Torres-Ruiz, M., Juárez-Hipólito, J.H., Lytras, M.D. and Moreno-Ibarra, M. (2016) Environmental Noise Sensing Approach Based on Volunteered Geographic Information and Spatio-Temporal Analysis with Machine Learning. Computational Science and Its Applications—ICCSA 2016, Beijing, 4-7 July 2016, 95-110.
https://doi.org/10.1007/978-3-319-42089-9_7

has been cited by the following article:

• TITLE: Internet-of-Things for Smart Agriculture: Current Applications, Future Perspectives, and Limitations

  AUTHORS: Nastaran Rizan, Siva K. Balasundram, Arash Bayat Shahbazi, Unetha Balachandran, Redmond R. Shamshiri

  KEYWORDS: Smart Agriculture, Internet of Things (IoT), Precision Farming, Data-Driven Agriculture, Agricultural Technology, Sustainable Food Production

  JOURNAL NAME: Agricultural Sciences, Vol.15 No.12, December 13, 2024

  ABSTRACT: The increasing global population has led to a higher demand for food production, while a decrease in rural labor and a rise in production costs present complex challenges for the food industry. Smart agriculture is a farm management concept that considers the deployment of Internet of Things (IoT) to address current food production challenges. In this regard, the agricultural sector is becoming increasingly data-focused, and requires data and technologies that are more precise, advanced, and cutting-edge than in the past. IoT enables agriculture to become data-driven, resulting in timely and more cost-effective farm intervention while reducing environmental impact. This review provides an analytical survey of the current and potential applications of IoT in smart agriculture to overcome challenges posed by spatio-temporal variability under varying environments and task diversity. This review also discusses the challenges that may arise from IoT deployment and presents an overview of the existing applications and those that may be developed in the future.