. Three replications of 30 eggs each (fertile and infertile eggs randomly mixed) were collected and imaged as above for model verification (n = 90). Eggs were backlit and positioned below and vertical to the imaging system (lens, spectrograph and CCD camera). Spatial and spectral data from approximately 400-1000nm were collected for each egg on each day of incubation with refinement to 550-899nm. A Mahalanobis Distance (MD) supervised classifier was trained with spectral data from the first 5 replicate sets of eggs, then Principal Component Analysis (PCA) was performed. This model was applied to the next 3 sets for model validation and then to the three 30 egg sets for verification. Fertility was confirmed on Day 5 of incubation by candling and breakout. The MD/PCA model predictions for the 3 validation sets of eggs were: 71% accuracy for Day 0; 63% for Day 1, 65% for Day 2 and 83% for Day 3. For the 3 sets of verification eggs, the MD/PCA model accurately predicted 46/90 on Day 1 and 45/90 on Day 3. The data indicate that the particular MD/PCA model used is not appropriate for predicting fertility and early development.

Keywords :
Broiler hatching eggs, fertility, hyperspectral imaging, predictive modeling

Date Deposited : 12 Jul 2011 11:10

Last Modified : 12 Jul 2011 11:17

Official URL:

Volume 7, Number 10, - 2008 , ISSN 1682-8356

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