Learning by repetition is a process of using artificial intelligence to learn from the data and patterns in greenhouses and to perform repetitive tasks with consistency and speed. It involves using sensors, automation, and robotics to measure the conditions in greenhouses, such as temperature, humidity, light, CO2, fan speed, or electricity bills, and to collect and analyze the data about the crop, such as growth rate, yield, quality, or health. This service enables greenhouse owners to make better decisions, by using machine learning models that can predict the optimal conditions and actions for each crop.