Integrating evolutionary algorithms and enhanced-YOLOv8 + for comprehensive apple ripeness prediction Article Swipe
Yuchi Li
,
Zhigao Wang
,
Anlong Yang
,
X. D. Yu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-91939-4
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-91939-4
The assessment of apple quality is pivotal in agricultural production management, and apple ripeness is a key determinant of apple quality. This paper proposes an approach for assessing apple ripeness from both structured and unstructured observation data, i.e., text and images. For structured text data, support vector regression (SVR) models optimized using the Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), and Sparrow Search Algorithm (SSA) were utilized to predict apple ripeness, with the WOA-optimized SVR demonstrating exceptional generalization capabilities. For unstructured image data, an Enhanced-YOLOv8+, a modified YOLOv8 architecture integrating Detect Efficient Head (DEH) and Efficient Channel Attention (ECA) mechanism, was employed for precise apple localization and ripeness identification. The synergistic application of these methods resulted in a significant improvement in prediction accuracy. These approaches provide a robust framework for apple quality assessment and deepen the understanding of the relationship between apple maturity and observed indicators, facilitating more informed decision-making in postharvest management.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-025-91939-4
- https://www.nature.com/articles/s41598-025-91939-4.pdf
- OA Status
- gold
- Cited By
- 2
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408070952
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4408070952Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1038/s41598-025-91939-4Digital Object Identifier
- Title
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Integrating evolutionary algorithms and enhanced-YOLOv8 + for comprehensive apple ripeness predictionWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-03-01Full publication date if available
- Authors
-
Yuchi Li, Zhigao Wang, Anlong Yang, X. D. YuList of authors in order
- Landing page
-
https://doi.org/10.1038/s41598-025-91939-4Publisher landing page
- PDF URL
-
https://www.nature.com/articles/s41598-025-91939-4.pdfDirect 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
-
https://www.nature.com/articles/s41598-025-91939-4.pdfDirect OA link when available
- Concepts
-
Ripeness, Computer science, Biology, Botany, RipeningTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2Per-year citation counts (last 5 years)
- References (count)
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32Number 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.apple | 3, 12, 19, 28, 70, 105, 131, 142 |
| abstract_inverted_index.data, | 36, 44, 83 |
| abstract_inverted_index.i.e., | 37 |
| abstract_inverted_index.image | 82 |
| abstract_inverted_index.paper | 22 |
| abstract_inverted_index.these | 114 |
| abstract_inverted_index.using | 51 |
| abstract_inverted_index.(GWO), | 60 |
| abstract_inverted_index.(WOA), | 56 |
| abstract_inverted_index.Detect | 91 |
| abstract_inverted_index.Search | 63 |
| abstract_inverted_index.YOLOv8 | 88 |
| abstract_inverted_index.deepen | 135 |
| abstract_inverted_index.models | 49 |
| abstract_inverted_index.robust | 128 |
| abstract_inverted_index.vector | 46 |
| abstract_inverted_index.Channel | 97 |
| abstract_inverted_index.Sparrow | 62 |
| abstract_inverted_index.between | 141 |
| abstract_inverted_index.images. | 40 |
| abstract_inverted_index.methods | 115 |
| abstract_inverted_index.pivotal | 6 |
| abstract_inverted_index.precise | 104 |
| abstract_inverted_index.predict | 69 |
| abstract_inverted_index.provide | 126 |
| abstract_inverted_index.quality | 4, 132 |
| abstract_inverted_index.support | 45 |
| abstract_inverted_index.approach | 25 |
| abstract_inverted_index.employed | 102 |
| abstract_inverted_index.informed | 149 |
| abstract_inverted_index.maturity | 143 |
| abstract_inverted_index.modified | 87 |
| abstract_inverted_index.observed | 145 |
| abstract_inverted_index.proposes | 23 |
| abstract_inverted_index.quality. | 20 |
| abstract_inverted_index.resulted | 116 |
| abstract_inverted_index.ripeness | 13, 29, 108 |
| abstract_inverted_index.utilized | 67 |
| abstract_inverted_index.Algorithm | 55, 64 |
| abstract_inverted_index.Attention | 98 |
| abstract_inverted_index.Efficient | 92, 96 |
| abstract_inverted_index.Optimizer | 59 |
| abstract_inverted_index.accuracy. | 123 |
| abstract_inverted_index.assessing | 27 |
| abstract_inverted_index.framework | 129 |
| abstract_inverted_index.optimized | 50 |
| abstract_inverted_index.ripeness, | 71 |
| abstract_inverted_index.approaches | 125 |
| abstract_inverted_index.assessment | 1, 133 |
| abstract_inverted_index.mechanism, | 100 |
| abstract_inverted_index.prediction | 122 |
| abstract_inverted_index.production | 9 |
| abstract_inverted_index.regression | 47 |
| abstract_inverted_index.structured | 32, 42 |
| abstract_inverted_index.application | 112 |
| abstract_inverted_index.determinant | 17 |
| abstract_inverted_index.exceptional | 77 |
| abstract_inverted_index.improvement | 120 |
| abstract_inverted_index.indicators, | 146 |
| abstract_inverted_index.integrating | 90 |
| abstract_inverted_index.management, | 10 |
| abstract_inverted_index.management. | 153 |
| abstract_inverted_index.observation | 35 |
| abstract_inverted_index.postharvest | 152 |
| abstract_inverted_index.significant | 119 |
| abstract_inverted_index.synergistic | 111 |
| abstract_inverted_index.Optimization | 54 |
| abstract_inverted_index.agricultural | 8 |
| abstract_inverted_index.architecture | 89 |
| abstract_inverted_index.facilitating | 147 |
| abstract_inverted_index.localization | 106 |
| abstract_inverted_index.relationship | 140 |
| abstract_inverted_index.unstructured | 34, 81 |
| abstract_inverted_index.WOA-optimized | 74 |
| abstract_inverted_index.capabilities. | 79 |
| abstract_inverted_index.demonstrating | 76 |
| abstract_inverted_index.understanding | 137 |
| abstract_inverted_index.generalization | 78 |
| abstract_inverted_index.decision-making | 150 |
| abstract_inverted_index.identification. | 109 |
| abstract_inverted_index.Enhanced-YOLOv8+, | 85 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/2 |
| sustainable_development_goals[0].score | 0.44999998807907104 |
| sustainable_development_goals[0].display_name | Zero hunger |
| citation_normalized_percentile.value | 0.94394305 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |