A Robust YOLOv5 Model with SE Attention and BIFPN for Jishan Jujube Detection in Complex Agricultural Environments Article Swipe
Hao Chen
,
Lijun Su
,
Yi-Cheng Tian
,
Chai YiXin
,
Gang Hu
,
Weiyi Mu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/agriculture15060665
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/agriculture15060665
This study presents an improved detection model based on the YOLOv5 (You Only Look Once version 5) framework to enhance the accuracy of Jishan jujube detection in complex natural environments, particularly with varying degrees of occlusion and dense foliage. To improve detection performance, we integrate an SE (squeeze-and-excitation) attention module into the backbone network to enhance the model’s ability to focus on target objects while suppressing background noise. Additionally, the original neck network is replaced with a BIFPN (bi-directional feature pyramid network) structure, enabling efficient multiscale feature fusion and improving the extraction of critical features, especially for small and occluded fruits. The experimental results demonstrate that the improved YOLOv5 model achieves a mean average precision (mAP) of 96.5%, outperforming the YOLOv3, YOLOv4, YOLOv5, and SSD (Single-Shot Multibox Detector) models by 7.4%, 9.9%, 2.5%, and 0.8%, respectively. Furthermore, the proposed model improves precision (95.8%) and F1 score (92.4%), reducing false positives and achieving a better balance between precision and recall. These results highlight the model’s effectiveness in addressing missed detections of small and occluded fruits while maintaining higher confidence in predictions.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/agriculture15060665
- https://www.mdpi.com/2077-0472/15/6/665/pdf?version=1742483169
- OA Status
- gold
- Cited By
- 3
- References
- 14
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- 10
- OpenAlex ID
- https://openalex.org/W4408658429
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4408658429Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/agriculture15060665Digital Object Identifier
- Title
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A Robust YOLOv5 Model with SE Attention and BIFPN for Jishan Jujube Detection in Complex Agricultural EnvironmentsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-03-20Full publication date if available
- Authors
-
Hao Chen, Lijun Su, Yi-Cheng Tian, Chai YiXin, Gang Hu, Weiyi MuList of authors in order
- Landing page
-
https://doi.org/10.3390/agriculture15060665Publisher landing page
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https://www.mdpi.com/2077-0472/15/6/665/pdf?version=1742483169Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2077-0472/15/6/665/pdf?version=1742483169Direct OA link when available
- Concepts
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Agriculture, Environmental science, Biology, EcologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3Per-year citation counts (last 5 years)
- References (count)
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14Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.higher | 176 |
| abstract_inverted_index.jujube | 24 |
| abstract_inverted_index.missed | 167 |
| abstract_inverted_index.models | 128 |
| abstract_inverted_index.module | 49 |
| abstract_inverted_index.noise. | 67 |
| abstract_inverted_index.target | 62 |
| abstract_inverted_index.(95.8%) | 142 |
| abstract_inverted_index.YOLOv3, | 120 |
| abstract_inverted_index.YOLOv4, | 121 |
| abstract_inverted_index.YOLOv5, | 122 |
| abstract_inverted_index.ability | 58 |
| abstract_inverted_index.average | 113 |
| abstract_inverted_index.balance | 154 |
| abstract_inverted_index.between | 155 |
| abstract_inverted_index.complex | 27 |
| abstract_inverted_index.degrees | 33 |
| abstract_inverted_index.enhance | 19, 55 |
| abstract_inverted_index.feature | 79, 86 |
| abstract_inverted_index.fruits. | 100 |
| abstract_inverted_index.improve | 40 |
| abstract_inverted_index.natural | 28 |
| abstract_inverted_index.network | 53, 72 |
| abstract_inverted_index.objects | 63 |
| abstract_inverted_index.pyramid | 80 |
| abstract_inverted_index.recall. | 158 |
| abstract_inverted_index.results | 103, 160 |
| abstract_inverted_index.varying | 32 |
| abstract_inverted_index.version | 15 |
| abstract_inverted_index.(92.4%), | 146 |
| abstract_inverted_index.Multibox | 126 |
| abstract_inverted_index.accuracy | 21 |
| abstract_inverted_index.achieves | 110 |
| abstract_inverted_index.backbone | 52 |
| abstract_inverted_index.critical | 93 |
| abstract_inverted_index.enabling | 83 |
| abstract_inverted_index.foliage. | 38 |
| abstract_inverted_index.improved | 4, 107 |
| abstract_inverted_index.improves | 140 |
| abstract_inverted_index.network) | 81 |
| abstract_inverted_index.occluded | 99, 172 |
| abstract_inverted_index.original | 70 |
| abstract_inverted_index.presents | 2 |
| abstract_inverted_index.proposed | 138 |
| abstract_inverted_index.reducing | 147 |
| abstract_inverted_index.replaced | 74 |
| abstract_inverted_index.Detector) | 127 |
| abstract_inverted_index.achieving | 151 |
| abstract_inverted_index.attention | 48 |
| abstract_inverted_index.detection | 5, 25, 41 |
| abstract_inverted_index.efficient | 84 |
| abstract_inverted_index.features, | 94 |
| abstract_inverted_index.framework | 17 |
| abstract_inverted_index.highlight | 161 |
| abstract_inverted_index.improving | 89 |
| abstract_inverted_index.integrate | 44 |
| abstract_inverted_index.model’s | 57, 163 |
| abstract_inverted_index.occlusion | 35 |
| abstract_inverted_index.positives | 149 |
| abstract_inverted_index.precision | 114, 141, 156 |
| abstract_inverted_index.addressing | 166 |
| abstract_inverted_index.background | 66 |
| abstract_inverted_index.confidence | 177 |
| abstract_inverted_index.detections | 168 |
| abstract_inverted_index.especially | 95 |
| abstract_inverted_index.extraction | 91 |
| abstract_inverted_index.multiscale | 85 |
| abstract_inverted_index.structure, | 82 |
| abstract_inverted_index.demonstrate | 104 |
| abstract_inverted_index.maintaining | 175 |
| abstract_inverted_index.suppressing | 65 |
| abstract_inverted_index.(Single-Shot | 125 |
| abstract_inverted_index.Furthermore, | 136 |
| abstract_inverted_index.experimental | 102 |
| abstract_inverted_index.particularly | 30 |
| abstract_inverted_index.performance, | 42 |
| abstract_inverted_index.predictions. | 179 |
| abstract_inverted_index.Additionally, | 68 |
| abstract_inverted_index.effectiveness | 164 |
| abstract_inverted_index.environments, | 29 |
| abstract_inverted_index.outperforming | 118 |
| abstract_inverted_index.respectively. | 135 |
| abstract_inverted_index.(bi-directional | 78 |
| abstract_inverted_index.(squeeze-and-excitation) | 47 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 96 |
| corresponding_author_ids | https://openalex.org/A5075421686 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I4210131919 |
| citation_normalized_percentile.value | 0.94613927 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |