This work presents the development and validation of an R Shiny application designed to predict and monitor chrysanthemum pinching, transforming a manual task into a planned, data-driven process. Key agronomic information, such as planting week and growth periods, was digitized in Google Sheets and integrated with R to automatically determine the optimal timing for intervention. The tool records data in real time, issues visual alerts, and generates interactive graphs, enabling precise workload estimation and efficient labor allocation. Its implementation enhanced operational efficiency, reduced the need for field inspections, and improved scheduling accuracy. This model, adaptable to other crops and aligned with the principles of Agriculture 4.0, demonstrates that combining data analysis with agricultural expertise can enhance quality and competitiveness in floriculture.