Identification of Grass Weed Species Using YOLO5 Algorithm
Grass weeds are considered one of the major pests that pose a challenge to agricultural activity as they consume nutrients, space, and water. With advancements in technology, these pests can be identified and removed. Using computer vision techniques, we developed a grass weed management and control...
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| Format: | Article |
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MDPI AG
2025-05-01
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| Series: | Engineering Proceedings |
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| Online Access: | https://www.mdpi.com/2673-4591/92/1/86 |
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| author | Charlene Grace Rabulan John Alfred Gascon Noel Linsangan |
| author_facet | Charlene Grace Rabulan John Alfred Gascon Noel Linsangan |
| author_sort | Charlene Grace Rabulan |
| collection | DOAJ |
| description | Grass weeds are considered one of the major pests that pose a challenge to agricultural activity as they consume nutrients, space, and water. With advancements in technology, these pests can be identified and removed. Using computer vision techniques, we developed a grass weed management and control method. Identifying the species of grass weeds enables the correct selection of weed control measures and decreases the use of herbicides and weedicides. The YOLOv5 algorithm was used in this study. It was trained using training images that were also captured as part of this study. These images were then augmented, and Raspberry Pi was adopted to create a portable system. By successfully training the YOLOv5 algorithm on four different types of grass weeds, the system achieved an overall accuracy rate of 95.31% in detecting and identifying the target objects. The developed system detects and identifies the four main types of weeds, contributing to the improvement of weed control management. |
| format | Article |
| id | doaj-art-a348db6e2f174d3191b3d875477b9467 |
| institution | OA Journals |
| issn | 2673-4591 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-a348db6e2f174d3191b3d875477b94672025-08-20T02:21:09ZengMDPI AGEngineering Proceedings2673-45912025-05-019218610.3390/engproc2025092086Identification of Grass Weed Species Using YOLO5 AlgorithmCharlene Grace Rabulan0John Alfred Gascon1Noel Linsangan2School of Electrical, Electronics, and Computer Engineering, Mapúa University, Manila 1002, PhilippinesSchool of Electrical, Electronics, and Computer Engineering, Mapúa University, Manila 1002, PhilippinesSchool of Electrical, Electronics, and Computer Engineering, Mapúa University, Manila 1002, PhilippinesGrass weeds are considered one of the major pests that pose a challenge to agricultural activity as they consume nutrients, space, and water. With advancements in technology, these pests can be identified and removed. Using computer vision techniques, we developed a grass weed management and control method. Identifying the species of grass weeds enables the correct selection of weed control measures and decreases the use of herbicides and weedicides. The YOLOv5 algorithm was used in this study. It was trained using training images that were also captured as part of this study. These images were then augmented, and Raspberry Pi was adopted to create a portable system. By successfully training the YOLOv5 algorithm on four different types of grass weeds, the system achieved an overall accuracy rate of 95.31% in detecting and identifying the target objects. The developed system detects and identifies the four main types of weeds, contributing to the improvement of weed control management.https://www.mdpi.com/2673-4591/92/1/86grass weed identificationYOLO5image processingRaspberry PIconfusion matrix |
| spellingShingle | Charlene Grace Rabulan John Alfred Gascon Noel Linsangan Identification of Grass Weed Species Using YOLO5 Algorithm Engineering Proceedings grass weed identification YOLO5 image processing Raspberry PI confusion matrix |
| title | Identification of Grass Weed Species Using YOLO5 Algorithm |
| title_full | Identification of Grass Weed Species Using YOLO5 Algorithm |
| title_fullStr | Identification of Grass Weed Species Using YOLO5 Algorithm |
| title_full_unstemmed | Identification of Grass Weed Species Using YOLO5 Algorithm |
| title_short | Identification of Grass Weed Species Using YOLO5 Algorithm |
| title_sort | identification of grass weed species using yolo5 algorithm |
| topic | grass weed identification YOLO5 image processing Raspberry PI confusion matrix |
| url | https://www.mdpi.com/2673-4591/92/1/86 |
| work_keys_str_mv | AT charlenegracerabulan identificationofgrassweedspeciesusingyolo5algorithm AT johnalfredgascon identificationofgrassweedspeciesusingyolo5algorithm AT noellinsangan identificationofgrassweedspeciesusingyolo5algorithm |