In this paper, sheep forage intake estimation models were developed based on acoustic analysis from data gathered by an audio recorder mounted on a sheep. Forage intake estimation can also make an important contribution to the design and implementation of rotational grazing systems. This introduced system can be applied as an accurate, consistent, fast, and non-destructive in-line sorting technique of chicken eggs in a production line system.įorage intake is one of the most important indicators of health and productivity of ruminant livestock, such as sheep. The same model estimated the volume of the eggs under partial occlusion at RMSE of 1.080 and 1.294 cm(3) for simple and complex, respectively.

The Exponential Gaussian Process Regression outperformed all the explored models with RMSE of 1.175 cm(3) and R-2 of 0.984.

Thirteen regression models based on the egg image (single egg) features were trained. Contour curvature analysis and k-closest M-circle-center algorithms were used to segment the occluded eggs. Two modes of egg configurations on a sorting line were evaluated single-egg (no occlusion) and multi-eggs (partially occluded, i.e., simple and complex). This study introduces a depth image-based chicken-egg volume estimation system. In chicken egg production line systems, grading based on vision systems is challenging due to ambient light conditions and egg occlusion problems.

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