AI-Enabled Crop Management Framework for Pest Detection Using Visual Sensor Data
Our research focuses on addressing the challenge of crop diseases and pest infestations in agriculture by utilizing UAV technology for improved crop monitoring through unmanned aerial vehicles (UAVs) and enhancing the detection and classification of agricultural pests. Traditional approaches often r...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Plants |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2223-7747/13/5/653 |
| _version_ | 1797263992421351424 |
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| author | Asma Khan Sharaf J. Malebary L. Minh Dang Faisal Binzagr Hyoung-Kyu Song Hyeonjoon Moon |
| author_facet | Asma Khan Sharaf J. Malebary L. Minh Dang Faisal Binzagr Hyoung-Kyu Song Hyeonjoon Moon |
| author_sort | Asma Khan |
| collection | DOAJ |
| description | Our research focuses on addressing the challenge of crop diseases and pest infestations in agriculture by utilizing UAV technology for improved crop monitoring through unmanned aerial vehicles (UAVs) and enhancing the detection and classification of agricultural pests. Traditional approaches often require arduous manual feature extraction or computationally demanding deep learning (DL) techniques. To address this, we introduce an optimized model tailored specifically for UAV-based applications. Our alterations to the YOLOv5s model, which include advanced attention modules, expanded cross-stage partial network (CSP) modules, and refined multiscale feature extraction mechanisms, enable precise pest detection and classification. Inspired by the efficiency and versatility of UAVs, our study strives to revolutionize pest management in sustainable agriculture while also detecting and preventing crop diseases. We conducted rigorous testing on a medium-scale dataset, identifying five agricultural pests, namely ants, grasshoppers, palm weevils, shield bugs, and wasps. Our comprehensive experimental analysis showcases superior performance compared to various YOLOv5 model versions. The proposed model obtained higher performance, with an average precision of 96.0%, an average recall of 93.0%, and a mean average precision (mAP) of 95.0%. Furthermore, the inherent capabilities of UAVs, combined with the YOLOv5s model tested here, could offer a reliable solution for real-time pest detection, demonstrating significant potential to optimize and improve agricultural production within a drone-centric ecosystem. |
| first_indexed | 2024-04-25T00:21:49Z |
| format | Article |
| id | doaj.art-0d4974811ef74d4aa25eedd9fee595bd |
| institution | Directory Open Access Journal |
| issn | 2223-7747 |
| language | English |
| last_indexed | 2024-04-25T00:21:49Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Plants |
| spelling | doaj.art-0d4974811ef74d4aa25eedd9fee595bd2024-03-12T16:52:48ZengMDPI AGPlants2223-77472024-02-0113565310.3390/plants13050653AI-Enabled Crop Management Framework for Pest Detection Using Visual Sensor DataAsma Khan0Sharaf J. Malebary1L. Minh Dang2Faisal Binzagr3Hyoung-Kyu Song4Hyeonjoon Moon5Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of KoreaDepartment of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, P.O. Box 344, Rabigh 21911, Saudi ArabiaDepartment of Information and Communication Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of KoreaDepartment of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, P.O. Box 344, Rabigh 21911, Saudi ArabiaDepartment of Information and Communication Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of KoreaDepartment of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of KoreaOur research focuses on addressing the challenge of crop diseases and pest infestations in agriculture by utilizing UAV technology for improved crop monitoring through unmanned aerial vehicles (UAVs) and enhancing the detection and classification of agricultural pests. Traditional approaches often require arduous manual feature extraction or computationally demanding deep learning (DL) techniques. To address this, we introduce an optimized model tailored specifically for UAV-based applications. Our alterations to the YOLOv5s model, which include advanced attention modules, expanded cross-stage partial network (CSP) modules, and refined multiscale feature extraction mechanisms, enable precise pest detection and classification. Inspired by the efficiency and versatility of UAVs, our study strives to revolutionize pest management in sustainable agriculture while also detecting and preventing crop diseases. We conducted rigorous testing on a medium-scale dataset, identifying five agricultural pests, namely ants, grasshoppers, palm weevils, shield bugs, and wasps. Our comprehensive experimental analysis showcases superior performance compared to various YOLOv5 model versions. The proposed model obtained higher performance, with an average precision of 96.0%, an average recall of 93.0%, and a mean average precision (mAP) of 95.0%. Furthermore, the inherent capabilities of UAVs, combined with the YOLOv5s model tested here, could offer a reliable solution for real-time pest detection, demonstrating significant potential to optimize and improve agricultural production within a drone-centric ecosystem.https://www.mdpi.com/2223-7747/13/5/653convolution neural networkdeep learningsustainable agricultureUAV technologycomputer visionmonitoring system |
| spellingShingle | Asma Khan Sharaf J. Malebary L. Minh Dang Faisal Binzagr Hyoung-Kyu Song Hyeonjoon Moon AI-Enabled Crop Management Framework for Pest Detection Using Visual Sensor Data Plants convolution neural network deep learning sustainable agriculture UAV technology computer vision monitoring system |
| title | AI-Enabled Crop Management Framework for Pest Detection Using Visual Sensor Data |
| title_full | AI-Enabled Crop Management Framework for Pest Detection Using Visual Sensor Data |
| title_fullStr | AI-Enabled Crop Management Framework for Pest Detection Using Visual Sensor Data |
| title_full_unstemmed | AI-Enabled Crop Management Framework for Pest Detection Using Visual Sensor Data |
| title_short | AI-Enabled Crop Management Framework for Pest Detection Using Visual Sensor Data |
| title_sort | ai enabled crop management framework for pest detection using visual sensor data |
| topic | convolution neural network deep learning sustainable agriculture UAV technology computer vision monitoring system |
| url | https://www.mdpi.com/2223-7747/13/5/653 |
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