Early Detection of Red Palm Weevil, <i>Rhynchophorus ferrugineus</i> (Olivier), Infestation Using Data Mining
In the past 30 years, the red palm weevil (RPW), <i>Rhynchophorus ferrugineus</i> (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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Plants |
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| Online Access: | https://www.mdpi.com/2223-7747/10/1/95 |
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| author | Heba Kurdi Amal Al-Aldawsari Isra Al-Turaiki Abdulrahman S. Aldawood |
| author_facet | Heba Kurdi Amal Al-Aldawsari Isra Al-Turaiki Abdulrahman S. Aldawood |
| author_sort | Heba Kurdi |
| collection | DOAJ |
| description | In the past 30 years, the red palm weevil (RPW), <i>Rhynchophorus ferrugineus</i> (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. |
| first_indexed | 2024-03-10T13:27:46Z |
| format | Article |
| id | doaj.art-1e13ecaa1c9646669ef3f824b9636a40 |
| institution | Directory Open Access Journal |
| issn | 2223-7747 |
| language | English |
| last_indexed | 2024-03-10T13:27:46Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Plants |
| spelling | doaj.art-1e13ecaa1c9646669ef3f824b9636a402023-11-21T08:41:05ZengMDPI AGPlants2223-77472021-01-011019510.3390/plants10010095Early Detection of Red Palm Weevil, <i>Rhynchophorus ferrugineus</i> (Olivier), Infestation Using Data MiningHeba Kurdi0Amal Al-Aldawsari1Isra Al-Turaiki2Abdulrahman S. Aldawood3Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi ArabiaComputer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi ArabiaInformation Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi ArabiaPlant Protection Department, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi ArabiaIn the past 30 years, the red palm weevil (RPW), <i>Rhynchophorus ferrugineus</i> (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.https://www.mdpi.com/2223-7747/10/1/95red palm weevil<i>Rhynchophorus ferrugineus</i>palminfestationpredictiondata mining |
| spellingShingle | Heba Kurdi Amal Al-Aldawsari Isra Al-Turaiki Abdulrahman S. Aldawood Early Detection of Red Palm Weevil, <i>Rhynchophorus ferrugineus</i> (Olivier), Infestation Using Data Mining Plants red palm weevil <i>Rhynchophorus ferrugineus</i> palm infestation prediction data mining |
| title | Early Detection of Red Palm Weevil, <i>Rhynchophorus ferrugineus</i> (Olivier), Infestation Using Data Mining |
| title_full | Early Detection of Red Palm Weevil, <i>Rhynchophorus ferrugineus</i> (Olivier), Infestation Using Data Mining |
| title_fullStr | Early Detection of Red Palm Weevil, <i>Rhynchophorus ferrugineus</i> (Olivier), Infestation Using Data Mining |
| title_full_unstemmed | Early Detection of Red Palm Weevil, <i>Rhynchophorus ferrugineus</i> (Olivier), Infestation Using Data Mining |
| title_short | Early Detection of Red Palm Weevil, <i>Rhynchophorus ferrugineus</i> (Olivier), Infestation Using Data Mining |
| title_sort | early detection of red palm weevil i rhynchophorus ferrugineus i olivier infestation using data mining |
| topic | red palm weevil <i>Rhynchophorus ferrugineus</i> palm infestation prediction data mining |
| url | https://www.mdpi.com/2223-7747/10/1/95 |
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