A Semantic Labeling Approach for Accurate Weed Mapping of High Resolution UAV Imagery Article Swipe
Huasheng Huang
,
Yubin Lan
,
Jizhong Deng
,
Aqing Yang
,
Xiaoling Deng
,
Lei Zhang
,
Sheng Wen
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.3390/s18072113
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.3390/s18072113
Weed control is necessary in rice cultivation, but the excessive use of herbicide treatments has led to serious agronomic and environmental problems. Suitable site-specific weed management (SSWM) is a solution to address this problem while maintaining the rice production quality and quantity. In the context of SSWM, an accurate weed distribution map is needed to provide decision support information for herbicide treatment. UAV remote sensing offers an efficient and effective platform to monitor weeds thanks to its high spatial resolution. In this work, UAV imagery was captured in a rice field located in South China. A semantic labeling approach was adopted to generate the weed distribution maps of the UAV imagery. An ImageNet pre-trained CNN with residual framework was adapted in a fully convolutional form, and transferred to our dataset by fine-tuning. Atrous convolution was applied to extend the field of view of convolutional filters; the performance of multi-scale processing was evaluated; and a fully connected conditional random field (CRF) was applied after the CNN to further refine the spatial details. Finally, our approach was compared with the pixel-based-SVM and the classical FCN-8s. Experimental results demonstrated that our approach achieved the best performance in terms of accuracy. Especially for the detection of small weed patches in the imagery, our approach significantly outperformed other methods. The mean intersection over union (mean IU), overall accuracy, and Kappa coefficient of our method were 0.7751, 0.9445, and 0.9128, respectively. The experiments showed that our approach has high potential in accurate weed mapping of UAV imagery.
Related Topics
Concepts
Computer science
Context (archaeology)
Pixel
Weed
Artificial intelligence
Convolutional neural network
Residual
Field (mathematics)
Paddy field
Conditional random field
Remote sensing
Pattern recognition (psychology)
Image resolution
Precision agriculture
Mathematics
Algorithm
Agronomy
Geography
Pure mathematics
Archaeology
Biology
Agriculture
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s18072113
- https://www.mdpi.com/1424-8220/18/7/2113/pdf?version=1530436493
- OA Status
- gold
- Cited By
- 59
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2811217578
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2811217578Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s18072113Digital Object Identifier
- Title
-
A Semantic Labeling Approach for Accurate Weed Mapping of High Resolution UAV ImageryWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-07-01Full publication date if available
- Authors
-
Huasheng Huang, Yubin Lan, Jizhong Deng, Aqing Yang, Xiaoling Deng, Lei Zhang, Sheng WenList of authors in order
- Landing page
-
https://doi.org/10.3390/s18072113Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/18/7/2113/pdf?version=1530436493Direct 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/1424-8220/18/7/2113/pdf?version=1530436493Direct OA link when available
- Concepts
-
Computer science, Context (archaeology), Pixel, Weed, Artificial intelligence, Convolutional neural network, Residual, Field (mathematics), Paddy field, Conditional random field, Remote sensing, Pattern recognition (psychology), Image resolution, Precision agriculture, Mathematics, Algorithm, Agronomy, Geography, Pure mathematics, Archaeology, Biology, AgricultureTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
59Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6, 2024: 8, 2023: 10, 2022: 11, 2021: 13Per-year citation counts (last 5 years)
- References (count)
-
33Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W2811217578 |
|---|---|
| doi | https://doi.org/10.3390/s18072113 |
| ids.doi | https://doi.org/10.3390/s18072113 |
| ids.mag | 2811217578 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/29966392 |
| ids.openalex | https://openalex.org/W2811217578 |
| fwci | 5.13625042 |
| type | article |
| title | A Semantic Labeling Approach for Accurate Weed Mapping of High Resolution UAV Imagery |
| biblio.issue | 7 |
| biblio.volume | 18 |
| biblio.last_page | 2113 |
| biblio.first_page | 2113 |
| 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 | 0.9995999932289124 |
| 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/T10111 |
| topics[1].field.id | https://openalex.org/fields/23 |
| topics[1].field.display_name | Environmental Science |
| topics[1].score | 0.9965000152587891 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2303 |
| topics[1].subfield.display_name | Ecology |
| topics[1].display_name | Remote Sensing in Agriculture |
| topics[2].id | https://openalex.org/T11164 |
| topics[2].field.id | https://openalex.org/fields/23 |
| topics[2].field.display_name | Environmental Science |
| topics[2].score | 0.992900013923645 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2305 |
| topics[2].subfield.display_name | Environmental Engineering |
| topics[2].display_name | Remote Sensing and LiDAR Applications |
| 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/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.6801860928535461 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C2779343474 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6174572110176086 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q3109175 |
| concepts[1].display_name | Context (archaeology) |
| concepts[2].id | https://openalex.org/C160633673 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6023937463760376 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q355198 |
| concepts[2].display_name | Pixel |
| concepts[3].id | https://openalex.org/C2775891814 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5804651379585266 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q101879 |
| concepts[3].display_name | Weed |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.5479701161384583 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C81363708 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5172879695892334 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[5].display_name | Convolutional neural network |
| concepts[6].id | https://openalex.org/C155512373 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5160081386566162 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q287450 |
| concepts[6].display_name | Residual |
| concepts[7].id | https://openalex.org/C9652623 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4815528690814972 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q190109 |
| concepts[7].display_name | Field (mathematics) |
| concepts[8].id | https://openalex.org/C85582077 |
| concepts[8].level | 2 |
| concepts[8].score | 0.47760292887687683 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q842623 |
| concepts[8].display_name | Paddy field |
| concepts[9].id | https://openalex.org/C152565575 |
| concepts[9].level | 2 |
| concepts[9].score | 0.47499576210975647 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q1124538 |
| concepts[9].display_name | Conditional random field |
| concepts[10].id | https://openalex.org/C62649853 |
| concepts[10].level | 1 |
| concepts[10].score | 0.4593513309955597 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q199687 |
| concepts[10].display_name | Remote sensing |
| concepts[11].id | https://openalex.org/C153180895 |
| concepts[11].level | 2 |
| concepts[11].score | 0.4503447413444519 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[11].display_name | Pattern recognition (psychology) |
| concepts[12].id | https://openalex.org/C205372480 |
| concepts[12].level | 2 |
| concepts[12].score | 0.4454018473625183 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q210521 |
| concepts[12].display_name | Image resolution |
| concepts[13].id | https://openalex.org/C120217122 |
| concepts[13].level | 3 |
| concepts[13].score | 0.4325750172138214 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q740083 |
| concepts[13].display_name | Precision agriculture |
| concepts[14].id | https://openalex.org/C33923547 |
| concepts[14].level | 0 |
| concepts[14].score | 0.1863037347793579 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[14].display_name | Mathematics |
| concepts[15].id | https://openalex.org/C11413529 |
| concepts[15].level | 1 |
| concepts[15].score | 0.1306302845478058 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[15].display_name | Algorithm |
| concepts[16].id | https://openalex.org/C6557445 |
| concepts[16].level | 1 |
| concepts[16].score | 0.08485296368598938 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q173113 |
| concepts[16].display_name | Agronomy |
| concepts[17].id | https://openalex.org/C205649164 |
| concepts[17].level | 0 |
| concepts[17].score | 0.0722486674785614 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[17].display_name | Geography |
| concepts[18].id | https://openalex.org/C202444582 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q837863 |
| concepts[18].display_name | Pure mathematics |
| concepts[19].id | https://openalex.org/C166957645 |
| concepts[19].level | 1 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q23498 |
| concepts[19].display_name | Archaeology |
| concepts[20].id | https://openalex.org/C86803240 |
| concepts[20].level | 0 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[20].display_name | Biology |
| concepts[21].id | https://openalex.org/C118518473 |
| concepts[21].level | 2 |
| concepts[21].score | 0.0 |
| concepts[21].wikidata | https://www.wikidata.org/wiki/Q11451 |
| concepts[21].display_name | Agriculture |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.6801860928535461 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/context |
| keywords[1].score | 0.6174572110176086 |
| keywords[1].display_name | Context (archaeology) |
| keywords[2].id | https://openalex.org/keywords/pixel |
| keywords[2].score | 0.6023937463760376 |
| keywords[2].display_name | Pixel |
| keywords[3].id | https://openalex.org/keywords/weed |
| keywords[3].score | 0.5804651379585266 |
| keywords[3].display_name | Weed |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.5479701161384583 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[5].score | 0.5172879695892334 |
| keywords[5].display_name | Convolutional neural network |
| keywords[6].id | https://openalex.org/keywords/residual |
| keywords[6].score | 0.5160081386566162 |
| keywords[6].display_name | Residual |
| keywords[7].id | https://openalex.org/keywords/field |
| keywords[7].score | 0.4815528690814972 |
| keywords[7].display_name | Field (mathematics) |
| keywords[8].id | https://openalex.org/keywords/paddy-field |
| keywords[8].score | 0.47760292887687683 |
| keywords[8].display_name | Paddy field |
| keywords[9].id | https://openalex.org/keywords/conditional-random-field |
| keywords[9].score | 0.47499576210975647 |
| keywords[9].display_name | Conditional random field |
| keywords[10].id | https://openalex.org/keywords/remote-sensing |
| keywords[10].score | 0.4593513309955597 |
| keywords[10].display_name | Remote sensing |
| keywords[11].id | https://openalex.org/keywords/pattern-recognition |
| keywords[11].score | 0.4503447413444519 |
| keywords[11].display_name | Pattern recognition (psychology) |
| keywords[12].id | https://openalex.org/keywords/image-resolution |
| keywords[12].score | 0.4454018473625183 |
| keywords[12].display_name | Image resolution |
| keywords[13].id | https://openalex.org/keywords/precision-agriculture |
| keywords[13].score | 0.4325750172138214 |
| keywords[13].display_name | Precision agriculture |
| keywords[14].id | https://openalex.org/keywords/mathematics |
| keywords[14].score | 0.1863037347793579 |
| keywords[14].display_name | Mathematics |
| keywords[15].id | https://openalex.org/keywords/algorithm |
| keywords[15].score | 0.1306302845478058 |
| keywords[15].display_name | Algorithm |
| keywords[16].id | https://openalex.org/keywords/agronomy |
| keywords[16].score | 0.08485296368598938 |
| keywords[16].display_name | Agronomy |
| keywords[17].id | https://openalex.org/keywords/geography |
| keywords[17].score | 0.0722486674785614 |
| keywords[17].display_name | Geography |
| language | en |
| locations[0].id | doi:10.3390/s18072113 |
| 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/1424-8220/18/7/2113/pdf?version=1530436493 |
| 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/s18072113 |
| locations[1].id | pmid:29966392 |
| 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/29966392 |
| locations[2].id | pmh:oai:doaj.org/article:2a22e928bb564d0cbe211a8d6cca61a2 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306401280 |
| 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 | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[2].source.host_organization | |
| locations[2].source.host_organization_name | |
| locations[2].license | cc-by-sa |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | article |
| locations[2].license_id | https://openalex.org/licenses/cc-by-sa |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Sensors, Vol 18, Iss 7, p 2113 (2018) |
| locations[2].landing_page_url | https://doaj.org/article/2a22e928bb564d0cbe211a8d6cca61a2 |
| locations[3].id | pmh:oai:europepmc.org:5028499 |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S4306400806 |
| 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 | Europe PMC (PubMed Central) |
| locations[3].source.host_organization | https://openalex.org/I1303153112 |
| locations[3].source.host_organization_name | European Bioinformatics Institute |
| locations[3].source.host_organization_lineage | https://openalex.org/I1303153112 |
| locations[3].license | other-oa |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | Text |
| locations[3].license_id | https://openalex.org/licenses/other-oa |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | |
| locations[3].landing_page_url | http://europepmc.org/pmc/articles/PMC6069478 |
| locations[4].id | pmh:oai:mdpi.com:/1424-8220/18/7/2113/ |
| 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 |
| locations[4].landing_page_url | http://dx.doi.org/10.3390/s18072113 |
| locations[5].id | pmh:oai:pubmedcentral.nih.gov:6069478 |
| locations[5].is_oa | True |
| locations[5].source.id | https://openalex.org/S2764455111 |
| locations[5].source.issn | |
| locations[5].source.type | repository |
| locations[5].source.is_oa | False |
| locations[5].source.issn_l | |
| locations[5].source.is_core | False |
| locations[5].source.is_in_doaj | False |
| locations[5].source.display_name | PubMed Central |
| locations[5].source.host_organization | https://openalex.org/I1299303238 |
| locations[5].source.host_organization_name | National Institutes of Health |
| locations[5].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[5].license | other-oa |
| locations[5].pdf_url | |
| locations[5].version | submittedVersion |
| locations[5].raw_type | Text |
| locations[5].license_id | https://openalex.org/licenses/other-oa |
| locations[5].is_accepted | False |
| locations[5].is_published | False |
| locations[5].raw_source_name | Sensors (Basel) |
| locations[5].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/6069478 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5051188156 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-6546-6501 |
| authorships[0].author.display_name | Huasheng Huang |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I101479585 |
| authorships[0].affiliations[0].raw_affiliation_string | College of Engineering, South China Agricultural University, Wushan Road, Guangzhou 510642, China |
| authorships[0].affiliations[1].raw_affiliation_string | National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Wushan Road, Guangzhou 510642, China |
| authorships[0].institutions[0].id | https://openalex.org/I101479585 |
| authorships[0].institutions[0].ror | https://ror.org/05v9jqt67 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I101479585 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | South China Agricultural University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Huasheng Huang |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | College of Engineering, South China Agricultural University, Wushan Road, Guangzhou 510642, China, National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Wushan Road, Guangzhou 510642, China |
| authorships[1].author.id | https://openalex.org/A5101666108 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-6664-5571 |
| authorships[1].author.display_name | Yubin Lan |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I101479585 |
| authorships[1].affiliations[0].raw_affiliation_string | College of Engineering, South China Agricultural University, Wushan Road, Guangzhou 510642, China |
| authorships[1].affiliations[1].raw_affiliation_string | National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Wushan Road, Guangzhou 510642, China |
| authorships[1].institutions[0].id | https://openalex.org/I101479585 |
| authorships[1].institutions[0].ror | https://ror.org/05v9jqt67 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I101479585 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | South China Agricultural University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Yubin Lan |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | College of Engineering, South China Agricultural University, Wushan Road, Guangzhou 510642, China, National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Wushan Road, Guangzhou 510642, China |
| authorships[2].author.id | https://openalex.org/A5071756107 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Jizhong Deng |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].raw_affiliation_string | National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Wushan Road, Guangzhou 510642, China |
| authorships[2].affiliations[1].institution_ids | https://openalex.org/I101479585 |
| authorships[2].affiliations[1].raw_affiliation_string | College of Engineering, South China Agricultural University, Wushan Road, Guangzhou 510642, China |
| authorships[2].institutions[0].id | https://openalex.org/I101479585 |
| authorships[2].institutions[0].ror | https://ror.org/05v9jqt67 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I101479585 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | South China Agricultural University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Jizhong Deng |
| authorships[2].is_corresponding | True |
| authorships[2].raw_affiliation_strings | College of Engineering, South China Agricultural University, Wushan Road, Guangzhou 510642, China, National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Wushan Road, Guangzhou 510642, China |
| authorships[3].author.id | https://openalex.org/A5109405883 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Aqing Yang |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I101479585 |
| authorships[3].affiliations[0].raw_affiliation_string | College of Electronic Engineering, South China Agricultural University, Wushan Road, Guangzhou 516042, China |
| authorships[3].institutions[0].id | https://openalex.org/I101479585 |
| authorships[3].institutions[0].ror | https://ror.org/05v9jqt67 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I101479585 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | South China Agricultural University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Aqing Yang |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | College of Electronic Engineering, South China Agricultural University, Wushan Road, Guangzhou 516042, China |
| authorships[4].author.id | https://openalex.org/A5053583417 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-5588-3443 |
| authorships[4].author.display_name | Xiaoling Deng |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I101479585 |
| authorships[4].affiliations[0].raw_affiliation_string | College of Electronic Engineering, South China Agricultural University, Wushan Road, Guangzhou 516042, China |
| authorships[4].affiliations[1].raw_affiliation_string | National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Wushan Road, Guangzhou 510642, China |
| authorships[4].institutions[0].id | https://openalex.org/I101479585 |
| authorships[4].institutions[0].ror | https://ror.org/05v9jqt67 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I101479585 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | South China Agricultural University |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Xiaoling Deng |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | College of Electronic Engineering, South China Agricultural University, Wushan Road, Guangzhou 516042, China, National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Wushan Road, Guangzhou 510642, China |
| authorships[5].author.id | https://openalex.org/A5100687352 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-9841-744X |
| authorships[5].author.display_name | Lei Zhang |
| authorships[5].countries | CN |
| authorships[5].affiliations[0].raw_affiliation_string | National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Wushan Road, Guangzhou 510642, China |
| authorships[5].affiliations[1].institution_ids | https://openalex.org/I101479585 |
| authorships[5].affiliations[1].raw_affiliation_string | College of Agriculture, South China Agricultural University, Wushan Road, Guangzhou 516042, China |
| authorships[5].institutions[0].id | https://openalex.org/I101479585 |
| authorships[5].institutions[0].ror | https://ror.org/05v9jqt67 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I101479585 |
| authorships[5].institutions[0].country_code | CN |
| authorships[5].institutions[0].display_name | South China Agricultural University |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Lei Zhang |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | College of Agriculture, South China Agricultural University, Wushan Road, Guangzhou 516042, China, National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Wushan Road, Guangzhou 510642, China |
| authorships[6].author.id | https://openalex.org/A5101922962 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-3035-1114 |
| authorships[6].author.display_name | Sheng Wen |
| authorships[6].countries | CN |
| authorships[6].affiliations[0].raw_affiliation_string | National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Wushan Road, Guangzhou 510642, China |
| authorships[6].affiliations[1].institution_ids | https://openalex.org/I101479585 |
| authorships[6].affiliations[1].raw_affiliation_string | Engineering Fundamental Teaching and Training Center, South China Agricultural University, Wushan Road, Guangzhou 510642, China |
| authorships[6].institutions[0].id | https://openalex.org/I101479585 |
| authorships[6].institutions[0].ror | https://ror.org/05v9jqt67 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I101479585 |
| authorships[6].institutions[0].country_code | CN |
| authorships[6].institutions[0].display_name | South China Agricultural University |
| authorships[6].author_position | last |
| authorships[6].raw_author_name | Sheng Wen |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Engineering Fundamental Teaching and Training Center, South China Agricultural University, Wushan Road, Guangzhou 510642, China, National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Wushan Road, Guangzhou 510642, 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/1424-8220/18/7/2113/pdf?version=1530436493 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | A Semantic Labeling Approach for Accurate Weed Mapping of High Resolution UAV Imagery |
| has_fulltext | True |
| 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 | 0.9995999932289124 |
| 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/W2356597680, https://openalex.org/W2093471820, https://openalex.org/W50079190, https://openalex.org/W2114846443, https://openalex.org/W3102147106, https://openalex.org/W2288291625, https://openalex.org/W2763924716, https://openalex.org/W1514027645, https://openalex.org/W2152372989, https://openalex.org/W2995963058 |
| cited_by_count | 59 |
| 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 | 8 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 10 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 11 |
| counts_by_year[4].year | 2021 |
| counts_by_year[4].cited_by_count | 13 |
| counts_by_year[5].year | 2020 |
| counts_by_year[5].cited_by_count | 3 |
| counts_by_year[6].year | 2019 |
| counts_by_year[6].cited_by_count | 5 |
| counts_by_year[7].year | 2018 |
| counts_by_year[7].cited_by_count | 3 |
| locations_count | 6 |
| best_oa_location.id | doi:10.3390/s18072113 |
| 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/1424-8220/18/7/2113/pdf?version=1530436493 |
| 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/s18072113 |
| primary_location.id | doi:10.3390/s18072113 |
| 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/1424-8220/18/7/2113/pdf?version=1530436493 |
| 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/s18072113 |
| publication_date | 2018-07-01 |
| publication_year | 2018 |
| referenced_works | https://openalex.org/W234990324, https://openalex.org/W6738904709, https://openalex.org/W2509244836, https://openalex.org/W1986018372, https://openalex.org/W2080091930, https://openalex.org/W2411438253, https://openalex.org/W6738559714, https://openalex.org/W2231576311, https://openalex.org/W2751440609, https://openalex.org/W2762128751, https://openalex.org/W2081286693, https://openalex.org/W1912954554, https://openalex.org/W2919115771, https://openalex.org/W2395611524, https://openalex.org/W2780835727, https://openalex.org/W2494341560, https://openalex.org/W2800002789, https://openalex.org/W2371094632, https://openalex.org/W2549124570, https://openalex.org/W6744523291, https://openalex.org/W2340897893, https://openalex.org/W2412782625, https://openalex.org/W2548390752, https://openalex.org/W2771404974, https://openalex.org/W2194775991, https://openalex.org/W2163605009, https://openalex.org/W2097117768, https://openalex.org/W2616755213, https://openalex.org/W2098676252, https://openalex.org/W2341130385, https://openalex.org/W2620727978, https://openalex.org/W3105127913, https://openalex.org/W2620669738 |
| referenced_works_count | 33 |
| abstract_inverted_index.A | 95 |
| abstract_inverted_index.a | 28, 88, 121, 153 |
| abstract_inverted_index.An | 111 |
| abstract_inverted_index.In | 42, 80 |
| abstract_inverted_index.an | 47, 66 |
| abstract_inverted_index.by | 130 |
| abstract_inverted_index.in | 4, 87, 92, 120, 193, 205, 244 |
| abstract_inverted_index.is | 2, 27, 52 |
| abstract_inverted_index.of | 11, 45, 107, 140, 142, 147, 195, 201, 226, 248 |
| abstract_inverted_index.to | 16, 30, 54, 71, 75, 101, 127, 136, 165 |
| abstract_inverted_index.CNN | 114, 164 |
| abstract_inverted_index.The | 214, 235 |
| abstract_inverted_index.UAV | 62, 83, 109, 249 |
| abstract_inverted_index.and | 19, 40, 68, 125, 152, 179, 223, 232 |
| abstract_inverted_index.but | 7 |
| abstract_inverted_index.for | 59, 198 |
| abstract_inverted_index.has | 14, 241 |
| abstract_inverted_index.its | 76 |
| abstract_inverted_index.led | 15 |
| abstract_inverted_index.map | 51 |
| abstract_inverted_index.our | 128, 172, 187, 208, 227, 239 |
| abstract_inverted_index.the | 8, 36, 43, 103, 108, 138, 145, 163, 168, 177, 180, 190, 199, 206 |
| abstract_inverted_index.use | 10 |
| abstract_inverted_index.was | 85, 99, 118, 134, 150, 160, 174 |
| abstract_inverted_index.IU), | 220 |
| abstract_inverted_index.Weed | 0 |
| abstract_inverted_index.best | 191 |
| abstract_inverted_index.high | 77, 242 |
| abstract_inverted_index.maps | 106 |
| abstract_inverted_index.mean | 215 |
| abstract_inverted_index.over | 217 |
| abstract_inverted_index.rice | 5, 37, 89 |
| abstract_inverted_index.that | 186, 238 |
| abstract_inverted_index.this | 32, 81 |
| abstract_inverted_index.view | 141 |
| abstract_inverted_index.weed | 24, 49, 104, 203, 246 |
| abstract_inverted_index.were | 229 |
| abstract_inverted_index.with | 115, 176 |
| abstract_inverted_index.(CRF) | 159 |
| abstract_inverted_index.(mean | 219 |
| abstract_inverted_index.Kappa | 224 |
| abstract_inverted_index.SSWM, | 46 |
| abstract_inverted_index.South | 93 |
| abstract_inverted_index.after | 162 |
| abstract_inverted_index.field | 90, 139, 158 |
| abstract_inverted_index.form, | 124 |
| abstract_inverted_index.fully | 122, 154 |
| abstract_inverted_index.other | 212 |
| abstract_inverted_index.small | 202 |
| abstract_inverted_index.terms | 194 |
| abstract_inverted_index.union | 218 |
| abstract_inverted_index.weeds | 73 |
| abstract_inverted_index.while | 34 |
| abstract_inverted_index.work, | 82 |
| abstract_inverted_index.(SSWM) | 26 |
| abstract_inverted_index.Atrous | 132 |
| abstract_inverted_index.China. | 94 |
| abstract_inverted_index.extend | 137 |
| abstract_inverted_index.method | 228 |
| abstract_inverted_index.needed | 53 |
| abstract_inverted_index.offers | 65 |
| abstract_inverted_index.random | 157 |
| abstract_inverted_index.refine | 167 |
| abstract_inverted_index.remote | 63 |
| abstract_inverted_index.showed | 237 |
| abstract_inverted_index.thanks | 74 |
| abstract_inverted_index.0.7751, | 230 |
| abstract_inverted_index.0.9128, | 233 |
| abstract_inverted_index.0.9445, | 231 |
| abstract_inverted_index.FCN-8s. | 182 |
| abstract_inverted_index.adapted | 119 |
| abstract_inverted_index.address | 31 |
| abstract_inverted_index.adopted | 100 |
| abstract_inverted_index.applied | 135, 161 |
| abstract_inverted_index.context | 44 |
| abstract_inverted_index.control | 1 |
| abstract_inverted_index.dataset | 129 |
| abstract_inverted_index.further | 166 |
| abstract_inverted_index.imagery | 84 |
| abstract_inverted_index.located | 91 |
| abstract_inverted_index.mapping | 247 |
| abstract_inverted_index.monitor | 72 |
| abstract_inverted_index.overall | 221 |
| abstract_inverted_index.patches | 204 |
| abstract_inverted_index.problem | 33 |
| abstract_inverted_index.provide | 55 |
| abstract_inverted_index.quality | 39 |
| abstract_inverted_index.results | 184 |
| abstract_inverted_index.sensing | 64 |
| abstract_inverted_index.serious | 17 |
| abstract_inverted_index.spatial | 78, 169 |
| abstract_inverted_index.support | 57 |
| abstract_inverted_index.Finally, | 171 |
| abstract_inverted_index.ImageNet | 112 |
| abstract_inverted_index.Suitable | 22 |
| abstract_inverted_index.accurate | 48, 245 |
| abstract_inverted_index.achieved | 189 |
| abstract_inverted_index.approach | 98, 173, 188, 209, 240 |
| abstract_inverted_index.captured | 86 |
| abstract_inverted_index.compared | 175 |
| abstract_inverted_index.decision | 56 |
| abstract_inverted_index.details. | 170 |
| abstract_inverted_index.filters; | 144 |
| abstract_inverted_index.generate | 102 |
| abstract_inverted_index.imagery, | 207 |
| abstract_inverted_index.imagery. | 110, 250 |
| abstract_inverted_index.labeling | 97 |
| abstract_inverted_index.methods. | 213 |
| abstract_inverted_index.platform | 70 |
| abstract_inverted_index.residual | 116 |
| abstract_inverted_index.semantic | 96 |
| abstract_inverted_index.solution | 29 |
| abstract_inverted_index.accuracy, | 222 |
| abstract_inverted_index.accuracy. | 196 |
| abstract_inverted_index.agronomic | 18 |
| abstract_inverted_index.classical | 181 |
| abstract_inverted_index.connected | 155 |
| abstract_inverted_index.detection | 200 |
| abstract_inverted_index.effective | 69 |
| abstract_inverted_index.efficient | 67 |
| abstract_inverted_index.excessive | 9 |
| abstract_inverted_index.framework | 117 |
| abstract_inverted_index.herbicide | 12, 60 |
| abstract_inverted_index.necessary | 3 |
| abstract_inverted_index.potential | 243 |
| abstract_inverted_index.problems. | 21 |
| abstract_inverted_index.quantity. | 41 |
| abstract_inverted_index.Especially | 197 |
| abstract_inverted_index.evaluated; | 151 |
| abstract_inverted_index.management | 25 |
| abstract_inverted_index.processing | 149 |
| abstract_inverted_index.production | 38 |
| abstract_inverted_index.treatment. | 61 |
| abstract_inverted_index.treatments | 13 |
| abstract_inverted_index.coefficient | 225 |
| abstract_inverted_index.conditional | 156 |
| abstract_inverted_index.convolution | 133 |
| abstract_inverted_index.experiments | 236 |
| abstract_inverted_index.information | 58 |
| abstract_inverted_index.maintaining | 35 |
| abstract_inverted_index.multi-scale | 148 |
| abstract_inverted_index.performance | 146, 192 |
| abstract_inverted_index.pre-trained | 113 |
| abstract_inverted_index.resolution. | 79 |
| abstract_inverted_index.transferred | 126 |
| abstract_inverted_index.Experimental | 183 |
| abstract_inverted_index.cultivation, | 6 |
| abstract_inverted_index.demonstrated | 185 |
| abstract_inverted_index.distribution | 50, 105 |
| abstract_inverted_index.fine-tuning. | 131 |
| abstract_inverted_index.intersection | 216 |
| abstract_inverted_index.outperformed | 211 |
| abstract_inverted_index.convolutional | 123, 143 |
| abstract_inverted_index.environmental | 20 |
| abstract_inverted_index.respectively. | 234 |
| abstract_inverted_index.significantly | 210 |
| abstract_inverted_index.site-specific | 23 |
| abstract_inverted_index.pixel-based-SVM | 178 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 96 |
| corresponding_author_ids | https://openalex.org/A5071756107 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| corresponding_institution_ids | https://openalex.org/I101479585 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/2 |
| sustainable_development_goals[0].score | 0.5799999833106995 |
| sustainable_development_goals[0].display_name | Zero hunger |
| citation_normalized_percentile.value | 0.96864604 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |