Enabling Scalable Smart Vertical Farming with IoT and Machine Learning Technologies
Csaba Hegedűs, Attila Frankó, Pál Varga, Stefan Gindl, Markus Tauber
Abstract
Current state of the art in vertical farming faces numerous challenges around optimisation and efficiency. This new domain of agriculture is targeting high level of automated and autonomous operations, which require advanced sensing and actuating capabilities with new types of process control. Although while several commercial solutions are already available, these only satisfy parts of the requirements, not enabling the desired level of autonomy and self-learning capabilities. To this end, this position paper examines state of the art and scopes the work on how to create and integrate Internet of Things and Machine Learning technologies to optimise the active ingredient output while growing medicinal plants in scalable vertical farming grow boxes.