Using Remote Sensing and an Unmanned Aerial System for Weed Management in Agricultural Crops: A Review
Weeds are unwanted plants that can reduce crop yields by competing for water, nutrients, light, space, and carbon dioxide, which need to be controlled to meet future food production requirements. The integration of drones, artificial intelligence, and various sensors, which include hyperspectral, mu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/11/9/1809 |
| _version_ | 1797520638110334976 |
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| author | Muhammad Huzaifah Mohd Roslim Abdul Shukor Juraimi Nik Norasma Che’Ya Nursyazyla Sulaiman Muhammad Noor Hazwan Abd Manaf Zaid Ramli Mst. Motmainna |
| author_facet | Muhammad Huzaifah Mohd Roslim Abdul Shukor Juraimi Nik Norasma Che’Ya Nursyazyla Sulaiman Muhammad Noor Hazwan Abd Manaf Zaid Ramli Mst. Motmainna |
| author_sort | Muhammad Huzaifah Mohd Roslim |
| collection | DOAJ |
| description | Weeds are unwanted plants that can reduce crop yields by competing for water, nutrients, light, space, and carbon dioxide, which need to be controlled to meet future food production requirements. The integration of drones, artificial intelligence, and various sensors, which include hyperspectral, multi-spectral, and RGB (red-green-blue), ensure the possibility of a better outcome in managing weed problems. Most of the major or minor challenges caused by weed infestation can be faced by implementing remote sensing systems in various agricultural tasks. It is a multi-disciplinary science that includes spectroscopy, optics, computer, photography, satellite launching, electronics, communication, and several other fields. Future challenges, including food security, sustainability, supply and demand, climate change, and herbicide resistance, can also be overcome by those technologies based on machine learning approaches. This review provides an overview of the potential and practical use of unmanned aerial vehicle and remote sensing techniques in weed management practices and discusses how they overcome future challenges. |
| first_indexed | 2024-03-10T07:59:30Z |
| format | Article |
| id | doaj.art-dde86dc400be44b58439783e7dba19ef |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-10T07:59:30Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-dde86dc400be44b58439783e7dba19ef2023-11-22T11:38:40ZengMDPI AGAgronomy2073-43952021-09-01119180910.3390/agronomy11091809Using Remote Sensing and an Unmanned Aerial System for Weed Management in Agricultural Crops: A ReviewMuhammad Huzaifah Mohd Roslim0Abdul Shukor Juraimi1Nik Norasma Che’Ya2Nursyazyla Sulaiman3Muhammad Noor Hazwan Abd Manaf4Zaid Ramli5Mst. Motmainna6Department of Crop Science, Faculty of Agricultural Science and Forestry, Universiti Putra Malaysia Bintulu Campus, Bintulu 97000, Sarawak, MalaysiaDepartment of Crop Science, Faculty of Agriculture, Universiti Putra Malaysia, Serdang 43400, Selangor, MalaysiaDepartment of Agriculture Technology, Faculty of Agriculture, Universiti Putra Malaysia, Serdang 43400, Selangor, MalaysiaDepartment of Agriculture Technology, Faculty of Agriculture, Universiti Putra Malaysia, Serdang 43400, Selangor, MalaysiaDepartment of Crop Science, Faculty of Agriculture, Universiti Putra Malaysia, Serdang 43400, Selangor, MalaysiaDepartment of Crop Science, Faculty of Agriculture, Universiti Putra Malaysia, Serdang 43400, Selangor, MalaysiaDepartment of Crop Science, Faculty of Agriculture, Universiti Putra Malaysia, Serdang 43400, Selangor, MalaysiaWeeds are unwanted plants that can reduce crop yields by competing for water, nutrients, light, space, and carbon dioxide, which need to be controlled to meet future food production requirements. The integration of drones, artificial intelligence, and various sensors, which include hyperspectral, multi-spectral, and RGB (red-green-blue), ensure the possibility of a better outcome in managing weed problems. Most of the major or minor challenges caused by weed infestation can be faced by implementing remote sensing systems in various agricultural tasks. It is a multi-disciplinary science that includes spectroscopy, optics, computer, photography, satellite launching, electronics, communication, and several other fields. Future challenges, including food security, sustainability, supply and demand, climate change, and herbicide resistance, can also be overcome by those technologies based on machine learning approaches. This review provides an overview of the potential and practical use of unmanned aerial vehicle and remote sensing techniques in weed management practices and discusses how they overcome future challenges.https://www.mdpi.com/2073-4395/11/9/1809weedsartificial intelligencehyperspectralmulti-spectralweeds management |
| spellingShingle | Muhammad Huzaifah Mohd Roslim Abdul Shukor Juraimi Nik Norasma Che’Ya Nursyazyla Sulaiman Muhammad Noor Hazwan Abd Manaf Zaid Ramli Mst. Motmainna Using Remote Sensing and an Unmanned Aerial System for Weed Management in Agricultural Crops: A Review Agronomy weeds artificial intelligence hyperspectral multi-spectral weeds management |
| title | Using Remote Sensing and an Unmanned Aerial System for Weed Management in Agricultural Crops: A Review |
| title_full | Using Remote Sensing and an Unmanned Aerial System for Weed Management in Agricultural Crops: A Review |
| title_fullStr | Using Remote Sensing and an Unmanned Aerial System for Weed Management in Agricultural Crops: A Review |
| title_full_unstemmed | Using Remote Sensing and an Unmanned Aerial System for Weed Management in Agricultural Crops: A Review |
| title_short | Using Remote Sensing and an Unmanned Aerial System for Weed Management in Agricultural Crops: A Review |
| title_sort | using remote sensing and an unmanned aerial system for weed management in agricultural crops a review |
| topic | weeds artificial intelligence hyperspectral multi-spectral weeds management |
| url | https://www.mdpi.com/2073-4395/11/9/1809 |
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