Food security, in the context of a growing world population and spatial restrictions for farmlands, demands new approaches to optimize crop productivity. This work proposes a methodology for monitoring agricultural fields over time with a high spatial and temporal resolution, using low-cost RGB sensors on board of small scale drones in combination with computer vision and programming techniques. The methodology was applied to study black bean crop dynamics in Costa Rica over a six-week period with weekly observations. Resolutions up to 8.5mm/pixel and RMSE deviations in the millimeter range could be achieved from dense point clouds and digital elevation models.