Digital image processing applications in agriculture with a machine learning approach
Abstract. Digital image processing involves the manipulation of images by digital means, and its use has been increasing exponentially in recent decades. It is applied in a diverse range of fields including medicine, remote sensing, robotic vision, and audiovisual processing. Image processing techno...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Trakia University. Faculty of Agriculture, Stara Zagora
2023-12-01
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| Series: | Agricultural Science and Technology |
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| Online Access: | https://agriscitech.eu/digital-image-processing-applications-in-agriculture-with-a-machine-learning-approach/ |
| _version_ | 1797306691611525120 |
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| author | Suraiya Yasmin Masum Billah |
| author_facet | Suraiya Yasmin Masum Billah |
| author_sort | Suraiya Yasmin |
| collection | DOAJ |
| description | Abstract. Digital image processing involves the manipulation of images by digital means, and its use has been increasing exponentially in recent decades. It is applied in a diverse range of fields including medicine, remote sensing, robotic vision, and audiovisual processing. Image processing technologies are widely used as a proficient tool in the agriculture sector. The combination of digital image processing and machine learning not only provides new insights into crop health and environmental circumstances, but also helps farmers reach timely and precise decisions. Using machine learning methods, this study examines digital image processing applications in agriculture, focusing on plant disease identification, fruit quality evaluation, weed detection, yield mapping, and robotic harvesting, to other issues. In essence, this study integrates existing knowledge at the interface of digital image processing, machine learning, and agriculture, providing insights into a promising and growing topic with significant implications for sustainable and resilient food production. |
| first_indexed | 2024-03-08T00:45:30Z |
| format | Article |
| id | doaj.art-c813010cbd484f6b93d0179c416e2b52 |
| institution | Directory Open Access Journal |
| issn | 1313-8820 1314-412X |
| language | English |
| last_indexed | 2024-03-08T00:45:30Z |
| publishDate | 2023-12-01 |
| publisher | Trakia University. Faculty of Agriculture, Stara Zagora |
| record_format | Article |
| series | Agricultural Science and Technology |
| spelling | doaj.art-c813010cbd484f6b93d0179c416e2b522024-02-15T12:11:44ZengTrakia University. Faculty of Agriculture, Stara ZagoraAgricultural Science and Technology1313-88201314-412X2023-12-01154122210.15547/ast.2023.04.033Digital image processing applications in agriculture with a machine learning approachSuraiya Yasmin 0Masum Billah 1Department of Computer Science and Information Technology, Faculty of Agriculture, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur-1706, Dhaka, BangladeshDepartment of Computer Science and Information Technology, Faculty of Agriculture, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur-1706, Dhaka, BangladeshAbstract. Digital image processing involves the manipulation of images by digital means, and its use has been increasing exponentially in recent decades. It is applied in a diverse range of fields including medicine, remote sensing, robotic vision, and audiovisual processing. Image processing technologies are widely used as a proficient tool in the agriculture sector. The combination of digital image processing and machine learning not only provides new insights into crop health and environmental circumstances, but also helps farmers reach timely and precise decisions. Using machine learning methods, this study examines digital image processing applications in agriculture, focusing on plant disease identification, fruit quality evaluation, weed detection, yield mapping, and robotic harvesting, to other issues. In essence, this study integrates existing knowledge at the interface of digital image processing, machine learning, and agriculture, providing insights into a promising and growing topic with significant implications for sustainable and resilient food production.https://agriscitech.eu/digital-image-processing-applications-in-agriculture-with-a-machine-learning-approach/image processingimage processingcomputer visionplant diseaserobotic harvestingmachine learningsmart farmingdeep learning |
| spellingShingle | Suraiya Yasmin Masum Billah Digital image processing applications in agriculture with a machine learning approach Agricultural Science and Technology image processing image processing computer vision plant disease robotic harvesting machine learning smart farming deep learning |
| title | Digital image processing applications in agriculture with a machine learning approach |
| title_full | Digital image processing applications in agriculture with a machine learning approach |
| title_fullStr | Digital image processing applications in agriculture with a machine learning approach |
| title_full_unstemmed | Digital image processing applications in agriculture with a machine learning approach |
| title_short | Digital image processing applications in agriculture with a machine learning approach |
| title_sort | digital image processing applications in agriculture with a machine learning approach |
| topic | image processing image processing computer vision plant disease robotic harvesting machine learning smart farming deep learning |
| url | https://agriscitech.eu/digital-image-processing-applications-in-agriculture-with-a-machine-learning-approach/ |
| work_keys_str_mv | AT suraiyayasmin digitalimageprocessingapplicationsinagriculturewithamachinelearningapproach AT masumbillah digitalimageprocessingapplicationsinagriculturewithamachinelearningapproach |