Smart Palm: An IoT Framework for Red Palm Weevil Early Detection

TL;DR

This paper presents a novel IoT-based smart palm monitoring system designed for early detection of red palm weevil infestations, integrating sensors, data analysis, and user interfaces to improve palm health management.

Contribution

The paper introduces a new IoT framework with sensor data analysis for early pest detection in palm agriculture, specifically targeting red palm weevil infestations.

Findings

Successful prototype implementation for remote palm monitoring

Effective detection of infestation fingerprints using accelerometer data

Potential for early intervention to prevent large-scale damage

Abstract

Smart agriculture is an evolving trend in agriculture industry, where sensors are embedded into plants to collect vital data and help in decision making to ensure higher quality of crops and prevent pests, disease, and other possible threats. In Saudi Arabia, growing palms is the most important agricultural activity, and there is an increasing need to leverage smart agriculture technology to improve the production of dates and prevent diseases. One of the most critical diseases of palms if the red palm weevil, which is an insect that causes a lot of damage to palm trees and can devast large areas of palm trees. The most challenging problem is that the effect of the weevil is not visible by humans until the palm reaches an advanced infestation state. For this reason, there is a need to use advanced technology for early detection and prevention of infestation propagation. In this project, we have developed am IoT based smart palm monitoring prototype as a proof-of-concept that (1) allows to monitor palms remotely using smart agriculture sensors, (2) contribute to the early detection of red palm weevil. Users can use web/mobile application to interact with their palm farms and help them in getting early detection of possible infestations. We used Elm company IoT platform to interface between the sensor layer and the user layer. In addition, we have collected data using accelerometer sensors and we applied signal processing and statistical techniques to analyze collected data and determine a fingerprint of the infestation.