Comparison of Predictive Algorithms for IOT Smart Agriculture Sensor Data

Authors

DOI:

https://doi.org/10.3991/ijim.v17i21.44143

Keywords:

NeuralProphet, Random Forest Regression, SARIMA, Artificial Neural Networks (ANN) by KERAS, Internet of Things (IoT), Smart agriculture sensor data, artificial intelligence

Abstract


This paper compares predictive algorithms for smart agriculture sensor data in Internet of Things (IoT) applications. The main objective of IoT in agriculture is to improve productivity and reduce production costs using advanced technology and artificial intelligence. In this study, we compared various predictive algorithms for analyzing IoT smart agriculture sensor data. Specifically, we evaluated the performance of NeuralProphet, Random Forest Regression, SARIMA, and Artificial Neural Networks (ANN) by KERAS algorithms on a dataset containing temperature, humidity, and soil moisture data. The dataset was collected using IoT sensors in a smart agriculture system. The results showed that Random Forest Regression, Seasonal ARIMA, and Artificial Neural Networks by KERAS algorithms outperformed NeuralProphet algorithm in terms of accuracy and computational efficiency.

Cover iJIM – Vol 17, No 21, 2023

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Published

2023-11-15

How to Cite

Fondaj, J., Hamiti, M. ., Krrabaj, S. ., Zenuni, X., & Ajdari, J. (2023). Comparison of Predictive Algorithms for IOT Smart Agriculture Sensor Data. International Journal of Interactive Mobile Technologies (iJIM), 17(21), pp. 65–78. https://doi.org/10.3991/ijim.v17i21.44143

Issue

Vol. 17 No. 21 (2023)

Section

Papers

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Copyright (c) 2023 Jakup Fondaj, Mentor Hamiti, Samedin Krrabaj, Xhemal Zenuni, Jaumin Ajdari

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