Integration of IoT sensing and spatial interpolation for optimized agricultural monitoring: a case study in Larache, Morocco

Abstract

Sudden variations in temperature and humidity driven by climate change foster plant diseases, affecting crop quality and reducing farmers profits. In this context, real-time monitoring is an effective solution for tracking agricultural fields and reacting rapidly to emerging issues. However, most existing real-time monitoring systems remain inapplicable in Morocco due to high installation and maintenance costs that exceed farmers capacities. This research proposes a low-cost monitoring system based on the Internet of Things that tracks four environmental parameters in real time within an agricultural field. This investigation proposes an optimized deployment strategy aimed at reducing the number of sensors in the field to lower costs, while integrating spatial interpolation techniques, especially the inverse distance weighting (IDW) method, to estimate the values of parameters in areas not covered by sensors. This work also enhances the classic IDW method by incorporating a spatial transformation, thereby improving its accuracy. The experimental results conducted in a real agricultural field showed that IDW with spatial transformation achieved robust performance, surpassing classical IDW and two other interpolation methods, with a low RMSE of 0.0552 for soil moisture and 0.0512 for soil temperature. These results make the system highly suitable for smart agriculture applications.