@inproceedings{1ee9ae9ea16646c79d786a38c31fd5cf,
title = "IoT-Enabled Crop Recommendation in Smart Agriculture Using Machine Learning",
abstract = "Crop recommendation systems driven by IoT data in smart agriculture is a valuable tool in contemporary farming approaches. Such systems increasingly rely on machine learning techniques to reason over the most suitable crops according to soil, environmental and weather parameters continously mea-sured by IoT sensors. This paper applies a set of state-of-the-art machine learning models for crop recommendation using an open dataset for multi-class classification. Evaluation results show that Random Forest classifier outperforms all the other models that were employed in our research.",
keywords = "Crop Recommendation, Internet of Things, Machine Learning, Smart Agriculture",
author = "Gregory Davrazos and Theodor Panagiotakopoulos and Sotiris Kotsiantis and Achilles Kameas",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023 ; Conference date: 10-07-2023 Through 12-07-2023",
year = "2023",
doi = "10.1109/IISA59645.2023.10345924",
language = "English",
series = "14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023",
}