@inbook {160, title = {Implementing an Integrated Internet of Things System (IoT) for Hydroponic Agriculture}, booktitle = {Data Science and Internet of Things: Research and Applications at the Intersection of DS and IoT}, year = {2021}, pages = {83{\textendash}102}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, abstract = {This chapter presents ongoing work in the development of a hydroponics monitoring system by using IoT technology. Hydroponics is a method of growing plants in water based nutrient rich solution system, instead of soil. By monitoring the parameters of the solution in parallel with the environmental parameters inside the greenhouse, farmers can increase the production while decreasing the need for manual labor. Multiple networked 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 or agronomist to be able to control and adjust current operating conditions (e.g. environmental controls) and plan the nutrition schedule. Furthermore Machine Learning (ML) can be used, so the system will provide recommendations to agronomists. The novelty presented in our system is that data contributed by multiple farming sites can be used to improve the quality of predictions and recommendations for all parties involved.}, isbn = {978-3-030-67197-6}, doi = {10.1007/978-3-030-67197-6_5}, url = {https://doi.org/10.1007/978-3-030-67197-6_5}, author = {Georgiadis, Georgios and Komninos, Andreas and Koskeris, Andreas and Garofalakis, John}, editor = {Fortino, Giancarlo and Liotta, Antonio and Gravina, Raffaele and Longheu, Alessandro} } @conference {142, title = {Improving Hydroponic Agriculture through IoT-enabled Collaborative Machine Learning}, booktitle = {Intl. Workshop on Data Science and Internet of Things}, year = {2019}, note = {Best paper nomination}, address = {Catania, Italy}, abstract = {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.}, author = {Georgiadis, Georgios and Andreas Komninos and Koskeris, Andreas and John Garofalakis} } @conference {141, title = {Internet of things applications on monitoring hydroponics through wireless sensor networks}, booktitle = {10th International Conference on Information, Intelligence, Systems and Applications (IISA{\textquoteright}19), Project Track}, year = {2019}, month = {07/2019}, address = {Patras, Greece}, abstract = {This paper presents the development of a scalable hydro- ponics monitoring system and data processing through wireless sensor networks. We implement novel hardware and virtual sensors through ab- straction in an IoT management platform. We introduce the concept of collaborative machine learning from multiple sites to improve prediction and discuss related project challenges}, keywords = {Hydroponics, Internet of Things, Machine Learning}, doi = {10.26220/iisa.3330}, author = {Andreas Komninos and Georgiadis, Georgios and Koskeris, Andreas and John Garofalakis} }