A Predictive Framework Combining IoT and Machine Learning Regression Models for Smart Precision Farming

Authors

Kusum Yadav, College of Computer Science and Engineering, University of Ha'il, Kingdom of Saudi ArabiaFollow
Nesreen Abdou El-Hadiede, College of Computer Science and Engineering, University of Ha'il, Kingdom of Saudi ArabiaFollow

Abstract

In this paper, the Internet of Things (IoT) and machine learning algorithms that incorporate regressor are integrated to improve precision agriculture. Data from Internet of Things sensors like temperature sensors, humidity sensors, and soil sensors can be collected and analysed using machine learning algorithms like Support Vector Machines (SVMs) and Multilayer Perceptrons (MLPs). By using the proposed system, crop yield will be optimised, resource usage will be minimised, and the environmental impact of agriculture will be reduced. A comparison of predictive accuracy and error metrics, such as RMSE, demonstrated the effectiveness of automated monitoring, predicting crop health issues, and implementing resource-efficient practices. The MLP algorithm outperforms SVM in terms of prediction performance, highlighting its potential to improve agricultural sustainability and address challenges related to food security.

Article Type

Article

Revise Date

12-22-2024

Recommended Citation

Yadav, Kusum and El-Hadiede, Nesreen Abdou (2024) "A Predictive Framework Combining IoT and Machine Learning Regression Models for Smart Precision Farming," NJF Intelligent Engineering Journal: Vol. 1: Iss. 1, Article 4.
Available at: https://iej.iunajafjournals.com/journal/vol1/iss1/4