Machine Learning based Early Detection of Red Palm Weevil using Remote Sensing Technology in Saudi Arabia

Authors

  • M.A.H. Farquad  Department of Information System, Faculty of Computer and Information Systems, Islamic University of Madinah, Saudi Arabia

Keywords:

Machine Learning, Early Detection, Red Palm Weevil, Remote Sensing Technology.

Abstract

Saudi Arabia is known to be the home land of date palm trees and it is one of the leading exporters of dates to the world. Aim of Ministry of Environment, Water and Agriculture is to develop and safeguard the natural resources of the kingdom. Saving and development of the agricultural resources of Saudi Arabia region is one of the main objectives of Vision 2030. The strategic objectives scheduled to achieve by 2020 by authorities in Saudi Arabia include the Controlling of pests and saving & developing the agricultural land in the kingdom. Red Palm Weevil (RPW, Rynochophorus Ferrugineus) is found to be one of the most dangerous threats to palm trees around the globe. Early detection of RPW is not easy because palm trees do not show any visual evidence of infection. Acoustic monitoring is one of the many approaches proposed in the literature for early detection of the RPW which is based on the audible sound of RPW larvae activity inside palm trunk. Later, autonomous bioacoustics sensors have been used to analyze the audio signal for longer period of times. In this research, we propose the use of Thermal Remote Sensing Technology (thermography) for early detection of the RPW resulting in acquiring remedial measures at the earliest possible to reduce the possible loss. Thermography is gaining popularity as it is cost effective and determines thermal properties of any object of interest. The principle of thermal remote sensing is to convert invisible radiation pattern of objects to visible images (thermal images). These images can be acquired using portable, handheld or thermal sensors that are coupled with optical systems mounted on satellite. For analysis, an image of relative radiant temperatures (a thermography) is depicted in gray levels, with warmer temperatures shown in light tones, and cooler temperatures in dark tones. The proposed research project will analyze the thermal image data acquired from various date palm fields around Saudi Arabia using machine learning algorithms for early identification and prediction of Red Palm Weevil. Root means squared error measure is used to evaluate the efficiency of early detection of RPW infestation in date palms using machine learning algorithms. The proposed research is expected to help the authorities to plan the controlling of pests ahead of times and reduce possible losses and also take precautionary measures to develop the agricultural initiatives in the kingdom of Saudi Arabia.

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Published

2018-06-30

Issue

Volume 3, Issue 5, May-June-2018

Section

Research Articles

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How to Cite

[1]
M.A.H. Farquad, " Machine Learning based Early Detection of Red Palm Weevil using Remote Sensing Technology in Saudi Arabia, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 5, pp.587-595, May-June-2018.