Giulia Oddi

@article{odgaberodafega:2025:cea,

title = {Prediction of Hop Cone Ripening through Internet of Things (IoT) and Machine Learning (ML) Technologies},

author = {Giulia Oddi and Martina Galaverni and Laura Belli and Margherita Rodolfi and Luca Davoli and Gianluigi Ferrari and Tommaso Ganino},

doi = {10.1016/j.compag.2025.110830},

issn = {0168-1699},

year  = {2025},

date = {2025-08-21},

urldate = {2025-01-01},

journal = {Computers and Electronics in Agriculture},

volume = {239},

pages = {110830},

abstract = {Hop (Humulus lupulus L.) cones ripening is characterized by a gradual increase of the valuable brewing metabolites, namely bitter acids and essential oils (EOs), thus making the identification of the optimal harvest time pivotal to obtain high quality yields and avoid economical losses. Cone ripeness is currently evaluated visually: Smart Agriculture (SA) technologies, including the Internet of Things (IoT) paradigm and Machine Learning (ML) models, are expected to have a significant impact on it. In this work, IoT devices are employed to collect data in the time period 2021–2023 at the “Azienda Agricola Ludovico Lucchi” hop testbed located in Campogalliano, Modena, Italy. Two ML-based algorithms are proposed to forecast the optimal harvesting period: the first relies on Multiple Linear Regression (MLR), while the second exploits Principal Component Regression (PCR). Finally, both algorithms classify ripening stages (namely: immature, mature, overripe) through a soft voting classifier. To this end, the identification of the optimal ripening time required the hop cones to be morphologically and chemically characterized (approximately) weekly for three growing seasons. Our results indicated that during the first half of September, there was a contraction in cone width and an increase in the EOs content, representing the optimal harvest maturity. Finally, the proposed ML models forecasted the optimal harvesting period for the 2024 season in the same days and this was confirmed in the reality. The correspondence between predictions and analytical results highlights the potential of integrated IoT and ML techniques to provide decision support for farmers and to improve agricultural operations.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{bedaodprgagafe:2025:dib,

title = {Smart agriculture dataset in a tomato cultivation under different irrigation regimes},

author = {Laura Belli and Luca Davoli and Giulia Oddi and Luca Preite and Martina Galaverni and Tommaso Ganino and Gianluigi Ferrari},

doi = {10.1016/j.dib.2025.111521},

issn = {2352-3409},

year  = {2025},

date = {2025-04-08},

urldate = {2025-01-01},

journal = {Data in Brief},

volume = {60},

pages = {111521},

abstract = {This dataset contains data collected in a tomato cultivation (namely, a Solanum lycopersicum L. cv. HEINZ 1301 cultivation) located at the “Azienda Sperimentale Stuard,” Parma, Italy, through an IoT infrastructure featuring Long Range Wide Area Network (LoRaWAN)-enabled commercial devices deployed in the crop during the summer 2023 period (June 29–September 13). The IoT architecture also controls the irrigation system deployed to manage the watering conditions in the tomato crop, in detail considering three different experimental lines (each one associated with a different irrigation regime): (i) line #1 was irrigated with a water quantity equal to the irrigation recommendation provided by a national cloud service, denoted as Irriframe and developed by the Water Boards Italian Association (ANBI); (ii) line #2 was irrigated with a water quantity equal to 60% of line #1; (iii) line #3 was irrigated with a water quantity equal to 30% of line #1. The dataset comprises 4 different CSV files. The first three files (named as “stuard_environmental_data.csv,” “stuard_water_meter_data.csv,” and “stuard_soil_data.csv”) contain the information sampled every 10 minute by the IoT devices deployed in the crop—one environmental sensor, three water meters, and three soil sensors. The fourth CSV file (named as “indicators.csv”) contains the values of agronomic indicators of interest, calculated daily and mainly depending on daily air temperature values: (i) the Growing Degree Days (GDD) index and (ii) the Heat Units indicators, both calculated on the collected experimental tomato crop data.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}