Travis Anthony Conrad,Adam Phillip Takla Greenberg,Kyle Terrence James Rooney
申请号：
US15286498
公开号：
US20170332544A1
申请日:
2016.10.05
申请国别(地区):
US
年份:
2017
代理人:
摘要：
Data driven indoor farming optimization may provide autonomous decision making that maximizes crop yield. One or more specifications and one or more constraints on resources for a crop being monitored may be received at computing devices. The computing devices may generate a simulation using a machine learning algorithm to determine whether the one or more specifications and the one or more constraints result in a grow solution for the crop. The simulation may be constrained by historical data on one or more variables that affect crop production. The computing devices may receive a modification to a constraint in the event that the simulation failed to generate a grow solution for the crop. On the other hand, if the simulation generates a grow solution for the crop, the simulation may be run to predict growth of the crop at specific time intervals of a grow cycle for the crop.
