Experimental Recognition System for Dirty Eggshell by Using Image Analysis Technique

Abstract

The present study was focused on the design and implementation of an experimental recognition system for dirty chicken eggshell by using an image analysis technique. Image analysis based observation and evaluation techniques can be used efficiently and effectively for agricultural product quality control. Dirt stains on eggs are the result of mainly by feces (black to light brown stains), uric acid (white stains), yolk, and blood. The experimental system was used to obtain dark level images of dirty stains of chicken eggs owing to feces. For this aim, the dirty chicken eggs which have dirty parts were put under a webcam, and dirtiness degree was evaluated by using developed image analysis software at the LabVIEW platform. For the experiment, 100 clean and 100 dirty eggs were used to accurate the determination of dark stains. The results of the research showed that the designed experimental system pointed an accuracy of 99.8% at painted grade eggs. On the other hand, the accuracy of the differentiation of the dirt stains by feces was 98.5%. The developed system can be upgraded for developing egg sorting machines by presence-absence of dirty stains in eggshell.