Bees play a significant role in the health of terrestrial ecosystems. The decline of bee populations due to colony collapse disorder around the world constitutes a severe ecological danger. Maintaining high yield of honey and understanding of bee behaviour necessitate constant attention to the hives. Research initiatives have been taken to establish monitoring programs to study the behaviour of bees in accessing their habitat. Monitoring the sanitation and development of bee brood allows for preventative measures to be taken against mite infections and an overall improvement in the brood's health. This study proposed a precision beekeeping method that aims to reduce bee colony mortality and improve conventional apiculture through the use of technological tools to gather, analyse, and understand bee colony characteristics. This research presents the application of advanced digital image processing with computer vision techniques for the visual identification and analysis of bee brood at various developing stages. The beehive images are first preprocessed to enhance the important features of object. Further, object is segmented and classified using computer vision techniques. The research is carried out with the images containing variety of immature brood stages. The suggested method and existing methods are tested and compared to evaluate efficiency of proposed methodology.

Using a production function approach, we estimate that the economic value of biotic pollination to Georgia’s agriculture increased from $425 million in 2009 to $488 million in 2017 in real terms. We perform spatial analysis to reveal county-level spatial patterns and temporal trends in that value. Using a unique set of pollinator survey data, we also compare the locations of biotic pollinators to the areas they bring the most economic value to, which provides insights on the variation in the dependency of the crop mix to pollination services.