GDFC-YOLO: An Efficient Perception Detection Model for Precise Wheat Disease Recognition Article Swipe
Jiawei Qian
,
Chenxu Dai
,
Zhanlin Ji
,
Jinyun Liu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/agriculture15141526
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/agriculture15141526
Wheat disease detection is a crucial component of intelligent agricultural systems in modern agriculture. However, at present, its detection accuracy still has certain limitations. The existing models hardly capture the irregular and fine-grained texture features of the lesions, and the results of spatial information reconstruction caused by standard upsampling operations are inaccuracy. In this work, the GDFC-YOLO method is proposed to address these limitations and enhance the accuracy of detection. This method is based on YOLOv11 and encompasses three key aspects of improvement: (1) a newly designed Ghost Dynamic Feature Core (GDFC) in the backbone, which improves the efficiency of disease feature extraction and enhances the model’s ability to capture informative representations; (2) a redesigned neck structure, Disease-Focused Neck (DF-Neck), which further strengthens feature expressiveness, to improve multi-scale fusion and refine feature processing pipelines; and (3) the integration of the Powerful Intersection over Union v2 (PIoUv2) loss function to optimize the regression accuracy and convergence speed. The results showed that GDFC-YOLO improved the average accuracy from 0.86 to 0.90 when the cross-overmerge threshold was 0.5 ([email protected]), its accuracy reached 0.899, its recall rate reached 0.821, and it still maintained a structure with only 9.27 M parameters. From these results, it can be known that GDFC-YOLO has a good detection performance and stronger practicability relatively. It is a solution that can accurately and efficiently detect crop diseases in real agricultural scenarios.
Related Topics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/agriculture15141526
- OA Status
- gold
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412451208
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4412451208Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/agriculture15141526Digital Object Identifier
- Title
-
GDFC-YOLO: An Efficient Perception Detection Model for Precise Wheat Disease RecognitionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-07-15Full publication date if available
- Authors
-
Jiawei Qian, Chenxu Dai, Zhanlin Ji, Jinyun LiuList of authors in order
- Landing page
-
https://doi.org/10.3390/agriculture15141526Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3390/agriculture15141526Direct OA link when available
- Concepts
-
Perception, Biology, Computer science, Artificial intelligence, NeuroscienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
28Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4412451208 |
|---|---|
| doi | https://doi.org/10.3390/agriculture15141526 |
| ids.doi | https://doi.org/10.3390/agriculture15141526 |
| ids.openalex | https://openalex.org/W4412451208 |
| fwci | 0.0 |
| type | article |
| title | GDFC-YOLO: An Efficient Perception Detection Model for Precise Wheat Disease Recognition |
| awards[0].id | https://openalex.org/G2322810405 |
| awards[0].funder_id | https://openalex.org/F4320335777 |
| awards[0].display_name | |
| awards[0].funder_award_id | 2017YFE0135700 |
| awards[0].funder_display_name | National Key Research and Development Program of China |
| biblio.issue | 14 |
| biblio.volume | 15 |
| biblio.last_page | 1526 |
| biblio.first_page | 1526 |
| topics[0].id | https://openalex.org/T10616 |
| topics[0].field.id | https://openalex.org/fields/11 |
| topics[0].field.display_name | Agricultural and Biological Sciences |
| topics[0].score | 0.996399998664856 |
| topics[0].domain.id | https://openalex.org/domains/1 |
| topics[0].domain.display_name | Life Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1110 |
| topics[0].subfield.display_name | Plant Science |
| topics[0].display_name | Smart Agriculture and AI |
| topics[1].id | https://openalex.org/T10640 |
| topics[1].field.id | https://openalex.org/fields/16 |
| topics[1].field.display_name | Chemistry |
| topics[1].score | 0.9824000000953674 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1602 |
| topics[1].subfield.display_name | Analytical Chemistry |
| topics[1].display_name | Spectroscopy and Chemometric Analyses |
| topics[2].id | https://openalex.org/T11667 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9757000207901001 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2204 |
| topics[2].subfield.display_name | Biomedical Engineering |
| topics[2].display_name | Advanced Chemical Sensor Technologies |
| funders[0].id | https://openalex.org/F4320335777 |
| funders[0].ror | |
| funders[0].display_name | National Key Research and Development Program of China |
| is_xpac | False |
| apc_list.value | 1800 |
| apc_list.currency | CHF |
| apc_list.value_usd | 1949 |
| apc_paid.value | 1800 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 1949 |
| concepts[0].id | https://openalex.org/C26760741 |
| concepts[0].level | 2 |
| concepts[0].score | 0.5552929639816284 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q160402 |
| concepts[0].display_name | Perception |
| concepts[1].id | https://openalex.org/C86803240 |
| concepts[1].level | 0 |
| concepts[1].score | 0.38335224986076355 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[1].display_name | Biology |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.36899304389953613 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.36797022819519043 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C169760540 |
| concepts[4].level | 1 |
| concepts[4].score | 0.1309797465801239 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q207011 |
| concepts[4].display_name | Neuroscience |
| keywords[0].id | https://openalex.org/keywords/perception |
| keywords[0].score | 0.5552929639816284 |
| keywords[0].display_name | Perception |
| keywords[1].id | https://openalex.org/keywords/biology |
| keywords[1].score | 0.38335224986076355 |
| keywords[1].display_name | Biology |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.36899304389953613 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.36797022819519043 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/neuroscience |
| keywords[4].score | 0.1309797465801239 |
| keywords[4].display_name | Neuroscience |
| language | en |
| locations[0].id | doi:10.3390/agriculture15141526 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210202585 |
| locations[0].source.issn | 2077-0472 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2077-0472 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Agriculture |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Agriculture |
| locations[0].landing_page_url | https://doi.org/10.3390/agriculture15141526 |
| locations[1].id | pmh:oai:doaj.org/article:a72df029167542788627687dfb20bf6c |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306401280 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Agriculture, Vol 15, Iss 14, p 1526 (2025) |
| locations[1].landing_page_url | https://doaj.org/article/a72df029167542788627687dfb20bf6c |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5100806726 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Jiawei Qian |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I137506752 |
| authorships[0].affiliations[0].raw_affiliation_string | College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China |
| authorships[0].institutions[0].id | https://openalex.org/I137506752 |
| authorships[0].institutions[0].ror | https://ror.org/04z4wmb81 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I137506752 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | North China University of Science and Technology |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Jiawei Qian |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China |
| authorships[1].author.id | https://openalex.org/A5108149359 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Chenxu Dai |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I137506752 |
| authorships[1].affiliations[0].raw_affiliation_string | College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China |
| authorships[1].institutions[0].id | https://openalex.org/I137506752 |
| authorships[1].institutions[0].ror | https://ror.org/04z4wmb81 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I137506752 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | North China University of Science and Technology |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Chenxu Dai |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China |
| authorships[2].author.id | https://openalex.org/A5030607091 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-3527-3773 |
| authorships[2].author.display_name | Zhanlin Ji |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I1284762954 |
| authorships[2].affiliations[0].raw_affiliation_string | College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China |
| authorships[2].institutions[0].id | https://openalex.org/I1284762954 |
| authorships[2].institutions[0].ror | https://ror.org/02vj4rn06 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I1284762954 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Zhejiang A & F University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Zhanlin Ji |
| authorships[2].is_corresponding | True |
| authorships[2].raw_affiliation_strings | College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China |
| authorships[3].author.id | https://openalex.org/A5091211713 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Jinyun Liu |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I137506752 |
| authorships[3].affiliations[0].raw_affiliation_string | College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China |
| authorships[3].institutions[0].id | https://openalex.org/I137506752 |
| authorships[3].institutions[0].ror | https://ror.org/04z4wmb81 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I137506752 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | North China University of Science and Technology |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Jinyun Liu |
| authorships[3].is_corresponding | True |
| authorships[3].raw_affiliation_strings | College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.3390/agriculture15141526 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | GDFC-YOLO: An Efficient Perception Detection Model for Precise Wheat Disease Recognition |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10616 |
| primary_topic.field.id | https://openalex.org/fields/11 |
| primary_topic.field.display_name | Agricultural and Biological Sciences |
| primary_topic.score | 0.996399998664856 |
| primary_topic.domain.id | https://openalex.org/domains/1 |
| primary_topic.domain.display_name | Life Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1110 |
| primary_topic.subfield.display_name | Plant Science |
| primary_topic.display_name | Smart Agriculture and AI |
| related_works | https://openalex.org/W2899084033, https://openalex.org/W2748952813, https://openalex.org/W4391375266, https://openalex.org/W2082860237, https://openalex.org/W2119695867, https://openalex.org/W2130076355, https://openalex.org/W1990804418, https://openalex.org/W1993764875, https://openalex.org/W2046158694, https://openalex.org/W2788277189 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | doi:10.3390/agriculture15141526 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210202585 |
| best_oa_location.source.issn | 2077-0472 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2077-0472 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Agriculture |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Agriculture |
| best_oa_location.landing_page_url | https://doi.org/10.3390/agriculture15141526 |
| primary_location.id | doi:10.3390/agriculture15141526 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210202585 |
| primary_location.source.issn | 2077-0472 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2077-0472 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Agriculture |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Agriculture |
| primary_location.landing_page_url | https://doi.org/10.3390/agriculture15141526 |
| publication_date | 2025-07-15 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W3127209611, https://openalex.org/W6869655759, https://openalex.org/W4409109899, https://openalex.org/W4402509011, https://openalex.org/W6877026641, https://openalex.org/W6878903869, https://openalex.org/W4390999417, https://openalex.org/W4403371906, https://openalex.org/W4401946934, https://openalex.org/W4403300788, https://openalex.org/W4396908544, https://openalex.org/W4401534741, https://openalex.org/W4389617116, https://openalex.org/W4396908579, https://openalex.org/W4402704579, https://openalex.org/W4408250502, https://openalex.org/W4390873058, https://openalex.org/W4403887646, https://openalex.org/W2962766617, https://openalex.org/W2997747012, https://openalex.org/W3122173535, https://openalex.org/W4388915117, https://openalex.org/W4389264715, https://openalex.org/W2989646980, https://openalex.org/W4400058707, https://openalex.org/W4407548071, https://openalex.org/W4409171386, https://openalex.org/W3106505022 |
| referenced_works_count | 28 |
| abstract_inverted_index.M | 194 |
| abstract_inverted_index.a | 4, 84, 113, 189, 206, 216 |
| abstract_inverted_index.In | 52 |
| abstract_inverted_index.It | 214 |
| abstract_inverted_index.at | 15 |
| abstract_inverted_index.be | 201 |
| abstract_inverted_index.by | 46 |
| abstract_inverted_index.in | 11, 92, 226 |
| abstract_inverted_index.is | 3, 58, 72, 215 |
| abstract_inverted_index.it | 186, 199 |
| abstract_inverted_index.of | 7, 35, 41, 68, 81, 99, 138 |
| abstract_inverted_index.on | 74 |
| abstract_inverted_index.to | 60, 108, 125, 148, 167 |
| abstract_inverted_index.v2 | 144 |
| abstract_inverted_index.(1) | 83 |
| abstract_inverted_index.(2) | 112 |
| abstract_inverted_index.(3) | 135 |
| abstract_inverted_index.0.5 | 174 |
| abstract_inverted_index.The | 24, 156 |
| abstract_inverted_index.and | 31, 38, 64, 76, 103, 129, 134, 153, 185, 210, 221 |
| abstract_inverted_index.are | 50 |
| abstract_inverted_index.can | 200, 219 |
| abstract_inverted_index.has | 21, 205 |
| abstract_inverted_index.its | 17, 176, 180 |
| abstract_inverted_index.key | 79 |
| abstract_inverted_index.the | 29, 36, 39, 55, 66, 93, 97, 105, 136, 139, 150, 162, 170 |
| abstract_inverted_index.was | 173 |
| abstract_inverted_index.0.86 | 166 |
| abstract_inverted_index.0.90 | 168 |
| abstract_inverted_index.9.27 | 193 |
| abstract_inverted_index.Core | 90 |
| abstract_inverted_index.From | 196 |
| abstract_inverted_index.Neck | 118 |
| abstract_inverted_index.This | 70 |
| abstract_inverted_index.crop | 224 |
| abstract_inverted_index.from | 165 |
| abstract_inverted_index.good | 207 |
| abstract_inverted_index.loss | 146 |
| abstract_inverted_index.neck | 115 |
| abstract_inverted_index.only | 192 |
| abstract_inverted_index.over | 142 |
| abstract_inverted_index.rate | 182 |
| abstract_inverted_index.real | 227 |
| abstract_inverted_index.that | 159, 203, 218 |
| abstract_inverted_index.this | 53 |
| abstract_inverted_index.when | 169 |
| abstract_inverted_index.with | 191 |
| abstract_inverted_index.Ghost | 87 |
| abstract_inverted_index.Union | 143 |
| abstract_inverted_index.Wheat | 0 |
| abstract_inverted_index.based | 73 |
| abstract_inverted_index.known | 202 |
| abstract_inverted_index.newly | 85 |
| abstract_inverted_index.still | 20, 187 |
| abstract_inverted_index.these | 62, 197 |
| abstract_inverted_index.three | 78 |
| abstract_inverted_index.which | 95, 120 |
| abstract_inverted_index.work, | 54 |
| abstract_inverted_index.(GDFC) | 91 |
| abstract_inverted_index.0.821, | 184 |
| abstract_inverted_index.0.899, | 179 |
| abstract_inverted_index.caused | 45 |
| abstract_inverted_index.detect | 223 |
| abstract_inverted_index.fusion | 128 |
| abstract_inverted_index.hardly | 27 |
| abstract_inverted_index.method | 57, 71 |
| abstract_inverted_index.models | 26 |
| abstract_inverted_index.modern | 12 |
| abstract_inverted_index.recall | 181 |
| abstract_inverted_index.refine | 130 |
| abstract_inverted_index.showed | 158 |
| abstract_inverted_index.speed. | 155 |
| abstract_inverted_index.Dynamic | 88 |
| abstract_inverted_index.Feature | 89 |
| abstract_inverted_index.YOLOv11 | 75 |
| abstract_inverted_index.ability | 107 |
| abstract_inverted_index.address | 61 |
| abstract_inverted_index.aspects | 80 |
| abstract_inverted_index.average | 163 |
| abstract_inverted_index.capture | 28, 109 |
| abstract_inverted_index.certain | 22 |
| abstract_inverted_index.crucial | 5 |
| abstract_inverted_index.disease | 1, 100 |
| abstract_inverted_index.enhance | 65 |
| abstract_inverted_index.feature | 101, 123, 131 |
| abstract_inverted_index.further | 121 |
| abstract_inverted_index.improve | 126 |
| abstract_inverted_index.reached | 178, 183 |
| abstract_inverted_index.results | 40, 157 |
| abstract_inverted_index.spatial | 42 |
| abstract_inverted_index.systems | 10 |
| abstract_inverted_index.texture | 33 |
| abstract_inverted_index.(PIoUv2) | 145 |
| abstract_inverted_index.However, | 14 |
| abstract_inverted_index.Powerful | 140 |
| abstract_inverted_index.accuracy | 19, 67, 152, 164, 177 |
| abstract_inverted_index.designed | 86 |
| abstract_inverted_index.diseases | 225 |
| abstract_inverted_index.enhances | 104 |
| abstract_inverted_index.existing | 25 |
| abstract_inverted_index.features | 34 |
| abstract_inverted_index.function | 147 |
| abstract_inverted_index.improved | 161 |
| abstract_inverted_index.improves | 96 |
| abstract_inverted_index.lesions, | 37 |
| abstract_inverted_index.optimize | 149 |
| abstract_inverted_index.present, | 16 |
| abstract_inverted_index.proposed | 59 |
| abstract_inverted_index.results, | 198 |
| abstract_inverted_index.solution | 217 |
| abstract_inverted_index.standard | 47 |
| abstract_inverted_index.stronger | 211 |
| abstract_inverted_index.GDFC-YOLO | 56, 160, 204 |
| abstract_inverted_index.backbone, | 94 |
| abstract_inverted_index.component | 6 |
| abstract_inverted_index.detection | 2, 18, 208 |
| abstract_inverted_index.irregular | 30 |
| abstract_inverted_index.model’s | 106 |
| abstract_inverted_index.structure | 190 |
| abstract_inverted_index.threshold | 172 |
| abstract_inverted_index.(DF-Neck), | 119 |
| abstract_inverted_index.([email protected]), | 175 |
| abstract_inverted_index.accurately | 220 |
| abstract_inverted_index.detection. | 69 |
| abstract_inverted_index.efficiency | 98 |
| abstract_inverted_index.extraction | 102 |
| abstract_inverted_index.maintained | 188 |
| abstract_inverted_index.operations | 49 |
| abstract_inverted_index.pipelines; | 133 |
| abstract_inverted_index.processing | 132 |
| abstract_inverted_index.redesigned | 114 |
| abstract_inverted_index.regression | 151 |
| abstract_inverted_index.scenarios. | 229 |
| abstract_inverted_index.structure, | 116 |
| abstract_inverted_index.upsampling | 48 |
| abstract_inverted_index.convergence | 154 |
| abstract_inverted_index.efficiently | 222 |
| abstract_inverted_index.encompasses | 77 |
| abstract_inverted_index.inaccuracy. | 51 |
| abstract_inverted_index.information | 43 |
| abstract_inverted_index.informative | 110 |
| abstract_inverted_index.integration | 137 |
| abstract_inverted_index.intelligent | 8 |
| abstract_inverted_index.limitations | 63 |
| abstract_inverted_index.multi-scale | 127 |
| abstract_inverted_index.parameters. | 195 |
| abstract_inverted_index.performance | 209 |
| abstract_inverted_index.relatively. | 213 |
| abstract_inverted_index.strengthens | 122 |
| abstract_inverted_index.Intersection | 141 |
| abstract_inverted_index.agricultural | 9, 228 |
| abstract_inverted_index.agriculture. | 13 |
| abstract_inverted_index.fine-grained | 32 |
| abstract_inverted_index.improvement: | 82 |
| abstract_inverted_index.limitations. | 23 |
| abstract_inverted_index.practicability | 212 |
| abstract_inverted_index.reconstruction | 44 |
| abstract_inverted_index.Disease-Focused | 117 |
| abstract_inverted_index.cross-overmerge | 171 |
| abstract_inverted_index.expressiveness, | 124 |
| abstract_inverted_index.representations; | 111 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5030607091, https://openalex.org/A5091211713 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I1284762954, https://openalex.org/I137506752 |
| citation_normalized_percentile.value | 0.19848209 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |