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Article

Early Detection of Red Palm Weevil, Rhynchophorus ferrugineus (Olivier), Infestation Using Data Mining

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Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
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Mechanical Engineering Department, Massachusetts Institute of Technology (MIT), Cambridge, MA 02142-1308, USA
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Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
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Plant Protection Department, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia
*
Author to whom correspondence should be addressed.
Plants 2021, 10(1), 95; https://doi.org/10.3390/plants10010095
Received: 23 November 2020 / Revised: 29 December 2020 / Accepted: 31 December 2020 / Published: 6 January 2021
(This article belongs to the Section Plant Protection and Biotic Interactions)
In the past 30 years, the red palm weevil (RPW), Rhynchophorus ferrugineus (Olivier), a pest that is highly destructive to all types of palms, has rapidly spread worldwide. However, detecting infestation with the RPW is highly challenging because symptoms are not visible until the death of the palm tree is inevitable. In addition, the use of automated RPW weevil identification tools to predict infestation is complicated by a lack of RPW datasets. In this study, we assessed the capability of 10 state-of-the-art data mining classification algorithms, Naive Bayes (NB), KSTAR, AdaBoost, bagging, PART, J48 Decision tree, multilayer perceptron (MLP), support vector machine (SVM), random forest, and logistic regression, to use plant-size and temperature measurements collected from individual trees to predict RPW infestation in its early stages before significant damage is caused to the tree. The performance of the classification algorithms was evaluated in terms of accuracy, precision, recall, and F-measure using a real RPW dataset. The experimental results showed that infestations with RPW can be predicted with an accuracy up to 93%, precision above 87%, recall equals 100%, and F-measure greater than 93% using data mining. Additionally, we found that temperature and circumference are the most important features for predicting RPW infestation. However, we strongly call for collecting and aggregating more RPW datasets to run more experiments to validate these results and provide more conclusive findings. View Full-Text
Keywords: red palm weevil; Rhynchophorus ferrugineus; palm; infestation; prediction; data mining red palm weevil; Rhynchophorus ferrugineus; palm; infestation; prediction; data mining
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MDPI and ACS Style

Kurdi, H.; Al-Aldawsari, A.; Al-Turaiki, I.; Aldawood, A.S. Early Detection of Red Palm Weevil, Rhynchophorus ferrugineus (Olivier), Infestation Using Data Mining. Plants 2021, 10, 95. https://doi.org/10.3390/plants10010095

AMA Style

Kurdi H, Al-Aldawsari A, Al-Turaiki I, Aldawood AS. Early Detection of Red Palm Weevil, Rhynchophorus ferrugineus (Olivier), Infestation Using Data Mining. Plants. 2021; 10(1):95. https://doi.org/10.3390/plants10010095

Chicago/Turabian Style

Kurdi, Heba, Amal Al-Aldawsari, Isra Al-Turaiki, and Abdulrahman S. Aldawood 2021. "Early Detection of Red Palm Weevil, Rhynchophorus ferrugineus (Olivier), Infestation Using Data Mining" Plants 10, no. 1: 95. https://doi.org/10.3390/plants10010095

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