Sap flow data used as growth predictor through Machine Deep Learning

Sap flows in the crop are an important indicator of plant development. Students at Inholland University of Applied Sciences worked in the Ideal Research Greenhouse Lab to measure and visualize the sap flows in a cherry tomato crop, whereby Machine Deep Learning has been used to predict the growth of the head thickness of the plants based on these data, they wrote in a research report. Algorithms Within the project, which was monitored using heat-balance sensor technology, algorithms were developed that monitor the moisture balance of the plant. The sap flows (Xylem) and their balance are in practice among the most difficult to measure and to control parameters, the researchers said in the report. "Plants can be grown optimally based on proactive response to evaporation of moisture from the plant." A model has been developed to control, automate, regulate and optimize the development process of the plant while wastage of raw materials such as water, nutrients, etc. is minimized or even prevented.