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.

The health and vitality of callus growth is one of the prerequisites for the success of further in vitro studies. This study investigated the efficiency of different percentage (0%, 10%, 20%, 30%, and 40%) of vermicompost tea as an organic substance on in vitro callus growth in sainfoin. Morpho-physiological responses of calli to vermicompost tea measured under in vitro conditions. As a result of this investigation, a combination of plant growth regulators (4 mg/l BAP and 0.5 mg/l NAA) with 20% of vermicompost tea causing significant callus initiation and growth in sainfoin stem explants. Under the light of these scientific findings, vermicompost tea might be used as an organic bio stimulant for efficient callus growth and complementary to commercial chemical hormones in sainfoin. This research is important due to it can contribute positively to the plant species that are difficult in terms of callus growth and plant regeneration in tissue culture.

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.