Publication

  • G. Codeluppi, A. Cilfone, L. Davoli and G. Ferrari. AI at the Edge: a Smart Gateway for Greenhouse Air Temperature Forecasting. In 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor). pp. 348–353, 2020. bib | doi ]
  • @inproceedings{cocidafe:2020:metroagrifor,
        author = {{Codeluppi}, Gaia and {Cilfone}, Antonio and {Davoli}, Luca and {Ferrari}, Gianluigi},
        booktitle = {2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)},
        title = {AI at the Edge: a Smart Gateway for Greenhouse Air Temperature Forecasting},
        year = {2020},
        volume = {},
        number = {},
        pages = {348--353},
        abstract = {Controlling and forecasting environmental variables (e.g., air temperature) is usually a key and complex part in a greenhouse management architecture. Indeed, a greenhouse inner micro-climate, which is the result of an extensive set of inter-related environmental variables influenced by external weather conditions, has to be tightly monitored, regulated, and, some-times, forecast. Nowadays, Wireless Sensor Networks (WSNs) and Machine Learning (ML) are two of the most successful technologies to deal with this challenge. In this paper, we discuss how a Smart Gateway (GW), acting as a collector for sensor data coming from a WSN installed in a greenhouse, could be enriched with a Neural Network (NN)-based prediction model allowing to forecast a greenhouse’s inner air temperature. In the case of missing sensor data coming from the WSN, the proposed prediction algorithm, fed with meteorological open data (gathered from the DarkSky repository), is run on the GW in order to predict the missing values. Despite the model is especially designed to be lightweight and executable by a device with constrained capabilities, it can be adopted either at Cloud or at GW level to forecast future air temperature’s values, in order to support the management of a greenhouse. Experimental results show that the NN-based prediction algorithm can forecast greenhouse air temperature with a Root Mean Square Error (RMSE) of 1.50 °C, a Mean Absolute Percentage Error (MAPE) of 4.91%, and a R$^2$ score of 0.965.},
        keywords = {Green products;Predictive models;Temperature sensors;Atmospheric modeling;Temperature measurement;Temperature distribution;Prediction algorithms;Internet of Things;Smart Farming;Edge AI;DNN;Greenhouse Management;Wireless Sensor Network;WSN},
        doi = {10.1109/MetroAgriFor50201.2020.9277553},
        issn = {},
        month = {Nov},
        location = {Trento, Italy}
    }