Detection of Chrysanthemums Inflorescence Based on Improved CR-YOLOv5s Algorithm Article Swipe
Wentao Zhao
,
Dasheng Wu
,
Xinyu Zheng
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23094234
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23094234
Accurate recognition of the flowering stage is a prerequisite for flower yield estimation. In order to improve the recognition accuracy based on the complex image background, such as flowers partially covered by leaves and flowers with insignificant differences in various fluorescence, this paper proposed an improved CR-YOLOv5s to recognize flower buds and blooms for chrysanthemums by emphasizing feature representation through an attention mechanism. The coordinate attention mechanism module has been introduced to the backbone of the YOLOv5s so that the network can pay more attention to chrysanthemum flowers, thereby improving detection accuracy and robustness. Specifically, we replaced the convolution blocks in the backbone network of YOLOv5s with the convolution blocks from the RepVGG block structure to improve the feature representation ability of YOLOv5s through a multi-branch structure, further improving the accuracy and robustness of detection. The results showed that the average accuracy of the improved CR-YOLOv5s was as high as 93.9%, which is 4.5% better than that of normal YOLOv5s. This research provides the basis for the automatic picking and grading of flowers, as well as a decision-making basis for estimating flower yield.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s23094234
- https://www.mdpi.com/1424-8220/23/9/4234/pdf?version=1682326247
- OA Status
- gold
- Cited By
- 12
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4366826433
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4366826433Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s23094234Digital Object Identifier
- Title
-
Detection of Chrysanthemums Inflorescence Based on Improved CR-YOLOv5s AlgorithmWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-04-24Full publication date if available
- Authors
-
Wentao Zhao, Dasheng Wu, Xinyu ZhengList of authors in order
- Landing page
-
https://doi.org/10.3390/s23094234Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/23/9/4234/pdf?version=1682326247Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/23/9/4234/pdf?version=1682326247Direct OA link when available
- Concepts
-
Robustness (evolution), Artificial intelligence, Inflorescence, Pattern recognition (psychology), Computer science, Convolutional neural network, Algorithm, Feature extraction, Mathematics, Botany, Biology, Biochemistry, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
12Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6, 2024: 3, 2023: 3Per-year citation counts (last 5 years)
- References (count)
-
27Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4366826433 |
|---|---|
| doi | https://doi.org/10.3390/s23094234 |
| ids.doi | https://doi.org/10.3390/s23094234 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/37177438 |
| ids.openalex | https://openalex.org/W4366826433 |
| fwci | 3.17016279 |
| mesh[0].qualifier_ui | |
| mesh[0].descriptor_ui | D056626 |
| mesh[0].is_major_topic | True |
| mesh[0].qualifier_name | |
| mesh[0].descriptor_name | Inflorescence |
| mesh[1].qualifier_ui | |
| mesh[1].descriptor_ui | D035264 |
| mesh[1].is_major_topic | False |
| mesh[1].qualifier_name | |
| mesh[1].descriptor_name | Flowers |
| mesh[2].qualifier_ui | |
| mesh[2].descriptor_ui | D000465 |
| mesh[2].is_major_topic | False |
| mesh[2].qualifier_name | |
| mesh[2].descriptor_name | Algorithms |
| mesh[3].qualifier_ui | |
| mesh[3].descriptor_ui | D031202 |
| mesh[3].is_major_topic | True |
| mesh[3].qualifier_name | |
| mesh[3].descriptor_name | Chrysanthemum |
| mesh[4].qualifier_ui | |
| mesh[4].descriptor_ui | D018515 |
| mesh[4].is_major_topic | False |
| mesh[4].qualifier_name | |
| mesh[4].descriptor_name | Plant Leaves |
| mesh[5].qualifier_ui | |
| mesh[5].descriptor_ui | D056626 |
| mesh[5].is_major_topic | True |
| mesh[5].qualifier_name | |
| mesh[5].descriptor_name | Inflorescence |
| mesh[6].qualifier_ui | |
| mesh[6].descriptor_ui | D035264 |
| mesh[6].is_major_topic | False |
| mesh[6].qualifier_name | |
| mesh[6].descriptor_name | Flowers |
| mesh[7].qualifier_ui | |
| mesh[7].descriptor_ui | D000465 |
| mesh[7].is_major_topic | False |
| mesh[7].qualifier_name | |
| mesh[7].descriptor_name | Algorithms |
| mesh[8].qualifier_ui | |
| mesh[8].descriptor_ui | D031202 |
| mesh[8].is_major_topic | True |
| mesh[8].qualifier_name | |
| mesh[8].descriptor_name | Chrysanthemum |
| mesh[9].qualifier_ui | |
| mesh[9].descriptor_ui | D018515 |
| mesh[9].is_major_topic | False |
| mesh[9].qualifier_name | |
| mesh[9].descriptor_name | Plant Leaves |
| type | article |
| title | Detection of Chrysanthemums Inflorescence Based on Improved CR-YOLOv5s Algorithm |
| biblio.issue | 9 |
| biblio.volume | 23 |
| biblio.last_page | 4234 |
| biblio.first_page | 4234 |
| 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 | 0.9921000003814697 |
| 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/T13890 |
| topics[1].field.id | https://openalex.org/fields/19 |
| topics[1].field.display_name | Earth and Planetary Sciences |
| topics[1].score | 0.9656000137329102 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1902 |
| topics[1].subfield.display_name | Atmospheric Science |
| topics[1].display_name | Remote Sensing and Land Use |
| topics[2].id | https://openalex.org/T14365 |
| topics[2].field.id | https://openalex.org/fields/11 |
| topics[2].field.display_name | Agricultural and Biological Sciences |
| topics[2].score | 0.9567000269889832 |
| topics[2].domain.id | https://openalex.org/domains/1 |
| topics[2].domain.display_name | Life Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1110 |
| topics[2].subfield.display_name | Plant Science |
| topics[2].display_name | Leaf Properties and Growth Measurement |
| is_xpac | False |
| apc_list.value | 2400 |
| apc_list.currency | CHF |
| apc_list.value_usd | 2598 |
| apc_paid.value | 2400 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 2598 |
| concepts[0].id | https://openalex.org/C63479239 |
| concepts[0].level | 3 |
| concepts[0].score | 0.7646373510360718 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q7353546 |
| concepts[0].display_name | Robustness (evolution) |
| concepts[1].id | https://openalex.org/C154945302 |
| concepts[1].level | 1 |
| concepts[1].score | 0.5251103043556213 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[1].display_name | Artificial intelligence |
| concepts[2].id | https://openalex.org/C178165689 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5110329389572144 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q156428 |
| concepts[2].display_name | Inflorescence |
| concepts[3].id | https://openalex.org/C153180895 |
| concepts[3].level | 2 |
| concepts[3].score | 0.493814080953598 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[3].display_name | Pattern recognition (psychology) |
| concepts[4].id | https://openalex.org/C41008148 |
| concepts[4].level | 0 |
| concepts[4].score | 0.48737818002700806 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[4].display_name | Computer science |
| concepts[5].id | https://openalex.org/C81363708 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4513853192329407 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[5].display_name | Convolutional neural network |
| concepts[6].id | https://openalex.org/C11413529 |
| concepts[6].level | 1 |
| concepts[6].score | 0.43630093336105347 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[6].display_name | Algorithm |
| concepts[7].id | https://openalex.org/C52622490 |
| concepts[7].level | 2 |
| concepts[7].score | 0.41563987731933594 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q1026626 |
| concepts[7].display_name | Feature extraction |
| concepts[8].id | https://openalex.org/C33923547 |
| concepts[8].level | 0 |
| concepts[8].score | 0.37450581789016724 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[8].display_name | Mathematics |
| concepts[9].id | https://openalex.org/C59822182 |
| concepts[9].level | 1 |
| concepts[9].score | 0.21354737877845764 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q441 |
| concepts[9].display_name | Botany |
| concepts[10].id | https://openalex.org/C86803240 |
| concepts[10].level | 0 |
| concepts[10].score | 0.15150663256645203 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[10].display_name | Biology |
| concepts[11].id | https://openalex.org/C55493867 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q7094 |
| concepts[11].display_name | Biochemistry |
| concepts[12].id | https://openalex.org/C104317684 |
| concepts[12].level | 2 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q7187 |
| concepts[12].display_name | Gene |
| keywords[0].id | https://openalex.org/keywords/robustness |
| keywords[0].score | 0.7646373510360718 |
| keywords[0].display_name | Robustness (evolution) |
| keywords[1].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[1].score | 0.5251103043556213 |
| keywords[1].display_name | Artificial intelligence |
| keywords[2].id | https://openalex.org/keywords/inflorescence |
| keywords[2].score | 0.5110329389572144 |
| keywords[2].display_name | Inflorescence |
| keywords[3].id | https://openalex.org/keywords/pattern-recognition |
| keywords[3].score | 0.493814080953598 |
| keywords[3].display_name | Pattern recognition (psychology) |
| keywords[4].id | https://openalex.org/keywords/computer-science |
| keywords[4].score | 0.48737818002700806 |
| keywords[4].display_name | Computer science |
| keywords[5].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[5].score | 0.4513853192329407 |
| keywords[5].display_name | Convolutional neural network |
| keywords[6].id | https://openalex.org/keywords/algorithm |
| keywords[6].score | 0.43630093336105347 |
| keywords[6].display_name | Algorithm |
| keywords[7].id | https://openalex.org/keywords/feature-extraction |
| keywords[7].score | 0.41563987731933594 |
| keywords[7].display_name | Feature extraction |
| keywords[8].id | https://openalex.org/keywords/mathematics |
| keywords[8].score | 0.37450581789016724 |
| keywords[8].display_name | Mathematics |
| keywords[9].id | https://openalex.org/keywords/botany |
| keywords[9].score | 0.21354737877845764 |
| keywords[9].display_name | Botany |
| keywords[10].id | https://openalex.org/keywords/biology |
| keywords[10].score | 0.15150663256645203 |
| keywords[10].display_name | Biology |
| language | en |
| locations[0].id | doi:10.3390/s23094234 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S101949793 |
| locations[0].source.issn | 1424-8220 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1424-8220 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Sensors |
| 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 | https://www.mdpi.com/1424-8220/23/9/4234/pdf?version=1682326247 |
| 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 | Sensors |
| locations[0].landing_page_url | https://doi.org/10.3390/s23094234 |
| locations[1].id | pmid:37177438 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| 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 | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Sensors (Basel, Switzerland) |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/37177438 |
| locations[2].id | pmh:oai:pubmedcentral.nih.gov:10181578 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S2764455111 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | PubMed Central |
| locations[2].source.host_organization | https://openalex.org/I1299303238 |
| locations[2].source.host_organization_name | National Institutes of Health |
| locations[2].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[2].license | cc-by |
| locations[2].pdf_url | https://pmc.ncbi.nlm.nih.gov/articles/PMC10181578/pdf/sensors-23-04234.pdf |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | https://openalex.org/licenses/cc-by |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Sensors (Basel) |
| locations[2].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/10181578 |
| locations[3].id | pmh:oai:doaj.org/article:ee66db6dbc384db7bc04392ad881cf87 |
| locations[3].is_oa | False |
| locations[3].source.id | https://openalex.org/S4306401280 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | False |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[3].source.host_organization | |
| locations[3].source.host_organization_name | |
| locations[3].license | |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | article |
| locations[3].license_id | |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | Sensors, Vol 23, Iss 9, p 4234 (2023) |
| locations[3].landing_page_url | https://doaj.org/article/ee66db6dbc384db7bc04392ad881cf87 |
| locations[4].id | pmh:oai:mdpi.com:/1424-8220/23/9/4234/ |
| locations[4].is_oa | True |
| locations[4].source.id | https://openalex.org/S4306400947 |
| locations[4].source.issn | |
| locations[4].source.type | repository |
| locations[4].source.is_oa | True |
| locations[4].source.issn_l | |
| locations[4].source.is_core | False |
| locations[4].source.is_in_doaj | False |
| locations[4].source.display_name | MDPI (MDPI AG) |
| locations[4].source.host_organization | https://openalex.org/I4210097602 |
| locations[4].source.host_organization_name | Multidisciplinary Digital Publishing Institute (Switzerland) |
| locations[4].source.host_organization_lineage | https://openalex.org/I4210097602 |
| locations[4].license | cc-by |
| locations[4].pdf_url | |
| locations[4].version | submittedVersion |
| locations[4].raw_type | Text |
| locations[4].license_id | https://openalex.org/licenses/cc-by |
| locations[4].is_accepted | False |
| locations[4].is_published | False |
| locations[4].raw_source_name | Sensors; Volume 23; Issue 9; Pages: 4234 |
| locations[4].landing_page_url | https://dx.doi.org/10.3390/s23094234 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5100781925 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-0805-9384 |
| authorships[0].author.display_name | Wentao Zhao |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].raw_affiliation_string | Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Hangzhou 311300, China |
| authorships[0].affiliations[1].institution_ids | https://openalex.org/I4210134523 |
| authorships[0].affiliations[1].raw_affiliation_string | Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment, Hangzhou 311300, China |
| authorships[0].affiliations[2].institution_ids | https://openalex.org/I1284762954 |
| authorships[0].affiliations[2].raw_affiliation_string | College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China |
| authorships[0].institutions[0].id | https://openalex.org/I4210134523 |
| authorships[0].institutions[0].ror | https://ror.org/03f2n3n81 |
| authorships[0].institutions[0].type | government |
| authorships[0].institutions[0].lineage | https://openalex.org/I4210134523 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | State Forestry and Grassland Administration |
| authorships[0].institutions[1].id | https://openalex.org/I1284762954 |
| authorships[0].institutions[1].ror | https://ror.org/02vj4rn06 |
| authorships[0].institutions[1].type | education |
| authorships[0].institutions[1].lineage | https://openalex.org/I1284762954 |
| authorships[0].institutions[1].country_code | CN |
| authorships[0].institutions[1].display_name | Zhejiang A & F University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Wentao Zhao |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China, Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Hangzhou 311300, China, Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment, Hangzhou 311300, China |
| authorships[1].author.id | https://openalex.org/A5033636430 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-6316-303X |
| authorships[1].author.display_name | Dasheng Wu |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I1284762954 |
| authorships[1].affiliations[0].raw_affiliation_string | College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China |
| authorships[1].affiliations[1].raw_affiliation_string | Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Hangzhou 311300, China |
| authorships[1].affiliations[2].institution_ids | https://openalex.org/I4210134523 |
| authorships[1].affiliations[2].raw_affiliation_string | Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment, Hangzhou 311300, China |
| authorships[1].institutions[0].id | https://openalex.org/I4210134523 |
| authorships[1].institutions[0].ror | https://ror.org/03f2n3n81 |
| authorships[1].institutions[0].type | government |
| authorships[1].institutions[0].lineage | https://openalex.org/I4210134523 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | State Forestry and Grassland Administration |
| authorships[1].institutions[1].id | https://openalex.org/I1284762954 |
| authorships[1].institutions[1].ror | https://ror.org/02vj4rn06 |
| authorships[1].institutions[1].type | education |
| authorships[1].institutions[1].lineage | https://openalex.org/I1284762954 |
| authorships[1].institutions[1].country_code | CN |
| authorships[1].institutions[1].display_name | Zhejiang A & F University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Dasheng Wu |
| authorships[1].is_corresponding | True |
| authorships[1].raw_affiliation_strings | College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China, Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Hangzhou 311300, China, Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment, Hangzhou 311300, China |
| authorships[2].author.id | https://openalex.org/A5082482226 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-1840-214X |
| authorships[2].author.display_name | Xinyu Zheng |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].raw_affiliation_string | Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Hangzhou 311300, China |
| authorships[2].affiliations[1].institution_ids | https://openalex.org/I1284762954 |
| authorships[2].affiliations[1].raw_affiliation_string | College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China |
| authorships[2].affiliations[2].institution_ids | https://openalex.org/I4210134523 |
| authorships[2].affiliations[2].raw_affiliation_string | Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment, Hangzhou 311300, China |
| authorships[2].institutions[0].id | https://openalex.org/I4210134523 |
| authorships[2].institutions[0].ror | https://ror.org/03f2n3n81 |
| authorships[2].institutions[0].type | government |
| authorships[2].institutions[0].lineage | https://openalex.org/I4210134523 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | State Forestry and Grassland Administration |
| authorships[2].institutions[1].id | https://openalex.org/I1284762954 |
| authorships[2].institutions[1].ror | https://ror.org/02vj4rn06 |
| authorships[2].institutions[1].type | education |
| authorships[2].institutions[1].lineage | https://openalex.org/I1284762954 |
| authorships[2].institutions[1].country_code | CN |
| authorships[2].institutions[1].display_name | Zhejiang A & F University |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Xinyu Zheng |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China, Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Hangzhou 311300, China, Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment, Hangzhou 311300, China |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.mdpi.com/1424-8220/23/9/4234/pdf?version=1682326247 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Detection of Chrysanthemums Inflorescence Based on Improved CR-YOLOv5s Algorithm |
| 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 | 0.9921000003814697 |
| 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/W1996690921, https://openalex.org/W2601157893, https://openalex.org/W2131735617, https://openalex.org/W2373006798, https://openalex.org/W2056912418, https://openalex.org/W2123759770, https://openalex.org/W2033213769, https://openalex.org/W2811390910, https://openalex.org/W4312376745, https://openalex.org/W2082269393 |
| cited_by_count | 12 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 6 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 3 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 3 |
| locations_count | 5 |
| best_oa_location.id | doi:10.3390/s23094234 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S101949793 |
| best_oa_location.source.issn | 1424-8220 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1424-8220 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Sensors |
| 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 | https://www.mdpi.com/1424-8220/23/9/4234/pdf?version=1682326247 |
| 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 | Sensors |
| best_oa_location.landing_page_url | https://doi.org/10.3390/s23094234 |
| primary_location.id | doi:10.3390/s23094234 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S101949793 |
| primary_location.source.issn | 1424-8220 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1424-8220 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Sensors |
| 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 | https://www.mdpi.com/1424-8220/23/9/4234/pdf?version=1682326247 |
| 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 | Sensors |
| primary_location.landing_page_url | https://doi.org/10.3390/s23094234 |
| publication_date | 2023-04-24 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W3159149035, https://openalex.org/W6783764359, https://openalex.org/W2794915299, https://openalex.org/W6734627499, https://openalex.org/W2912922175, https://openalex.org/W2957655889, https://openalex.org/W2991463804, https://openalex.org/W3121637738, https://openalex.org/W3085201316, https://openalex.org/W2969769846, https://openalex.org/W4312186231, https://openalex.org/W3179888767, https://openalex.org/W3150095517, https://openalex.org/W3036111160, https://openalex.org/W4289846077, https://openalex.org/W3215748458, https://openalex.org/W4220846219, https://openalex.org/W4210848647, https://openalex.org/W2963420686, https://openalex.org/W4234020306, https://openalex.org/W3177052299, https://openalex.org/W3167976421, https://openalex.org/W639708223, https://openalex.org/W2564755587, https://openalex.org/W6803003410, https://openalex.org/W2594297985, https://openalex.org/W3087448910 |
| referenced_works_count | 27 |
| abstract_inverted_index.a | 7, 124, 176 |
| abstract_inverted_index.In | 13 |
| abstract_inverted_index.an | 44, 60 |
| abstract_inverted_index.as | 27, 147, 149, 173, 175 |
| abstract_inverted_index.by | 31, 55 |
| abstract_inverted_index.in | 38, 100 |
| abstract_inverted_index.is | 6, 152 |
| abstract_inverted_index.of | 2, 74, 104, 121, 133, 142, 157, 171 |
| abstract_inverted_index.on | 21 |
| abstract_inverted_index.so | 77 |
| abstract_inverted_index.to | 15, 47, 71, 85, 115 |
| abstract_inverted_index.we | 95 |
| abstract_inverted_index.The | 63, 135 |
| abstract_inverted_index.and | 33, 51, 92, 131, 169 |
| abstract_inverted_index.can | 81 |
| abstract_inverted_index.for | 9, 53, 165, 179 |
| abstract_inverted_index.has | 68 |
| abstract_inverted_index.pay | 82 |
| abstract_inverted_index.the | 3, 17, 22, 72, 75, 79, 97, 101, 107, 111, 117, 129, 139, 143, 163, 166 |
| abstract_inverted_index.was | 146 |
| abstract_inverted_index.4.5% | 153 |
| abstract_inverted_index.This | 160 |
| abstract_inverted_index.been | 69 |
| abstract_inverted_index.buds | 50 |
| abstract_inverted_index.from | 110 |
| abstract_inverted_index.high | 148 |
| abstract_inverted_index.more | 83 |
| abstract_inverted_index.such | 26 |
| abstract_inverted_index.than | 155 |
| abstract_inverted_index.that | 78, 138, 156 |
| abstract_inverted_index.this | 41 |
| abstract_inverted_index.well | 174 |
| abstract_inverted_index.with | 35, 106 |
| abstract_inverted_index.based | 20 |
| abstract_inverted_index.basis | 164, 178 |
| abstract_inverted_index.block | 113 |
| abstract_inverted_index.image | 24 |
| abstract_inverted_index.order | 14 |
| abstract_inverted_index.paper | 42 |
| abstract_inverted_index.stage | 5 |
| abstract_inverted_index.which | 151 |
| abstract_inverted_index.yield | 11 |
| abstract_inverted_index.93.9%, | 150 |
| abstract_inverted_index.RepVGG | 112 |
| abstract_inverted_index.better | 154 |
| abstract_inverted_index.blocks | 99, 109 |
| abstract_inverted_index.blooms | 52 |
| abstract_inverted_index.flower | 10, 49, 181 |
| abstract_inverted_index.leaves | 32 |
| abstract_inverted_index.module | 67 |
| abstract_inverted_index.normal | 158 |
| abstract_inverted_index.showed | 137 |
| abstract_inverted_index.yield. | 182 |
| abstract_inverted_index.YOLOv5s | 76, 105, 122 |
| abstract_inverted_index.ability | 120 |
| abstract_inverted_index.average | 140 |
| abstract_inverted_index.complex | 23 |
| abstract_inverted_index.covered | 30 |
| abstract_inverted_index.feature | 57, 118 |
| abstract_inverted_index.flowers | 28, 34 |
| abstract_inverted_index.further | 127 |
| abstract_inverted_index.grading | 170 |
| abstract_inverted_index.improve | 16, 116 |
| abstract_inverted_index.network | 80, 103 |
| abstract_inverted_index.picking | 168 |
| abstract_inverted_index.results | 136 |
| abstract_inverted_index.thereby | 88 |
| abstract_inverted_index.through | 59, 123 |
| abstract_inverted_index.various | 39 |
| abstract_inverted_index.Accurate | 0 |
| abstract_inverted_index.YOLOv5s. | 159 |
| abstract_inverted_index.accuracy | 19, 91, 130, 141 |
| abstract_inverted_index.backbone | 73, 102 |
| abstract_inverted_index.flowers, | 87, 172 |
| abstract_inverted_index.improved | 45, 144 |
| abstract_inverted_index.proposed | 43 |
| abstract_inverted_index.provides | 162 |
| abstract_inverted_index.replaced | 96 |
| abstract_inverted_index.research | 161 |
| abstract_inverted_index.attention | 61, 65, 84 |
| abstract_inverted_index.automatic | 167 |
| abstract_inverted_index.detection | 90 |
| abstract_inverted_index.flowering | 4 |
| abstract_inverted_index.improving | 89, 128 |
| abstract_inverted_index.mechanism | 66 |
| abstract_inverted_index.partially | 29 |
| abstract_inverted_index.recognize | 48 |
| abstract_inverted_index.structure | 114 |
| abstract_inverted_index.CR-YOLOv5s | 46, 145 |
| abstract_inverted_index.coordinate | 64 |
| abstract_inverted_index.detection. | 134 |
| abstract_inverted_index.estimating | 180 |
| abstract_inverted_index.introduced | 70 |
| abstract_inverted_index.mechanism. | 62 |
| abstract_inverted_index.robustness | 132 |
| abstract_inverted_index.structure, | 126 |
| abstract_inverted_index.background, | 25 |
| abstract_inverted_index.convolution | 98, 108 |
| abstract_inverted_index.differences | 37 |
| abstract_inverted_index.emphasizing | 56 |
| abstract_inverted_index.estimation. | 12 |
| abstract_inverted_index.recognition | 1, 18 |
| abstract_inverted_index.robustness. | 93 |
| abstract_inverted_index.multi-branch | 125 |
| abstract_inverted_index.prerequisite | 8 |
| abstract_inverted_index.Specifically, | 94 |
| abstract_inverted_index.chrysanthemum | 86 |
| abstract_inverted_index.fluorescence, | 40 |
| abstract_inverted_index.insignificant | 36 |
| abstract_inverted_index.chrysanthemums | 54 |
| abstract_inverted_index.representation | 58, 119 |
| abstract_inverted_index.decision-making | 177 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 96 |
| corresponding_author_ids | https://openalex.org/A5033636430 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I1284762954, https://openalex.org/I4210134523 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.5299999713897705 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.94955357 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |