Internet of Things Based Weekly Crop Pest Prediction by Using Deep Neural Network
Internet of Things (IoT) assisted application in agriculture shows tremendous success to improve productivity in agriculture. Agriculture is grappling with issues such as depleted soil fertility, climate-related hazards like intensified pest attacks and diseases. Accurate forecasting of pest outbrea...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10207031/ |
| _version_ | 1797741537623277568 |
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| author | Rana Muhammad Saleem Rab Nawaz Bashir Muhammad Faheem Mohd Anul Haq Ahmed Alhussen Zamil S. Alzamil Shakir Khan |
| author_facet | Rana Muhammad Saleem Rab Nawaz Bashir Muhammad Faheem Mohd Anul Haq Ahmed Alhussen Zamil S. Alzamil Shakir Khan |
| author_sort | Rana Muhammad Saleem |
| collection | DOAJ |
| description | Internet of Things (IoT) assisted application in agriculture shows tremendous success to improve productivity in agriculture. Agriculture is grappling with issues such as depleted soil fertility, climate-related hazards like intensified pest attacks and diseases. Accurate forecasting of pest outbreaks can play a vital role in improving agricultural yield. Utilizing IoT technology for environmental monitoring in crop fields to forecast pest attacks. The important parameters for pest predictions are temperature, humidity, rainfall, wind speed and sunshine duration. Directly sensed environmental conditions are utilized as input to a deep learning model, which makes binary decisions about the presence of pest populations based on the prevailing environmental conditions. The accuracy and precision of the deep learning model in making predictions are assessed through evaluation with test data. Five-year data 2028 to 2022 have been used for making prediction. The model of pest prediction generates weekly predictions. The overall accuracy of the weekly predictions is 94% and high F-measure, Precision, Recall, Cohens kappa, and ROC AUC for making to optimize the prediction. The accuracy of the pest prediction improves gradually with time. Weekly predictions are generated from the means of all environmental conditions from the last seven days. The weekly predictions are important for the short-term measures against pest attacks. |
| first_indexed | 2024-03-12T14:28:09Z |
| format | Article |
| id | doaj.art-32eacd13674d4ad7b41e720a1d396df9 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T14:28:09Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-32eacd13674d4ad7b41e720a1d396df92023-08-17T23:00:18ZengIEEEIEEE Access2169-35362023-01-0111859008591310.1109/ACCESS.2023.330150410207031Internet of Things Based Weekly Crop Pest Prediction by Using Deep Neural NetworkRana Muhammad Saleem0https://orcid.org/0000-0001-8653-9135Rab Nawaz Bashir1https://orcid.org/0000-0001-7409-1775Muhammad Faheem2https://orcid.org/0000-0003-4628-4486Mohd Anul Haq3https://orcid.org/0000-0001-5913-5979Ahmed Alhussen4Zamil S. Alzamil5https://orcid.org/0000-0002-8407-6437Shakir Khan6https://orcid.org/0000-0002-7925-9191Department of Computer Science, University of Agriculture, Faisalabad Sub Campus Burewala, Faisalabad, PakistanDepartment of Computer Science, COMSAT University Islamabad, Vehari Campus, Vehari, PakistanSchool of Technology and Innovations, University of Vaasa, Vaasa, FinlandDepartment of Computer Science, College of Computer and Information Sciences, Majmaah University, Al-Majmaah, Saudi ArabiaDepartment of Computer Engineering, College of Computer and Information Sciences, Majmaah University, Al-Majmaah, Saudi ArabiaDepartment of Computer Science, College of Computer and Information Sciences, Majmaah University, Al-Majmaah, Saudi ArabiaCollege of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi ArabiaInternet of Things (IoT) assisted application in agriculture shows tremendous success to improve productivity in agriculture. Agriculture is grappling with issues such as depleted soil fertility, climate-related hazards like intensified pest attacks and diseases. Accurate forecasting of pest outbreaks can play a vital role in improving agricultural yield. Utilizing IoT technology for environmental monitoring in crop fields to forecast pest attacks. The important parameters for pest predictions are temperature, humidity, rainfall, wind speed and sunshine duration. Directly sensed environmental conditions are utilized as input to a deep learning model, which makes binary decisions about the presence of pest populations based on the prevailing environmental conditions. The accuracy and precision of the deep learning model in making predictions are assessed through evaluation with test data. Five-year data 2028 to 2022 have been used for making prediction. The model of pest prediction generates weekly predictions. The overall accuracy of the weekly predictions is 94% and high F-measure, Precision, Recall, Cohens kappa, and ROC AUC for making to optimize the prediction. The accuracy of the pest prediction improves gradually with time. Weekly predictions are generated from the means of all environmental conditions from the last seven days. The weekly predictions are important for the short-term measures against pest attacks.https://ieeexplore.ieee.org/document/10207031/Internet of Things (IoT)deep learning modelpest predictionsweekly predictions |
| spellingShingle | Rana Muhammad Saleem Rab Nawaz Bashir Muhammad Faheem Mohd Anul Haq Ahmed Alhussen Zamil S. Alzamil Shakir Khan Internet of Things Based Weekly Crop Pest Prediction by Using Deep Neural Network IEEE Access Internet of Things (IoT) deep learning model pest predictions weekly predictions |
| title | Internet of Things Based Weekly Crop Pest Prediction by Using Deep Neural Network |
| title_full | Internet of Things Based Weekly Crop Pest Prediction by Using Deep Neural Network |
| title_fullStr | Internet of Things Based Weekly Crop Pest Prediction by Using Deep Neural Network |
| title_full_unstemmed | Internet of Things Based Weekly Crop Pest Prediction by Using Deep Neural Network |
| title_short | Internet of Things Based Weekly Crop Pest Prediction by Using Deep Neural Network |
| title_sort | internet of things based weekly crop pest prediction by using deep neural network |
| topic | Internet of Things (IoT) deep learning model pest predictions weekly predictions |
| url | https://ieeexplore.ieee.org/document/10207031/ |
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