IoT based intelligent pest management system for precision agriculture
Abstract Despite seemingly inexorable imminent risks of food insecurity that hang over the world, especially in developing countries like Pakistan where traditional agricultural methods are being followed, there still are opportunities created by technology that can help us steer clear of food crisi...
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-83012-3 |
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| author | Salman Ahmed Safdar Nawaz Khan Marwat Ghassen Ben Brahim Waseem Ullah Khan Shahid Khan Ala Al-Fuqaha Slawomir Koziel |
| author_facet | Salman Ahmed Safdar Nawaz Khan Marwat Ghassen Ben Brahim Waseem Ullah Khan Shahid Khan Ala Al-Fuqaha Slawomir Koziel |
| author_sort | Salman Ahmed |
| collection | DOAJ |
| description | Abstract Despite seemingly inexorable imminent risks of food insecurity that hang over the world, especially in developing countries like Pakistan where traditional agricultural methods are being followed, there still are opportunities created by technology that can help us steer clear of food crisis threats in upcoming years. At present, the agricultural sector worldwide is rapidly pacing towards technology-driven Precision Agriculture (PA) approaches for enhancing crop protection and boosting productivity. Literature highlights the limitations of traditional approaches such as chances of human error in recognizing and counting pests, and require trained labor. Against such a backdrop, this paper proposes a smart IoT-based pest detection platform for integrated pest management, and monitoring crop field conditions that are of crucial help to farmers in real field environments. The proposed system comprises a physical prototype of a smart insect trap equipped with embedded computing to detect and classify pests. To this aim, a dataset was created featuring images of oriental fruit flies captured under varying illumination conditions in guava orchards. The size of the dataset is 1000+ images categorized into two groups: (1) fruit fly and (2) not fruit fly and a convolutional neural network (CNN) classifier was trained based on the following features: (1) Haralick features (2) Histogram of oriented gradients (3) Hu moments and (4) Color histogram. The system achieved a recall value of 86.2% for real test images with Mean Average Precision (mAP) of 97.3%. Additionally, the proposed model has been compared with numerous machine learning (ML) and deep learning (DL) based models to verify the efficacy of the proposed model. The comparative results indicated that the best performance was achieved by the proposed model with the highest accuracy, precision, recall, F1-score, specificity, and FNR with values of 97.5%, 92.82%, 98.92%, 95.00%, 95.90%, and 5.88% respectively. |
| format | Article |
| id | doaj-art-d53c5445e5ad47059a622aa2afc47f36 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-d53c5445e5ad47059a622aa2afc47f362025-01-05T12:26:11ZengNature PortfolioScientific Reports2045-23222024-12-0114111510.1038/s41598-024-83012-3IoT based intelligent pest management system for precision agricultureSalman Ahmed0Safdar Nawaz Khan Marwat1Ghassen Ben Brahim2Waseem Ullah Khan3Shahid Khan4Ala Al-Fuqaha5Slawomir Koziel6Faculty of Computer Science and Engineering, GIK InstituteDepartment of Computer Systems Engineering, Faculty of Electrical and Computer Engineering, University of Engineering and TechnologyCollege of Computer Engineering and Science, Prince Mohammad Bin Fahd UniversityDepartment of Computer Systems Engineering, Faculty of Electrical and Computer Engineering, University of Engineering and TechnologyFaculty of Electronics, Telecommunications, and Informatics, Gdansk University of TechnologyCollege of Science and Engineering, Hamad Bin Khalifa UniversityFaculty of Electronics, Telecommunications, and Informatics, Gdansk University of TechnologyAbstract Despite seemingly inexorable imminent risks of food insecurity that hang over the world, especially in developing countries like Pakistan where traditional agricultural methods are being followed, there still are opportunities created by technology that can help us steer clear of food crisis threats in upcoming years. At present, the agricultural sector worldwide is rapidly pacing towards technology-driven Precision Agriculture (PA) approaches for enhancing crop protection and boosting productivity. Literature highlights the limitations of traditional approaches such as chances of human error in recognizing and counting pests, and require trained labor. Against such a backdrop, this paper proposes a smart IoT-based pest detection platform for integrated pest management, and monitoring crop field conditions that are of crucial help to farmers in real field environments. The proposed system comprises a physical prototype of a smart insect trap equipped with embedded computing to detect and classify pests. To this aim, a dataset was created featuring images of oriental fruit flies captured under varying illumination conditions in guava orchards. The size of the dataset is 1000+ images categorized into two groups: (1) fruit fly and (2) not fruit fly and a convolutional neural network (CNN) classifier was trained based on the following features: (1) Haralick features (2) Histogram of oriented gradients (3) Hu moments and (4) Color histogram. The system achieved a recall value of 86.2% for real test images with Mean Average Precision (mAP) of 97.3%. Additionally, the proposed model has been compared with numerous machine learning (ML) and deep learning (DL) based models to verify the efficacy of the proposed model. The comparative results indicated that the best performance was achieved by the proposed model with the highest accuracy, precision, recall, F1-score, specificity, and FNR with values of 97.5%, 92.82%, 98.92%, 95.00%, 95.90%, and 5.88% respectively.https://doi.org/10.1038/s41598-024-83012-3Internet of ThingsSmart insect trapMachine learningDeep learning |
| spellingShingle | Salman Ahmed Safdar Nawaz Khan Marwat Ghassen Ben Brahim Waseem Ullah Khan Shahid Khan Ala Al-Fuqaha Slawomir Koziel IoT based intelligent pest management system for precision agriculture Scientific Reports Internet of Things Smart insect trap Machine learning Deep learning |
| title | IoT based intelligent pest management system for precision agriculture |
| title_full | IoT based intelligent pest management system for precision agriculture |
| title_fullStr | IoT based intelligent pest management system for precision agriculture |
| title_full_unstemmed | IoT based intelligent pest management system for precision agriculture |
| title_short | IoT based intelligent pest management system for precision agriculture |
| title_sort | iot based intelligent pest management system for precision agriculture |
| topic | Internet of Things Smart insect trap Machine learning Deep learning |
| url | https://doi.org/10.1038/s41598-024-83012-3 |
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