Improving Hydroponic Agriculture through IoT-enabled Collaborative Machine Learning
Published in:Intl. Workshop on Data Science and Internet of Things, Catania, Italy (2019)
This paper presents ongoing work in the development of a scalable hydroponics monitoring system. Our system leverages using wireless IoT technology and applies machine learning techniques on gath- ered data to provide recommendations to agronomists. Hydroponics is a method of growing plants in a water based nutrient rich solution system, instead of soil. By monitoring the parameters of the solution and the en- vironmental parameters inside the greenhouse, farmers can increase the production while decreasing the need for manual labor. Multiple net- worked sensors can measure these parameters and send all the necessary information to an Internet of things (IoT) platform (i.e., Thingsboard) in order the farmer to be able to control and adjust current operating conditions (e.g. environmental controls) and plan the nutrition schedule. Machine Learning can be used to detect anomalous operating conditions and to provide operational recommendations to assist farmers. The nov- elty presented in our system is that data contributed by multiple farming sites can be used to improve the quality of predictions and recommen- dations for all parties involved.
Best paper nomination