Monitoring Population Phenology of Asian Citrus Psyllid Using Deep Learning
Asian citrus psyllid, Diaphorina citri Kuwayama (Liviidae: Hemiptera) is a menacing and notorious pest of citrus plants. It vectors a phloem vessel-dwelling bacterium Candidatus Liberibacter asiaticus, which is a causative pathogen of the serious citrus disease known as Huanglongbing. Huanglongbing...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/4644213 |
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| author | Maria Bibi Muhammad Kashif Hanif Muhammad Umer Sarwar Muhammad Irfan Khan Shouket Zaman Khan Casper Shikali Shivachi Asad Anees |
| author_facet | Maria Bibi Muhammad Kashif Hanif Muhammad Umer Sarwar Muhammad Irfan Khan Shouket Zaman Khan Casper Shikali Shivachi Asad Anees |
| author_sort | Maria Bibi |
| collection | DOAJ |
| description | Asian citrus psyllid, Diaphorina citri Kuwayama (Liviidae: Hemiptera) is a menacing and notorious pest of citrus plants. It vectors a phloem vessel-dwelling bacterium Candidatus Liberibacter asiaticus, which is a causative pathogen of the serious citrus disease known as Huanglongbing. Huanglongbing disease is a major bottleneck in the export of citrus fruits from Pakistan. It is being responsible for huge citrus economic losses globally. In the current study, several prediction models were developed based on regression algorithms of machine learning to monitor different phenological stages of Asian citrus psyllid to predict its population about different abiotic variables (average maximum temperature, average minimum temperature, average weekly temperature, average weekly relative humidity, and average weekly rainfall) and biotic variable (host plant phenological patterns) in citrus-growing regions of Pakistan. The pest prediction models can be used for proper applications of pesticides only when needed for reducing the environmental and cost impacts of pesticides. Pearson’s correlation analysis was performed to find the relationship between different predictor (abiotic and biotic) variables and pest infestation rate on citrus plants. Multiple linear regression, random forest regressor, and deep neural network approaches were compared to predict population dynamics of Asian citrus psyllid. In comparison with other regression techniques, a deep neural network-based prediction model resulted in the least root mean squared error values while predicting egg, nymph, and adult populations. |
| format | Article |
| id | doaj-art-77bc3ec82e7e404cadf9e7892f6a6f2e |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-77bc3ec82e7e404cadf9e7892f6a6f2e2025-02-03T01:04:21ZengWileyComplexity1099-05262021-01-01202110.1155/2021/4644213Monitoring Population Phenology of Asian Citrus Psyllid Using Deep LearningMaria Bibi0Muhammad Kashif Hanif1Muhammad Umer Sarwar2Muhammad Irfan Khan3Shouket Zaman Khan4Casper Shikali Shivachi5Asad Anees6Department of Computer ScienceDepartment of Computer ScienceDepartment of Computer ScienceDepartment of Computer ScienceDepartment of EntomologySouth Eastern Kenya UniversityCardiovascular Engineering IncAsian citrus psyllid, Diaphorina citri Kuwayama (Liviidae: Hemiptera) is a menacing and notorious pest of citrus plants. It vectors a phloem vessel-dwelling bacterium Candidatus Liberibacter asiaticus, which is a causative pathogen of the serious citrus disease known as Huanglongbing. Huanglongbing disease is a major bottleneck in the export of citrus fruits from Pakistan. It is being responsible for huge citrus economic losses globally. In the current study, several prediction models were developed based on regression algorithms of machine learning to monitor different phenological stages of Asian citrus psyllid to predict its population about different abiotic variables (average maximum temperature, average minimum temperature, average weekly temperature, average weekly relative humidity, and average weekly rainfall) and biotic variable (host plant phenological patterns) in citrus-growing regions of Pakistan. The pest prediction models can be used for proper applications of pesticides only when needed for reducing the environmental and cost impacts of pesticides. Pearson’s correlation analysis was performed to find the relationship between different predictor (abiotic and biotic) variables and pest infestation rate on citrus plants. Multiple linear regression, random forest regressor, and deep neural network approaches were compared to predict population dynamics of Asian citrus psyllid. In comparison with other regression techniques, a deep neural network-based prediction model resulted in the least root mean squared error values while predicting egg, nymph, and adult populations.http://dx.doi.org/10.1155/2021/4644213 |
| spellingShingle | Maria Bibi Muhammad Kashif Hanif Muhammad Umer Sarwar Muhammad Irfan Khan Shouket Zaman Khan Casper Shikali Shivachi Asad Anees Monitoring Population Phenology of Asian Citrus Psyllid Using Deep Learning Complexity |
| title | Monitoring Population Phenology of Asian Citrus Psyllid Using Deep Learning |
| title_full | Monitoring Population Phenology of Asian Citrus Psyllid Using Deep Learning |
| title_fullStr | Monitoring Population Phenology of Asian Citrus Psyllid Using Deep Learning |
| title_full_unstemmed | Monitoring Population Phenology of Asian Citrus Psyllid Using Deep Learning |
| title_short | Monitoring Population Phenology of Asian Citrus Psyllid Using Deep Learning |
| title_sort | monitoring population phenology of asian citrus psyllid using deep learning |
| url | http://dx.doi.org/10.1155/2021/4644213 |
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