Transforming Agribusiness in Algeria: Harnessing Cognitive Visual Environment and Data-Driven Technologies for Sustainable Growth

Algeria's agricultural output has been greatly influenced by the unfavorable effects of climate change in Africa. The main developments and adoption prospects for agricultural technology are examined in this article, with an emphasis on their intellectualization, digitization, and Big Data integration. Furthermore, it presents the idea of the cognitive visual environment as a potentially effective way to improve smart agriculture practices in Algeria. In order to maximize agribusiness operations, the study investigates the possibilities of the cognitive visual environment by utilizing cutting-edge technology, data analytics, and intelligent systems. This study uses Python to examine unemployment data for Algeria from 1991 to 2021, processing and analyzing the data using Hadoop and PySpark. The goal of the study is to pinpoint temporal trends in the unemployment rate as well as the variables that contribute to the persistently high rate of unemployment. The results show that despite fluctuations, the unemployment rate was continuously high. Low private sector job growth, a lack of economic diversity, and a skills gap are all factors that contribute to high unemployment. For decision-makers and professionals attempting to address Algeria's unemployment, these insights are helpful. This study adds to the expanding body of literature on using Hadoop and big data for economic analysis, emphasizing the value of using cutting-edge analytical tools for well-informed decision-making.