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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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/92/1/86 |
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| Summary: | 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. |
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| ISSN: | 2673-4591 |