Advancing agriculture with machine learning: a new frontier in weed management
Weed management is a crucial aspect of modern agriculture as invasive plants can negatively impact crop yields and profitability. Long-established methods of weed control, such as manual labor and synthetic herbicides, have been widely used but come with their own set of challenges. These methods ar...
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| Format: | Article |
| Language: | English |
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Higher Education Press
2025-06-01
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| Series: | Frontiers of Agricultural Science and Engineering |
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| Online Access: | https://journal.hep.com.cn/fase/EN/PDF/10.15302/J-FASE-2024564 |
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| author | Mohammad MEHDIZADEH Duraid K. A. AL-TAEY Anahita OMIDI Aljanabi Hadi Yasir ABBOOD Shavan ASKAR Soxibjon TOPILDIYEV Harikumar PALLATHADKA Renas Rajab ASAAD |
| author_facet | Mohammad MEHDIZADEH Duraid K. A. AL-TAEY Anahita OMIDI Aljanabi Hadi Yasir ABBOOD Shavan ASKAR Soxibjon TOPILDIYEV Harikumar PALLATHADKA Renas Rajab ASAAD |
| author_sort | Mohammad MEHDIZADEH |
| collection | DOAJ |
| description | Weed management is a crucial aspect of modern agriculture as invasive plants can negatively impact crop yields and profitability. Long-established methods of weed control, such as manual labor and synthetic herbicides, have been widely used but come with their own set of challenges. These methods are often time-consuming, labor-intensive, and pose environmental risks. Herbicides have been the primary method of weed control due to their efficiency and cost-effectiveness. However, over-reliance on herbicides has led to environmental contamination, weed resistance, and potential health hazards. To address these issues, researchers and industry experts are now exploring the integration of machine learning into chemical weed management strategies. As technology advances, there is a growing interest in exploring innovative and sustainable weed management approaches. This review examines the potential of machine learning in chemical weed management. Machine learning offers innovative and sustainable approaches by analyzing large data sets, recognizing patterns, and making accurate predictions. Machine learning models can classify weed species and optimize herbicide usage. Real-time monitoring enables timely intervention, preventing invasive species spread. Integrating machine learning into chemical weed management holds promise for enhancing agricultural practices, reducing herbicide usage and minimizing environmental impact. Validation and refinement of these algorithms are needed for practical application. |
| format | Article |
| id | doaj-art-ab93b9146e1a47a7a29b981eb82c3c7c |
| institution | OA Journals |
| issn | 2095-7505 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Higher Education Press |
| record_format | Article |
| series | Frontiers of Agricultural Science and Engineering |
| spelling | doaj-art-ab93b9146e1a47a7a29b981eb82c3c7c2025-08-20T02:14:10ZengHigher Education PressFrontiers of Agricultural Science and Engineering2095-75052025-06-0112228830710.15302/J-FASE-2024564Advancing agriculture with machine learning: a new frontier in weed managementMohammad MEHDIZADEH0Duraid K. A. AL-TAEY1Anahita OMIDI2Aljanabi Hadi Yasir ABBOOD3Shavan ASKAR4Soxibjon TOPILDIYEV5Harikumar PALLATHADKA6Renas Rajab ASAAD71. Department of Agronomy and Plant Breeding, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, 5619913131, Ardabil, Iran|2. Ilam Science and Technology Park, 6939177157, Ilam, Iran3. Department of Horticulture, College of Agriculture, University of Al-Qasim Green, 00964 Babylon, Iraq4. Department of GIS and Remote Sensing, Faculty of Geography, University of Tehran, 1417935840, Tehran, Iran5. Medical Laboratories Techniques Department, Al-Mustaqbal University College, 51001 Hillah, Iraq6. Erbil polytechnic university, Technical engineering college, information system engineering department, 44001 Erbil, Iraq7. Department of Fundamental Economics, Tashkent State University of Economics, 100066, Tashkent, Uzbekistan8. Manipur International University, Manipur 795140, Imphal, India9. Department of Computer Science, Nawroz University, PO BOX 77, Duhok, IraqWeed management is a crucial aspect of modern agriculture as invasive plants can negatively impact crop yields and profitability. Long-established methods of weed control, such as manual labor and synthetic herbicides, have been widely used but come with their own set of challenges. These methods are often time-consuming, labor-intensive, and pose environmental risks. Herbicides have been the primary method of weed control due to their efficiency and cost-effectiveness. However, over-reliance on herbicides has led to environmental contamination, weed resistance, and potential health hazards. To address these issues, researchers and industry experts are now exploring the integration of machine learning into chemical weed management strategies. As technology advances, there is a growing interest in exploring innovative and sustainable weed management approaches. This review examines the potential of machine learning in chemical weed management. Machine learning offers innovative and sustainable approaches by analyzing large data sets, recognizing patterns, and making accurate predictions. Machine learning models can classify weed species and optimize herbicide usage. Real-time monitoring enables timely intervention, preventing invasive species spread. Integrating machine learning into chemical weed management holds promise for enhancing agricultural practices, reducing herbicide usage and minimizing environmental impact. Validation and refinement of these algorithms are needed for practical application.https://journal.hep.com.cn/fase/EN/PDF/10.15302/J-FASE-2024564Weed managementherbicidesmachine learningagricultural practicesenvironmental impact |
| spellingShingle | Mohammad MEHDIZADEH Duraid K. A. AL-TAEY Anahita OMIDI Aljanabi Hadi Yasir ABBOOD Shavan ASKAR Soxibjon TOPILDIYEV Harikumar PALLATHADKA Renas Rajab ASAAD Advancing agriculture with machine learning: a new frontier in weed management Frontiers of Agricultural Science and Engineering Weed management herbicides machine learning agricultural practices environmental impact |
| title | Advancing agriculture with machine learning: a new frontier in weed management |
| title_full | Advancing agriculture with machine learning: a new frontier in weed management |
| title_fullStr | Advancing agriculture with machine learning: a new frontier in weed management |
| title_full_unstemmed | Advancing agriculture with machine learning: a new frontier in weed management |
| title_short | Advancing agriculture with machine learning: a new frontier in weed management |
| title_sort | advancing agriculture with machine learning a new frontier in weed management |
| topic | Weed management herbicides machine learning agricultural practices environmental impact |
| url | https://journal.hep.com.cn/fase/EN/PDF/10.15302/J-FASE-2024564 |
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