Context-Aware Computing for Balanced Agricultural Production using Machine Learning

Agriculture is the foundation factor of many developing countries' economy. Now we are in the era of industrial revolution and especially the developing countries are shifted their vision completely to this revolution. But, they are not thinking about smart cultivation and more production from this ancient key factor of economic growth. Still farmers are following the ancient techniques for cultivation without thinking different dimensions or key factors for better production. Farmers should understand the present context for cultivating any crops to get proper profit against any specific cultivation. And to do this study they have to analysis statistical data of production report over a long period considering climate, region, availability of pesticides, period specific demand and so many dimensions. So we have to employ the context-aware computing to estimate the reasonable balanced production of any crops. In many countries like; Bangladesh in every cultivation season many farmers are not getting proper price of their crops due to over production. Sometimes farmers fail to return their invested money for their cultivation where their time and physical effort is completely a loss. To address this vital issue our research work will do proper context-aware analysis. This paper's agricultural context-aware computing for balanced production deals with extract-transform-loading (ETL), data warehousing (DWH), dimension (period, weather, pesticides etc.) specific data visualization, and supervised learning process to estimate future production.