A Knowledge-Enhanced Object Detection for Sustainable Agriculture
The integration of autonomous aerial vehicles (AAVs) in agriculture has advanced precision farming by enhancing the ability to monitor and optimize agricultural plots. Object detection—critical for identifying crops, pests, and diseases–presents challenges due to data availabil...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10752099/ |
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| author | Youcef Djenouri Ahmed Nabil Belbachir Tomasz Michalak Asma Belhadi Gautam Srivastava |
| author_facet | Youcef Djenouri Ahmed Nabil Belbachir Tomasz Michalak Asma Belhadi Gautam Srivastava |
| author_sort | Youcef Djenouri |
| collection | DOAJ |
| description | The integration of autonomous aerial vehicles (AAVs) in agriculture has advanced precision farming by enhancing the ability to monitor and optimize agricultural plots. Object detection—critical for identifying crops, pests, and diseases–presents challenges due to data availability and varying environmental conditions. To address these challenges, we propose a Deep Learning framework tailored to agricultural contexts, utilizing domain-specific knowledge from AAV imagery. Our framework uses a knowledge base of visual features and loss values from multiple deep-learning models during the training phase to choose the most effective model for the testing phase. This approach improves model adaptability and accuracy across diverse agricultural scenarios. Evaluated on a comprehensive dataset of AAV-captured images covering various crop types and conditions, our model shows superior performance compared to state-of-the-art techniques. This demonstrates the value of integrating domain knowledge into deep learning for enhancing object detection, ultimately advancing agricultural efficiency, supporting sustainable resource management, and reducing environmental impact. |
| format | Article |
| id | doaj-art-becafd848329457f8a24cd26f81e158c |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-becafd848329457f8a24cd26f81e158c2025-08-20T02:21:02ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-011872874010.1109/JSTARS.2024.349757610752099A Knowledge-Enhanced Object Detection for Sustainable AgricultureYoucef Djenouri0https://orcid.org/0000-0001-5140-1224Ahmed Nabil Belbachir1https://orcid.org/0000-0001-9233-3723Tomasz Michalak2https://orcid.org/0000-0002-5288-0324Asma Belhadi3Gautam Srivastava4https://orcid.org/0000-0001-9851-4103Norwegian Research Center, University of South-Eastern Norway, Oslo, NorwayNorwegian Research Center, Grimstad, NorwayWarsaw University, Warsaw, PolandOsloMet University, Oslo, NorwayDepartment of Math and Computer Science, Brandon University, Brandon, MB, CanadaThe integration of autonomous aerial vehicles (AAVs) in agriculture has advanced precision farming by enhancing the ability to monitor and optimize agricultural plots. Object detection—critical for identifying crops, pests, and diseases–presents challenges due to data availability and varying environmental conditions. To address these challenges, we propose a Deep Learning framework tailored to agricultural contexts, utilizing domain-specific knowledge from AAV imagery. Our framework uses a knowledge base of visual features and loss values from multiple deep-learning models during the training phase to choose the most effective model for the testing phase. This approach improves model adaptability and accuracy across diverse agricultural scenarios. Evaluated on a comprehensive dataset of AAV-captured images covering various crop types and conditions, our model shows superior performance compared to state-of-the-art techniques. This demonstrates the value of integrating domain knowledge into deep learning for enhancing object detection, ultimately advancing agricultural efficiency, supporting sustainable resource management, and reducing environmental impact.https://ieeexplore.ieee.org/document/10752099/Agricultureknowledge guided deep learningobject detectionremote sensingsustainability |
| spellingShingle | Youcef Djenouri Ahmed Nabil Belbachir Tomasz Michalak Asma Belhadi Gautam Srivastava A Knowledge-Enhanced Object Detection for Sustainable Agriculture IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Agriculture knowledge guided deep learning object detection remote sensing sustainability |
| title | A Knowledge-Enhanced Object Detection for Sustainable Agriculture |
| title_full | A Knowledge-Enhanced Object Detection for Sustainable Agriculture |
| title_fullStr | A Knowledge-Enhanced Object Detection for Sustainable Agriculture |
| title_full_unstemmed | A Knowledge-Enhanced Object Detection for Sustainable Agriculture |
| title_short | A Knowledge-Enhanced Object Detection for Sustainable Agriculture |
| title_sort | knowledge enhanced object detection for sustainable agriculture |
| topic | Agriculture knowledge guided deep learning object detection remote sensing sustainability |
| url | https://ieeexplore.ieee.org/document/10752099/ |
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