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Abstrakt
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. PA combines techniques from emerging disciplines i.e., artificial intelligence, and the Internet-of-Things to increase the productivity of agricultural land. From the literature, it is evident that traditional approaches hold limitations 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. The developed system can classify a fruit fly in real field conditions using a convolutional neural network (CNN) classifier based on the following features: (1) Haralick features (2) Histogram of oriented gradients (3) Hu moments and (4) Color histogram. A recall value of 86.2% has been achieved for real test images with mAP of 97.3%. Moreover, 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 an accuracy of 97.5%.
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Autorzy (7)
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Salman Ahmed
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Safdar Nawaz Khan Marwat
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Ghassen Ben Brahim
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Waseem Ullah Khan
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Ala Al-Fuqaha
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Pełna treść
- Wersja publikacji
- Accepted albo Published Version
- DOI:
- Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1038/s41598-024-83012-3
- Licencja
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Informacje szczegółowe
- Kategoria:
- Publikacja w czasopiśmie
- Typ:
- artykuły w czasopismach
- Opublikowano w:
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Scientific Reports
nr 14,
ISSN: 2045-2322 - Język:
- angielski
- Rok wydania:
- 2024
- Opis bibliograficzny:
- Ahmed S., Marwat S. N. K., Brahim G. B., Khan W. U., Khan S., Al-Fuqaha A., Kozieł S.: IoT Based Intelligent Pest Management System for Precision Agriculture// Scientific Reports -Vol. 142, (2024), s.1-16
- DOI:
- Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1038/s41598-024-83012-3
- Źródła finansowania:
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- Publikacja bezkosztowa
- Weryfikacja:
- Politechnika Gdańska
wyświetlono 2 razy
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