Application of IoT-Based Drones in Precision Agriculture for Pest Control
Unmanned aerial vehicles (UAVs), commonly known as drones, have been progressively prevalent due to their capability to operate quickly and their vast range of applications in a variety of real-world circumstances. The utilization of UAVs in precision farming has lately gained a lot of attention fro...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/1160258 |
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| author | Mohamad Reda. A. Refaai Vinjamuri SNCH Dattu N. Gireesh Ekta Dixit CH. Sandeep David Christopher |
| author_facet | Mohamad Reda. A. Refaai Vinjamuri SNCH Dattu N. Gireesh Ekta Dixit CH. Sandeep David Christopher |
| author_sort | Mohamad Reda. A. Refaai |
| collection | DOAJ |
| description | Unmanned aerial vehicles (UAVs), commonly known as drones, have been progressively prevalent due to their capability to operate quickly and their vast range of applications in a variety of real-world circumstances. The utilization of UAVs in precision farming has lately gained a lot of attention from the scientific community. This study addresses with the assistance of drones in the precision agricultural area. This paper makes significant contributions by analyzing communication protocols and applying them to the challenge of commanding a fleet of drones to protect crops from parasite infestations. In this research, the effectiveness of nine powerful deep neural network models is measured for the detection of plant diseases using diverse methodologies. These deep neural networks are adapted to the immediate situation using transfer learning and deep extraction of features approaches. The presented study takes into account the used pretrained deep learning model for extracting features and fine-tuning. The deep feature extraction characteristics are subsequently categorized using support vector machines (SVMs) and extreme learning machines (ELMs). For measuring performance, the precision, sensitivities, specific, and F1-score are all evaluated. Deep feature extraction and SVM/ELM classification generated better outcomes than transfer learning, according to the analysis result. Furthermore, the analysis of the various methodologies tries to assess their effectiveness and costs. The different approaches, for example, confront difficulties such as investigating the region in the shortest possible time feasible, while eliminating the same region being searched by more drones, detecting parasites, and stopping their spread by applying the appropriate number of pesticides. Simulation models are a significant aid to researchers in conducting to evaluate these technologies and creating specific tactics and coordinating procedures capable of effectively supporting farms and achieving the aim. The main objective of this paper is to compare the search techniques of two distinct methods of parasitic to identify performance. |
| format | Article |
| id | doaj-art-01a58d88d486407385a0f55f5504abdb |
| institution | Kabale University |
| issn | 1687-8442 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-01a58d88d486407385a0f55f5504abdb2025-02-03T05:57:22ZengWileyAdvances in Materials Science and Engineering1687-84422022-01-01202210.1155/2022/1160258Application of IoT-Based Drones in Precision Agriculture for Pest ControlMohamad Reda. A. Refaai0Vinjamuri SNCH Dattu1N. Gireesh2Ekta Dixit3CH. Sandeep4David Christopher5Department of Mechanical EngineeringDepartment of Mechanical EngineeringDepartment of Electronics and Communication EngineeringDepartment of Computer Science and EngineeringDepartment of Computer Science and Artificial IntelligenceDepartment of Mechanical EngineeringUnmanned aerial vehicles (UAVs), commonly known as drones, have been progressively prevalent due to their capability to operate quickly and their vast range of applications in a variety of real-world circumstances. The utilization of UAVs in precision farming has lately gained a lot of attention from the scientific community. This study addresses with the assistance of drones in the precision agricultural area. This paper makes significant contributions by analyzing communication protocols and applying them to the challenge of commanding a fleet of drones to protect crops from parasite infestations. In this research, the effectiveness of nine powerful deep neural network models is measured for the detection of plant diseases using diverse methodologies. These deep neural networks are adapted to the immediate situation using transfer learning and deep extraction of features approaches. The presented study takes into account the used pretrained deep learning model for extracting features and fine-tuning. The deep feature extraction characteristics are subsequently categorized using support vector machines (SVMs) and extreme learning machines (ELMs). For measuring performance, the precision, sensitivities, specific, and F1-score are all evaluated. Deep feature extraction and SVM/ELM classification generated better outcomes than transfer learning, according to the analysis result. Furthermore, the analysis of the various methodologies tries to assess their effectiveness and costs. The different approaches, for example, confront difficulties such as investigating the region in the shortest possible time feasible, while eliminating the same region being searched by more drones, detecting parasites, and stopping their spread by applying the appropriate number of pesticides. Simulation models are a significant aid to researchers in conducting to evaluate these technologies and creating specific tactics and coordinating procedures capable of effectively supporting farms and achieving the aim. The main objective of this paper is to compare the search techniques of two distinct methods of parasitic to identify performance.http://dx.doi.org/10.1155/2022/1160258 |
| spellingShingle | Mohamad Reda. A. Refaai Vinjamuri SNCH Dattu N. Gireesh Ekta Dixit CH. Sandeep David Christopher Application of IoT-Based Drones in Precision Agriculture for Pest Control Advances in Materials Science and Engineering |
| title | Application of IoT-Based Drones in Precision Agriculture for Pest Control |
| title_full | Application of IoT-Based Drones in Precision Agriculture for Pest Control |
| title_fullStr | Application of IoT-Based Drones in Precision Agriculture for Pest Control |
| title_full_unstemmed | Application of IoT-Based Drones in Precision Agriculture for Pest Control |
| title_short | Application of IoT-Based Drones in Precision Agriculture for Pest Control |
| title_sort | application of iot based drones in precision agriculture for pest control |
| url | http://dx.doi.org/10.1155/2022/1160258 |
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