Global banana production faces significant challenges in environmental monitoring and data driven decision making. Although there are mobile applications that integrate environmental sensors and offer real-time visualization, some with predictive algorithms to optimize irrigation and fertilizer usage, limitations still remain. These include interoperability between systems and accessibility for small producers. In the midst of these challenges, there is an urgent need to develop comprehensive, accessible, AI-powered mobile solutions to increase production efficiency and environmental sustainability in banana cultivation. This study will address the importance of this topic through systematic research mapping. We will search, study, select, analyze, and identify relationships in mobile banana production monitoring applications and evaluating the frequency of publications in high-impact scientific libraries. This is necessary given the absence of previous statistical studies in this area.