An Intelligent and Unmanned System for Urban Vertical Agriculture Hydroponic Planting

Because of the need for low-cost and efficient cultivation and control methods, vertical hydroponic technology is considered as a viable alternative to traditional agriculture. This paper proposes an intelligent and unmanned system for urban vertical agriculture hydroponic planting, which combines urban vertical planting with a machine learning algorithm. Based on a machine learning algorithm, the urban vertical agriculture hydroponic unmanned planting model is used to fit the relationship among the plant’s height, growth time, environmental temperature, and humidity, followed by the prediction of the optimal environmental conditions for plants. The predicted results calculated by the urban vertical agriculture hydroponic unmanned planting model would feed back into an unmanned quantitative irrigation system to adjust the environmental temperature and humidity in real-time, which would improve the algorithm according to the planting situations in the unmanned quantitative irrigation system. This intelligent urban vertical agriculture hydroponic planting system is significant for improving the cost and manpower resources.