Smart crop disease monitoring system in IoT using optimization enabled deep residual network
Abstract The Internet of Things (IoT) has recently attracted substantial interest because of its diverse applications. In the agriculture sector, automated methods for detecting plant diseases offer numerous advantages over traditional methods. In the current study, a new model is developed to categ...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-85486-1 |
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| author | Ashish Saini Nasib Singh Gill Preeti Gulia Anoop Kumar Tiwari Priti Maratha Mohd Asif Shah |
| author_facet | Ashish Saini Nasib Singh Gill Preeti Gulia Anoop Kumar Tiwari Priti Maratha Mohd Asif Shah |
| author_sort | Ashish Saini |
| collection | DOAJ |
| description | Abstract The Internet of Things (IoT) has recently attracted substantial interest because of its diverse applications. In the agriculture sector, automated methods for detecting plant diseases offer numerous advantages over traditional methods. In the current study, a new model is developed to categorize plant diseases within an IoT network. The IoT network is simulated for monitoring crop diseases. Routing is performed with Henry Gas Chicken Swarm Optimization (HGCSO), which is designed by integrating Henry Gas Solubility Optimization (HGSO) and Chicken Swarm Optimization (CSO). The fitness parameters of the model include delay, energy, distance, and link lifetime (LLT). At the Base Station (BS), plant disease categorization is performed by collecting plant leaf images. Preprocessing is done on the input images using median filtering. Various features, such as Histogram of Oriented Gradient (HoG), statistical features, Spider Local Image Features (SLIF), and Local Ternary Patterns (LTP) are extracted. Plant disease categorization is carried out using a Deep Residual Network (DRN), which is trained using the developed Caviar Henry Gas Chicken Swarm Optimization (CHGCSO) that combines the CAViaR model with HGCSO. Comparative results show an accuracy of 94.3%, a maximum sensitivity of 93.3%, a maximum specificity of 92%, and an F1-score of 93%, indicating that the CHGCSO-based DRN outperforms existing methods. Graphic Abstract |
| format | Article |
| id | doaj-art-a9fbcf030f0c45b7a7f2fab033d0204d |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-a9fbcf030f0c45b7a7f2fab033d0204d2025-01-12T12:19:03ZengNature PortfolioScientific Reports2045-23222025-01-0115112110.1038/s41598-025-85486-1Smart crop disease monitoring system in IoT using optimization enabled deep residual networkAshish Saini0Nasib Singh Gill1Preeti Gulia2Anoop Kumar Tiwari3Priti Maratha4Mohd Asif Shah5Department of Computer Science & Applications, Maharshi Dayanand UniversityDepartment of Computer Science & Applications, Maharshi Dayanand UniversityDepartment of Computer Science & Applications, Maharshi Dayanand UniversityDepartment of Computer Science & Information Technology, Central University of HaryanaDepartment of Computer Science & Information Technology, Central University of HaryanaDepartment of Economics, Kardan UniversityAbstract The Internet of Things (IoT) has recently attracted substantial interest because of its diverse applications. In the agriculture sector, automated methods for detecting plant diseases offer numerous advantages over traditional methods. In the current study, a new model is developed to categorize plant diseases within an IoT network. The IoT network is simulated for monitoring crop diseases. Routing is performed with Henry Gas Chicken Swarm Optimization (HGCSO), which is designed by integrating Henry Gas Solubility Optimization (HGSO) and Chicken Swarm Optimization (CSO). The fitness parameters of the model include delay, energy, distance, and link lifetime (LLT). At the Base Station (BS), plant disease categorization is performed by collecting plant leaf images. Preprocessing is done on the input images using median filtering. Various features, such as Histogram of Oriented Gradient (HoG), statistical features, Spider Local Image Features (SLIF), and Local Ternary Patterns (LTP) are extracted. Plant disease categorization is carried out using a Deep Residual Network (DRN), which is trained using the developed Caviar Henry Gas Chicken Swarm Optimization (CHGCSO) that combines the CAViaR model with HGCSO. Comparative results show an accuracy of 94.3%, a maximum sensitivity of 93.3%, a maximum specificity of 92%, and an F1-score of 93%, indicating that the CHGCSO-based DRN outperforms existing methods. Graphic Abstracthttps://doi.org/10.1038/s41598-025-85486-1Internet of ThingsSmart crop disease monitoringDeep residual networkSpider local image features |
| spellingShingle | Ashish Saini Nasib Singh Gill Preeti Gulia Anoop Kumar Tiwari Priti Maratha Mohd Asif Shah Smart crop disease monitoring system in IoT using optimization enabled deep residual network Scientific Reports Internet of Things Smart crop disease monitoring Deep residual network Spider local image features |
| title | Smart crop disease monitoring system in IoT using optimization enabled deep residual network |
| title_full | Smart crop disease monitoring system in IoT using optimization enabled deep residual network |
| title_fullStr | Smart crop disease monitoring system in IoT using optimization enabled deep residual network |
| title_full_unstemmed | Smart crop disease monitoring system in IoT using optimization enabled deep residual network |
| title_short | Smart crop disease monitoring system in IoT using optimization enabled deep residual network |
| title_sort | smart crop disease monitoring system in iot using optimization enabled deep residual network |
| topic | Internet of Things Smart crop disease monitoring Deep residual network Spider local image features |
| url | https://doi.org/10.1038/s41598-025-85486-1 |
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