Prediction of Frost Events Using Machine Learning and IoT Sensing Devices
| Autor: | Facundo Bromberg, Diego Dujovne, Keoma Brun-Laguna, Ana Diedrichs, Thomas Watteyne |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
010504 meteorology & atmospheric sciences
Computer Networks and Communications Computer science Machine learning computer.software_genre Logistic regression 01 natural sciences Resource (project management) Component (UML) 0105 earth and related environmental sciences business.industry Humidity 04 agricultural and veterinary sciences Computer Science Applications Random forest 13. Climate action Hardware and Architecture Agriculture Signal Processing Frost 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Artificial intelligence business computer Information Systems |
| Zdroj: | IEEE Internet of Things Journal. 5:4589-4597 |
| ISSN: | 2372-2541 |
| DOI: | 10.1109/jiot.2018.2867333 |
| Popis: | Internet of Things (IoT) in agriculture applications have evolved to solve several relevant problems from producers. Here, we describe a component of an IoT-enabled frost prediction system. We follow current approaches for prediction that use machine learning algorithms trained by past readings of temperature and humidity sensors to predict future temperatures. However, contrary to current approaches, we assume that the surrounding thermodynamical conditions are informative for prediction. For that, a model was developed for each location, including in its training information of sensor readings of all other locations, autonomously selecting the most relevant ones (algorithm dependent). We evaluated our approach by training regression and classification models using several machine learning algorithms, many already proposed in the literature for the frost prediction problem, over data from five meteorological stations spread along the Mendoza Province of Argentina. Given the scarcity of frost events, data was augmented using the synthetic minority oversampling technique (SMOTE). The experimental results show that selecting the most relevant neighbors and training the models with SMOTE reduces the prediction errors of both regression predictors for all five locations, increases the performance of Random Forest classification predictors for four locations while keeping it unchanged for the remaining one, and produces inconclusive results for logistic regression predictor. These results demonstrate the main claim of these works: that thermodynamic information of neighboring locations can be informative for improving both regression and classification predictions, but also are good enough to suggest that the present approach is a valid and useful resource for decision makers and producers. |
| Databáze: | OpenAIRE |
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