Object detection model for mango leaf diseases / Muhammad Norzakwan Mohd Sham and Mohammad Hafiz Ismail
Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. A deep learning method was used to develop a leaf disease object detection model. However, this project...
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| Format: | Book Section |
| Language: | en |
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College of Computing, Informatics and Media, UiTM Perlis
2023
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| Online Access: | https://ir.uitm.edu.my/id/eprint/100532/1/100532.pdf https://ir.uitm.edu.my/id/eprint/100532/ |
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| _version_ | 1833321772929253376 |
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| author | Mohd Sham, Muhammad Norzakwan Ismail, Mohammad Hafiz |
| author_facet | Mohd Sham, Muhammad Norzakwan Ismail, Mohammad Hafiz |
| author_sort | Mohd Sham, Muhammad Norzakwan |
| building | Tun Abdul Razak Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Mara |
| content_source | UiTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. A deep learning method was used to develop a leaf disease object detection model. However, this project will focus on collecting datasets mango leaf disease images samples from UiTM Harumanis mango tree farm. In addition, this object detection model for mango leaf diseases used the techniques mean average precision (mAP) to performance accuracy and speed of the algorithm. This project would detect mango tree growers' leaf diseases using the YOLOv4 darknet. This model can also be utilised by homeowners that grow mango trees. On object detection, farmers can detect leaf diseases like black sooty molds and white wax scales earlier and treat them. Thus, leaf disease-detecting projects will use this feature to help the users facing this leaf disease problem. This object detection model will also benefit mango farmers and agriculture students. This study will also help farmers monitor several mango trees rapidly. |
| format | Book Section |
| id | my.uitm.ir-100532 |
| institution | Universiti Teknologi Mara |
| language | en |
| publishDate | 2023 |
| publisher | College of Computing, Informatics and Media, UiTM Perlis |
| record_format | eprints |
| spelling | my.uitm.ir-1005322024-09-27T08:49:31Z https://ir.uitm.edu.my/id/eprint/100532/ Object detection model for mango leaf diseases / Muhammad Norzakwan Mohd Sham and Mohammad Hafiz Ismail Mohd Sham, Muhammad Norzakwan Ismail, Mohammad Hafiz Machine learning Neural networks (Computer science) Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. A deep learning method was used to develop a leaf disease object detection model. However, this project will focus on collecting datasets mango leaf disease images samples from UiTM Harumanis mango tree farm. In addition, this object detection model for mango leaf diseases used the techniques mean average precision (mAP) to performance accuracy and speed of the algorithm. This project would detect mango tree growers' leaf diseases using the YOLOv4 darknet. This model can also be utilised by homeowners that grow mango trees. On object detection, farmers can detect leaf diseases like black sooty molds and white wax scales earlier and treat them. Thus, leaf disease-detecting projects will use this feature to help the users facing this leaf disease problem. This object detection model will also benefit mango farmers and agriculture students. This study will also help farmers monitor several mango trees rapidly. College of Computing, Informatics and Media, UiTM Perlis 2023 Book Section PeerReviewed text en https://ir.uitm.edu.my/id/eprint/100532/1/100532.pdf Object detection model for mango leaf diseases / Muhammad Norzakwan Mohd Sham and Mohammad Hafiz Ismail. (2023) In: Research Exhibition in Mathematics and Computer Sciences (REMACS 5.0). College of Computing, Informatics and Media, UiTM Perlis, pp. 89-90. ISBN 978-629-97934-0-3 |
| spellingShingle | Machine learning Neural networks (Computer science) Mohd Sham, Muhammad Norzakwan Ismail, Mohammad Hafiz Object detection model for mango leaf diseases / Muhammad Norzakwan Mohd Sham and Mohammad Hafiz Ismail |
| title | Object detection model for mango leaf diseases / Muhammad Norzakwan Mohd Sham and Mohammad Hafiz Ismail |
| title_full | Object detection model for mango leaf diseases / Muhammad Norzakwan Mohd Sham and Mohammad Hafiz Ismail |
| title_fullStr | Object detection model for mango leaf diseases / Muhammad Norzakwan Mohd Sham and Mohammad Hafiz Ismail |
| title_full_unstemmed | Object detection model for mango leaf diseases / Muhammad Norzakwan Mohd Sham and Mohammad Hafiz Ismail |
| title_short | Object detection model for mango leaf diseases / Muhammad Norzakwan Mohd Sham and Mohammad Hafiz Ismail |
| title_sort | object detection model for mango leaf diseases / muhammad norzakwan mohd sham and mohammad hafiz ismail |
| topic | Machine learning Neural networks (Computer science) |
| url | https://ir.uitm.edu.my/id/eprint/100532/1/100532.pdf https://ir.uitm.edu.my/id/eprint/100532/ |
| url_provider | http://ir.uitm.edu.my/ |