Prediction of hop cone ripening through Internet of Things (IoT) and Machine Learning (ML) technologies

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.