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Towards Detecting Red Palm Weevil Using Machine Learning and Fiber Optic Distributed Acoustic Sensing.

Biwei Wang^1,2, Yuan Mao¹, Islam Ashry¹

¹Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.

Sensors (Basel, Switzerland)

|March 6, 2021

Summary

Early detection of red palm weevil (RPW) is now possible using machine learning and fiber optic distributed acoustic sensing (DAS). This technology accurately identifies infested trees, even in noisy environments, protecting palm farms.

Keywords:

fiber optic acoustic sensing machine learning red palm weevil

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Area of Science:

Agricultural Science
Sensor Technology
Machine Learning

Background:

Red palm weevil (RPW) poses a significant threat to global palm tree farms.
Early detection of RPW infestation is crucial but challenging in large-scale agricultural settings.

Purpose of the Study:

To develop and evaluate a novel system for early detection of red palm weevil (RPW) in palm trees.
To combine machine learning algorithms with fiber optic distributed acoustic sensing (DAS) for pest monitoring.

Main Methods:

Simulated a farm environment with healthy and RPW-infested trees under laboratory conditions.
Introduced ambient noise sources (wind, birds) to mimic real-world scenarios.
Utilized fiber optic DAS to collect time- and frequency-domain acoustic data.
Trained a fully-connected artificial neural network (ANN) and a convolutional neural network (CNN) for classification.

Main Results:

Both ANN and CNN models achieved high classification accuracy.
ANN achieved 99.9% accuracy using temporal data.
CNN achieved 99.7% accuracy using spectral data, demonstrating robustness in noisy conditions.

Conclusions:

The integrated machine learning and DAS approach offers an efficient and cost-effective solution for early RPW detection.
This technology has the potential for large-scale, open-air monitoring of RPW in vast palm farms.