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Recently the term Cryptocurrency is a frequently used in the mainstream media. The main reason for that is a bitcoin occurrence with reference to the technology and cryptographic inventions that stand behind the bitcoin. Cryptocurrency is legal in region because the European Union has liberal monetary regulations and it can be seen that region successfully follows the world trends in this field. The goal of this paper is to indicate a strong relationship between the value of Bitcoin and expressed interest for it in 11 South-East Europe region countries. The last four and half years will be analysed on a monthly base and data from service Google Trends will be used. Spearman's rank correlation between the expressed interest and bitcoin values for the last four and half years will be calculated. Comment on strong relationship between those variables will be given at the end. This work is licensed under aCreative Commons Attribution-NonCommercial 4.0 International License.

In the last decade, there has been a significant increase in the number of papers related to machine learning and the application of machine learning in various fields of science. Belmonte et al. observed that between 2010 and 2018, the growth in the number of papers related to machine learning topics and big data was exponential. They analysed 4240 scientific publications from the Web of Science citation database [1]. Xu et al., in the analysis of publications in the International Journal of Machine Learning and Cybernetics, noted that from 2010 to 2017, the number of publications, the cooperation rate, the total number of authors, and the degree of cooperation had shown an increasing trend [2]. Dokic et al. analysed the publication of papers in which deep learning is applied in the field of agriculture and noticed that the first papers were published in 2014, and in the second half of the second decade of the 21st century, exponential growth in the number of published papers was observed [3]. The objectives of this paper are primarily to analyze the literature related to the application of deep learning in apple growing, to propose the division of these papers depending on the area, and to analyze the observed trends in publishing papers related to this topic. In the analysis, only papers published in scientific journals were considered, and the condition is that they be found in the citation databases of the Web of Science or Scopus. The second section gives a brief overview of deep learning and its development and a presentation of the importance of apple growing in agriculture. The third section is an overview of papers that use deep learning methods and solve some problems in growing apples. The third section is divided into four parts, depending on the area the paper deals with. The fourth section is a discussion and conclusion.