GRETA: Pervasive and AR Interfaces for Controlling Intelligent Greenhouses

Bekiaris, I., Leonidis, A., Korozi, M., Stratakis, C., Zidianakis, E., Doxastaki, M. and Stephanidis, C. (2021) GRETA: Pervasive and AR Interfaces for Controlling Intelligent Greenhouses The 17th International Conference on Intelligent Environments (IE2021) will be held in Dubai, United Arab Emirates at Middlesex University Dubai from June 21 – June 24, 2021.

Abstract

Abstract— Considering the prevalence of Ambient Intelligence, this work aims to enhance the interaction between farmers and Intelligent Environments, in order to support their various daily Agricultural activities, aspiring to improve the quality and quantity of cultivated species. Towards this direction, the Greta system was designed and developed, following a user-centered design process, permitting farmers/agronomists to easily monitor and control an Intelligent Greenhouse via a set of useful and usable applications. Greta offers a progressive web app (PWAs) targeting PCs, handheld devices, and technologically-enhanced artifacts of Smart Homes, while it also delivers an Augmented Reality application that visualizes the greenhouse’s interior conditions in a sophisticated manner, and provides context-sensitive assistance regarding cultivation activities. In more detail, the system interoperates with the ambient facilities of an Intelligent Greenhouse allowing end-users to: monitor the conditions inside the greenhouse, remotely control the state of various actuators, be notified regarding the available/active automations, be aware of the optimal conditions for their plants to grow and receive relevant guidelines, be informed regarding any diseases, and communicate with experts for receiving treatment advice. This work describes the design methodology and functionality of Greta, and documents the results of a series of expert-based evaluation experiments.

Integrating Ambient Intelligence Technologies for Empowering Agriculture

Stratakis, C., Stivaktakis, N., M., Bouloukakis, M. Leonidis, A., Doxastaki, M., Kapnas, G., Evdaimon, T., Korozi, M., Kalligiannakis, E. and Stephanidis, C. (2021) Integrating Ambient Intelligence Technologies for Empowering Agriculture 13th EFITA International online Conference

Abstract

This work blends the domain of Precision Agriculture with the prevalent paradigm of Ambient Intelligence, so as to enhance the interaction between farmers and Intelligent Environments, and support their various daily Agricultural activities, aspiring to improve the quality and quantity of cultivated plants. In this paper, two systems are being presented, namely the Intelligent Greenhouse and the AmI seedbed, targeting a wide range of agricultural activities starting from planting the seeds, caring each individual sprouted plant up to their transplantation in the greenhouse, where the provision for the entire plantation lasts until the harvesting period.

AmI Garden: building an IoT Infrastructure for Precision Agriculture

Bouloukakis, M., Stratakis, C., & Stephanidis, C. (2018, April) (2018) AmI Garden: building an IoT Infrastructure for Precision Agriculture Ercim News, Special Theme: Smart Farming, 113, 18-19.

Abstract

ICS-FORTH has recently initiated AmI-Garden, a smart farming project in the framework of its Ambient Intelligence Research Programme. A small experimental IoT greenhouse has been constructed and equipped with polycarbonate cover sheets and all the necessary infrastructure and hardware (automatic window-roof opening/closing, sliding door, fan installation for heating/cooling, vegetable breeding lamps etc.). Inside the greenhouse, a network of wireless sensors is used to measure environmental conditions and parameters, such as air/soil temperature and moisture, sunlight level, soil conductivity, quality and level of chemical ions in irrigation water, etc. The sensors communicate through IoT gateways to the greenhouse’s data centre for storage and post-processing. The system comes with pre-installed agricultural scenarios, a set of activity flows based on environmental conditions that are ideal for each plant species and are monitored in the greenhouse as explained above. The scenarios currently contain parameters to predict common diseases of the plants, as well as unexpected changes in the greenhouse’s microclimate. For example, the irrigation process is built as an agricultural scenario using data from current plant status and past data in order to establish the optimal amount of water to irrigate. The parameters of this scenario are based on specific plant breed and environmental variables. The intelligence behind the scenarios is based on critical limits and thresholds to create cultivation rules. On top of this rule based process, event-driven activation of various automations in the greenhouse is provided, for example, automatic humidity/temperature control, soil fertilisation (hydro fusion) and precise irrigation. Various sets of raw data are produced and ingested into the system, as the life cycle of each one of the plants evolves, in order to be used as the main input for the system’s actuations based on the agricultural treatment scenarios.