Accurate Identification of Grade of Grape Damage by Brevipalpus spp. Based on the Improved YOLOv8n Model Article Swipe
Chaoxue Wang
,
Wenxi Tian
,
Gang Ma
,
Liang Zhu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/app15052712
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/app15052712
Brevipalpus spp. are widespread pests on wine grapes in northwest China and have generated a major threat to the local wine grape industry in recent years. We advanced the YOLOv8n model (object detection algorithm), termed SEM-YOLOv8n, to predict the degree of damage from these mites, and thereby provided the appropriate time for pest management. The damage symptoms of Brevipalpus spp. were classified into the following five grades: non-infested, slight, moderate, severe, and extremely severe; the pictures of different grades were structured into a self-constructed dataset. Regarding algorithm improvements, to improve the ability to recognize subtle differences between the various grades of damage symptoms in complex natural backgrounds, the EMA attention mechanism was introduced after the SPPF layer of the backbone network. Secondly, to address the problem of target omission caused by grapevine fruit overlapping, the MPDIoU loss function was used instead of the CIoU loss function. Finally, the Slim-Neck structure was adopted in the neck of YOLOv8n to generate a lightweight model. The experimental results showed that the improved model increased the mean accuracy by 1.1% and decreased the number of parameters by about 13.3% compared with the original model. Compared with the related authoritative YOLO series algorithms, the improved model proposed in this study had a better detection performance in terms of both the accuracy and model size.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app15052712
- https://www.mdpi.com/2076-3417/15/5/2712/pdf?version=1741015244
- OA Status
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- References
- 25
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- OpenAlex ID
- https://openalex.org/W4408123703
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4408123703Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/app15052712Digital Object Identifier
- Title
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Accurate Identification of Grade of Grape Damage by Brevipalpus spp. Based on the Improved YOLOv8n ModelWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-03-03Full publication date if available
- Authors
-
Chaoxue Wang, Wenxi Tian, Gang Ma, Liang ZhuList of authors in order
- Landing page
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https://doi.org/10.3390/app15052712Publisher landing page
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https://www.mdpi.com/2076-3417/15/5/2712/pdf?version=1741015244Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2076-3417/15/5/2712/pdf?version=1741015244Direct OA link when available
- Concepts
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Identification (biology), Horticulture, Biology, BotanyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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25Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.model | 30, 169, 200, 217 |
| abstract_inverted_index.pests | 4 |
| abstract_inverted_index.size. | 218 |
| abstract_inverted_index.study | 204 |
| abstract_inverted_index.terms | 211 |
| abstract_inverted_index.these | 43 |
| abstract_inverted_index.MPDIoU | 135 |
| abstract_inverted_index.better | 207 |
| abstract_inverted_index.caused | 129 |
| abstract_inverted_index.damage | 41, 55, 101 |
| abstract_inverted_index.degree | 39 |
| abstract_inverted_index.grades | 78, 99 |
| abstract_inverted_index.grapes | 7 |
| abstract_inverted_index.mites, | 44 |
| abstract_inverted_index.model. | 161, 189 |
| abstract_inverted_index.number | 179 |
| abstract_inverted_index.recent | 24 |
| abstract_inverted_index.series | 196 |
| abstract_inverted_index.showed | 165 |
| abstract_inverted_index.subtle | 94 |
| abstract_inverted_index.target | 127 |
| abstract_inverted_index.termed | 34 |
| abstract_inverted_index.threat | 16 |
| abstract_inverted_index.years. | 25 |
| abstract_inverted_index.(object | 31 |
| abstract_inverted_index.YOLOv8n | 29, 156 |
| abstract_inverted_index.ability | 91 |
| abstract_inverted_index.address | 123 |
| abstract_inverted_index.adopted | 151 |
| abstract_inverted_index.between | 96 |
| abstract_inverted_index.complex | 104 |
| abstract_inverted_index.grades: | 66 |
| abstract_inverted_index.improve | 89 |
| abstract_inverted_index.instead | 140 |
| abstract_inverted_index.natural | 105 |
| abstract_inverted_index.predict | 37 |
| abstract_inverted_index.problem | 125 |
| abstract_inverted_index.related | 193 |
| abstract_inverted_index.results | 164 |
| abstract_inverted_index.severe, | 70 |
| abstract_inverted_index.severe; | 73 |
| abstract_inverted_index.slight, | 68 |
| abstract_inverted_index.thereby | 46 |
| abstract_inverted_index.various | 98 |
| abstract_inverted_index.Compared | 190 |
| abstract_inverted_index.Finally, | 146 |
| abstract_inverted_index.accuracy | 173, 215 |
| abstract_inverted_index.advanced | 27 |
| abstract_inverted_index.backbone | 119 |
| abstract_inverted_index.compared | 185 |
| abstract_inverted_index.dataset. | 84 |
| abstract_inverted_index.function | 137 |
| abstract_inverted_index.generate | 158 |
| abstract_inverted_index.improved | 168, 199 |
| abstract_inverted_index.industry | 22 |
| abstract_inverted_index.network. | 120 |
| abstract_inverted_index.omission | 128 |
| abstract_inverted_index.original | 188 |
| abstract_inverted_index.pictures | 75 |
| abstract_inverted_index.proposed | 201 |
| abstract_inverted_index.provided | 47 |
| abstract_inverted_index.symptoms | 56, 102 |
| abstract_inverted_index.Regarding | 85 |
| abstract_inverted_index.Secondly, | 121 |
| abstract_inverted_index.Slim-Neck | 148 |
| abstract_inverted_index.algorithm | 86 |
| abstract_inverted_index.attention | 109 |
| abstract_inverted_index.decreased | 177 |
| abstract_inverted_index.detection | 32, 208 |
| abstract_inverted_index.different | 77 |
| abstract_inverted_index.extremely | 72 |
| abstract_inverted_index.following | 64 |
| abstract_inverted_index.function. | 145 |
| abstract_inverted_index.generated | 13 |
| abstract_inverted_index.grapevine | 131 |
| abstract_inverted_index.increased | 170 |
| abstract_inverted_index.mechanism | 110 |
| abstract_inverted_index.moderate, | 69 |
| abstract_inverted_index.northwest | 9 |
| abstract_inverted_index.recognize | 93 |
| abstract_inverted_index.structure | 149 |
| abstract_inverted_index.classified | 61 |
| abstract_inverted_index.introduced | 112 |
| abstract_inverted_index.parameters | 181 |
| abstract_inverted_index.structured | 80 |
| abstract_inverted_index.widespread | 3 |
| abstract_inverted_index.Brevipalpus | 0, 58 |
| abstract_inverted_index.algorithm), | 33 |
| abstract_inverted_index.algorithms, | 197 |
| abstract_inverted_index.appropriate | 49 |
| abstract_inverted_index.differences | 95 |
| abstract_inverted_index.lightweight | 160 |
| abstract_inverted_index.management. | 53 |
| abstract_inverted_index.performance | 209 |
| abstract_inverted_index.SEM-YOLOv8n, | 35 |
| abstract_inverted_index.backgrounds, | 106 |
| abstract_inverted_index.experimental | 163 |
| abstract_inverted_index.overlapping, | 133 |
| abstract_inverted_index.authoritative | 194 |
| abstract_inverted_index.improvements, | 87 |
| abstract_inverted_index.non-infested, | 67 |
| abstract_inverted_index.self-constructed | 83 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.07699055 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |