Towards Automated Analysis of Grain Spikes in Greenhouse Images Using Neural Network Approaches: A Comparative Investigation of Six Methods Article Swipe
Sajid Ullah
,
M. Henke
,
Narendra Narisetti
,
Klará Panzarová
,
Martin Trtílek
,
Jan Hejátko
,
Evgeny Gladilin
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/s21227441
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/s21227441
Automated analysis of small and optically variable plant organs, such as grain spikes, is highly demanded in quantitative plant science and breeding. Previous works primarily focused on the detection of prominently visible spikes emerging on the top of the grain plants growing in field conditions. However, accurate and automated analysis of all fully and partially visible spikes in greenhouse images renders a more challenging task, which was rarely addressed in the past. A particular difficulty for image analysis is represented by leaf-covered, occluded but also matured spikes of bushy crop cultivars that can hardly be differentiated from the remaining plant biomass. To address the challenge of automated analysis of arbitrary spike phenotypes in different grain crops and optical setups, here, we performed a comparative investigation of six neural network methods for pattern detection and segmentation in RGB images, including five deep and one shallow neural network. Our experimental results demonstrate that advanced deep learning methods show superior performance, achieving over 90% accuracy by detection and segmentation of spikes in wheat, barley and rye images. However, spike detection in new crop phenotypes can be performed more accurately than segmentation. Furthermore, the detection and segmentation of matured, partially visible and occluded spikes, for which phenotypes substantially deviate from the training set of regular spikes, still represent a challenge to neural network models trained on a limited set of a few hundreds of manually labeled ground truth images. Limitations and further potential improvements of the presented algorithmic frameworks for spike image analysis are discussed. Besides theoretical and experimental investigations, we provide a GUI-based tool (SpikeApp), which shows the application of pre-trained neural networks to fully automate spike detection, segmentation and phenotyping in images of greenhouse-grown plants.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s21227441
- https://www.mdpi.com/1424-8220/21/22/7441/pdf?version=1636458973
- OA Status
- gold
- Cited By
- 6
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3214345143
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3214345143Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s21227441Digital Object Identifier
- Title
-
Towards Automated Analysis of Grain Spikes in Greenhouse Images Using Neural Network Approaches: A Comparative Investigation of Six MethodsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-11-09Full publication date if available
- Authors
-
Sajid Ullah, M. Henke, Narendra Narisetti, Klará Panzarová, Martin Trtílek, Jan Hejátko, Evgeny GladilinList of authors in order
- Landing page
-
https://doi.org/10.3390/s21227441Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/21/22/7441/pdf?version=1636458973Direct 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/1424-8220/21/22/7441/pdf?version=1636458973Direct OA link when available
- Concepts
-
Segmentation, Artificial intelligence, Computer science, Artificial neural network, Ground truth, Spike (software development), Pattern recognition (psychology), Image segmentation, Set (abstract data type), Computer vision, Programming language, Software engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 4, 2023: 1, 2022: 1Per-year citation counts (last 5 years)
- References (count)
-
34Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3214345143 |
|---|---|
| doi | https://doi.org/10.3390/s21227441 |
| ids.doi | https://doi.org/10.3390/s21227441 |
| ids.mag | 3214345143 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/34833515 |
| ids.openalex | https://openalex.org/W3214345143 |
| fwci | 0.93850365 |
| mesh[0].qualifier_ui | |
| mesh[0].descriptor_ui | D002523 |
| mesh[0].is_major_topic | False |
| mesh[0].qualifier_name | |
| mesh[0].descriptor_name | Edible Grain |
| mesh[1].qualifier_ui | |
| mesh[1].descriptor_ui | D007091 |
| mesh[1].is_major_topic | False |
| mesh[1].qualifier_name | |
| mesh[1].descriptor_name | Image Processing, Computer-Assisted |
| mesh[2].qualifier_ui | |
| mesh[2].descriptor_ui | D016571 |
| mesh[2].is_major_topic | True |
| mesh[2].qualifier_name | |
| mesh[2].descriptor_name | Neural Networks, Computer |
| mesh[3].qualifier_ui | |
| mesh[3].descriptor_ui | D000069600 |
| mesh[3].is_major_topic | True |
| mesh[3].qualifier_name | |
| mesh[3].descriptor_name | Plant Breeding |
| 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 | D002523 |
| mesh[5].is_major_topic | False |
| mesh[5].qualifier_name | |
| mesh[5].descriptor_name | Edible Grain |
| mesh[6].qualifier_ui | |
| mesh[6].descriptor_ui | D007091 |
| mesh[6].is_major_topic | False |
| mesh[6].qualifier_name | |
| mesh[6].descriptor_name | Image Processing, Computer-Assisted |
| mesh[7].qualifier_ui | |
| mesh[7].descriptor_ui | D016571 |
| mesh[7].is_major_topic | True |
| mesh[7].qualifier_name | |
| mesh[7].descriptor_name | Neural Networks, Computer |
| mesh[8].qualifier_ui | |
| mesh[8].descriptor_ui | D000069600 |
| mesh[8].is_major_topic | True |
| mesh[8].qualifier_name | |
| mesh[8].descriptor_name | Plant Breeding |
| 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 | Towards Automated Analysis of Grain Spikes in Greenhouse Images Using Neural Network Approaches: A Comparative Investigation of Six Methods |
| awards[0].id | https://openalex.org/G702534825 |
| awards[0].funder_id | https://openalex.org/F4320335322 |
| awards[0].display_name | |
| awards[0].funder_award_id | No.CZ.02.1.01/0.0/0.0/16_026/0008446 |
| awards[0].funder_display_name | European Regional Development Fund |
| biblio.issue | 22 |
| biblio.volume | 21 |
| biblio.last_page | 7441 |
| biblio.first_page | 7441 |
| topics[0].id | https://openalex.org/T11796 |
| topics[0].field.id | https://openalex.org/fields/11 |
| topics[0].field.display_name | Agricultural and Biological Sciences |
| topics[0].score | 0.998199999332428 |
| 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 | Horticultural and Viticultural Research |
| topics[1].id | https://openalex.org/T10616 |
| topics[1].field.id | https://openalex.org/fields/11 |
| topics[1].field.display_name | Agricultural and Biological Sciences |
| topics[1].score | 0.9980000257492065 |
| topics[1].domain.id | https://openalex.org/domains/1 |
| topics[1].domain.display_name | Life Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1110 |
| topics[1].subfield.display_name | Plant Science |
| topics[1].display_name | Smart Agriculture and AI |
| topics[2].id | https://openalex.org/T10111 |
| topics[2].field.id | https://openalex.org/fields/23 |
| topics[2].field.display_name | Environmental Science |
| topics[2].score | 0.9957000017166138 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2303 |
| topics[2].subfield.display_name | Ecology |
| topics[2].display_name | Remote Sensing in Agriculture |
| funders[0].id | https://openalex.org/F4320335322 |
| funders[0].ror | https://ror.org/00k4n6c32 |
| funders[0].display_name | European Regional Development Fund |
| 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/C89600930 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7140325307846069 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1423946 |
| concepts[0].display_name | Segmentation |
| concepts[1].id | https://openalex.org/C154945302 |
| concepts[1].level | 1 |
| concepts[1].score | 0.7003487348556519 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[1].display_name | Artificial intelligence |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.6581598520278931 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C50644808 |
| concepts[3].level | 2 |
| concepts[3].score | 0.6318028569221497 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[3].display_name | Artificial neural network |
| concepts[4].id | https://openalex.org/C146849305 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5866833329200745 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q370766 |
| concepts[4].display_name | Ground truth |
| concepts[5].id | https://openalex.org/C2781390188 |
| concepts[5].level | 2 |
| concepts[5].score | 0.550574779510498 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q25203449 |
| concepts[5].display_name | Spike (software development) |
| concepts[6].id | https://openalex.org/C153180895 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5208066701889038 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[6].display_name | Pattern recognition (psychology) |
| concepts[7].id | https://openalex.org/C124504099 |
| concepts[7].level | 3 |
| concepts[7].score | 0.4924961030483246 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q56933 |
| concepts[7].display_name | Image segmentation |
| concepts[8].id | https://openalex.org/C177264268 |
| concepts[8].level | 2 |
| concepts[8].score | 0.460018128156662 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q1514741 |
| concepts[8].display_name | Set (abstract data type) |
| concepts[9].id | https://openalex.org/C31972630 |
| concepts[9].level | 1 |
| concepts[9].score | 0.3442246913909912 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[9].display_name | Computer vision |
| concepts[10].id | https://openalex.org/C199360897 |
| concepts[10].level | 1 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[10].display_name | Programming language |
| concepts[11].id | https://openalex.org/C115903868 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q80993 |
| concepts[11].display_name | Software engineering |
| keywords[0].id | https://openalex.org/keywords/segmentation |
| keywords[0].score | 0.7140325307846069 |
| keywords[0].display_name | Segmentation |
| keywords[1].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[1].score | 0.7003487348556519 |
| keywords[1].display_name | Artificial intelligence |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.6581598520278931 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[3].score | 0.6318028569221497 |
| keywords[3].display_name | Artificial neural network |
| keywords[4].id | https://openalex.org/keywords/ground-truth |
| keywords[4].score | 0.5866833329200745 |
| keywords[4].display_name | Ground truth |
| keywords[5].id | https://openalex.org/keywords/spike |
| keywords[5].score | 0.550574779510498 |
| keywords[5].display_name | Spike (software development) |
| keywords[6].id | https://openalex.org/keywords/pattern-recognition |
| keywords[6].score | 0.5208066701889038 |
| keywords[6].display_name | Pattern recognition (psychology) |
| keywords[7].id | https://openalex.org/keywords/image-segmentation |
| keywords[7].score | 0.4924961030483246 |
| keywords[7].display_name | Image segmentation |
| keywords[8].id | https://openalex.org/keywords/set |
| keywords[8].score | 0.460018128156662 |
| keywords[8].display_name | Set (abstract data type) |
| keywords[9].id | https://openalex.org/keywords/computer-vision |
| keywords[9].score | 0.3442246913909912 |
| keywords[9].display_name | Computer vision |
| language | en |
| locations[0].id | doi:10.3390/s21227441 |
| 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/1424-8220/21/22/7441/pdf?version=1636458973 |
| 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/s21227441 |
| locations[1].id | pmid:34833515 |
| 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/34833515 |
| locations[2].id | pmh:oai:doaj.org/article:809f54c958a54ad19a00344090331f23 |
| 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 21, Iss 22, p 7441 (2021) |
| locations[2].landing_page_url | https://doaj.org/article/809f54c958a54ad19a00344090331f23 |
| locations[3].id | pmh:oai:frl.publisso.de:frl:6438462 |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S4306402606 |
| 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 | Informationszentrum Lebenswissenschaften (ZB MED) |
| locations[3].source.host_organization | https://openalex.org/I4210112663 |
| locations[3].source.host_organization_name | ZB MED - Information Centre for Life Sciences |
| locations[3].source.host_organization_lineage | https://openalex.org/I4210112663 |
| locations[3].license | cc-by |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | Zeitschriftenartikel |
| locations[3].license_id | https://openalex.org/licenses/cc-by |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | Sensors, 21(22):7441 |
| locations[3].landing_page_url | https://repository.publisso.de/resource/frl:6438462 |
| locations[4].id | pmh:oai:mdpi.com:/1424-8220/21/22/7441/ |
| 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 21; Issue 22; Pages: 7441 |
| locations[4].landing_page_url | https://dx.doi.org/10.3390/s21227441 |
| locations[5].id | pmh:oai:publications.goettingen-research-online.de:2/101305 |
| locations[5].is_oa | True |
| locations[5].source | |
| locations[5].license | cc-by |
| locations[5].pdf_url | |
| locations[5].version | submittedVersion |
| locations[5].raw_type | info:eu-repo/semantics/article |
| locations[5].license_id | https://openalex.org/licenses/cc-by |
| locations[5].is_accepted | False |
| locations[5].is_published | False |
| locations[5].raw_source_name | |
| locations[5].landing_page_url | https://resolver.sub.uni-goettingen.de/purl?gro-2/101305 |
| locations[6].id | pmh:oai:pubmedcentral.nih.gov:8621358 |
| locations[6].is_oa | True |
| locations[6].source.id | https://openalex.org/S2764455111 |
| locations[6].source.issn | |
| locations[6].source.type | repository |
| locations[6].source.is_oa | False |
| locations[6].source.issn_l | |
| locations[6].source.is_core | False |
| locations[6].source.is_in_doaj | False |
| locations[6].source.display_name | PubMed Central |
| locations[6].source.host_organization | https://openalex.org/I1299303238 |
| locations[6].source.host_organization_name | National Institutes of Health |
| locations[6].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[6].license | other-oa |
| locations[6].pdf_url | |
| locations[6].version | submittedVersion |
| locations[6].raw_type | Text |
| locations[6].license_id | https://openalex.org/licenses/other-oa |
| locations[6].is_accepted | False |
| locations[6].is_published | False |
| locations[6].raw_source_name | Sensors (Basel) |
| locations[6].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/8621358 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5101451622 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-4906-721X |
| authorships[0].author.display_name | Sajid Ullah |
| authorships[0].countries | CZ |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I1310815967, https://openalex.org/I4210090417 |
| authorships[0].affiliations[0].raw_affiliation_string | Plant Sciences Core Facility, CEITEC—Central European Institute of Technology, Masaryk University, 60200 Brno, Czech Republic |
| authorships[0].institutions[0].id | https://openalex.org/I1310815967 |
| authorships[0].institutions[0].ror | https://ror.org/01p7k1986 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I1310815967 |
| authorships[0].institutions[0].country_code | CZ |
| authorships[0].institutions[0].display_name | Central European Institute of Technology |
| authorships[0].institutions[1].id | https://openalex.org/I4210090417 |
| authorships[0].institutions[1].ror | https://ror.org/009nz6031 |
| authorships[0].institutions[1].type | education |
| authorships[0].institutions[1].lineage | https://openalex.org/I1310815967, https://openalex.org/I21449261, https://openalex.org/I4210090417 |
| authorships[0].institutions[1].country_code | CZ |
| authorships[0].institutions[1].display_name | Central European Institute of Technology – Masaryk University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Sajid Ullah |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | Plant Sciences Core Facility, CEITEC—Central European Institute of Technology, Masaryk University, 60200 Brno, Czech Republic |
| authorships[1].author.id | https://openalex.org/A5021941784 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-0673-3873 |
| authorships[1].author.display_name | M. Henke |
| authorships[1].countries | CZ |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I1310815967, https://openalex.org/I4210090417 |
| authorships[1].affiliations[0].raw_affiliation_string | Plant Sciences Core Facility, CEITEC—Central European Institute of Technology, Masaryk University, 60200 Brno, Czech Republic |
| authorships[1].institutions[0].id | https://openalex.org/I1310815967 |
| authorships[1].institutions[0].ror | https://ror.org/01p7k1986 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I1310815967 |
| authorships[1].institutions[0].country_code | CZ |
| authorships[1].institutions[0].display_name | Central European Institute of Technology |
| authorships[1].institutions[1].id | https://openalex.org/I4210090417 |
| authorships[1].institutions[1].ror | https://ror.org/009nz6031 |
| authorships[1].institutions[1].type | education |
| authorships[1].institutions[1].lineage | https://openalex.org/I1310815967, https://openalex.org/I21449261, https://openalex.org/I4210090417 |
| authorships[1].institutions[1].country_code | CZ |
| authorships[1].institutions[1].display_name | Central European Institute of Technology – Masaryk University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Michael Henke |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Plant Sciences Core Facility, CEITEC—Central European Institute of Technology, Masaryk University, 60200 Brno, Czech Republic |
| authorships[2].author.id | https://openalex.org/A5015034486 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-7584-9461 |
| authorships[2].author.display_name | Narendra Narisetti |
| authorships[2].countries | DE |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I4210127902 |
| authorships[2].affiliations[0].raw_affiliation_string | Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Gatersleben, Germany |
| authorships[2].institutions[0].id | https://openalex.org/I4210127902 |
| authorships[2].institutions[0].ror | https://ror.org/02skbsp27 |
| authorships[2].institutions[0].type | facility |
| authorships[2].institutions[0].lineage | https://openalex.org/I315704651, https://openalex.org/I4210127902 |
| authorships[2].institutions[0].country_code | DE |
| authorships[2].institutions[0].display_name | Leibniz Institute of Plant Genetics and Crop Plant Research |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Narendra Narisetti |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Gatersleben, Germany |
| authorships[3].author.id | https://openalex.org/A5066155028 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-7519-5162 |
| authorships[3].author.display_name | Klará Panzarová |
| authorships[3].countries | CZ |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I4210144831 |
| authorships[3].affiliations[0].raw_affiliation_string | PSI (Photon Systems Instruments), spol. s r.o., 66424 Drasov, Czech Republic |
| authorships[3].institutions[0].id | https://openalex.org/I4210144831 |
| authorships[3].institutions[0].ror | https://ror.org/03ef7g429 |
| authorships[3].institutions[0].type | company |
| authorships[3].institutions[0].lineage | https://openalex.org/I4210144831 |
| authorships[3].institutions[0].country_code | CZ |
| authorships[3].institutions[0].display_name | Photon Systems Instruments (Czechia) |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Klára Panzarová |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | PSI (Photon Systems Instruments), spol. s r.o., 66424 Drasov, Czech Republic |
| authorships[4].author.id | https://openalex.org/A5080583665 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-6294-052X |
| authorships[4].author.display_name | Martin Trtílek |
| authorships[4].countries | CZ |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I4210144831 |
| authorships[4].affiliations[0].raw_affiliation_string | PSI (Photon Systems Instruments), spol. s r.o., 66424 Drasov, Czech Republic |
| authorships[4].institutions[0].id | https://openalex.org/I4210144831 |
| authorships[4].institutions[0].ror | https://ror.org/03ef7g429 |
| authorships[4].institutions[0].type | company |
| authorships[4].institutions[0].lineage | https://openalex.org/I4210144831 |
| authorships[4].institutions[0].country_code | CZ |
| authorships[4].institutions[0].display_name | Photon Systems Instruments (Czechia) |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Martin Trtílek |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | PSI (Photon Systems Instruments), spol. s r.o., 66424 Drasov, Czech Republic |
| authorships[5].author.id | https://openalex.org/A5034888758 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-2622-6046 |
| authorships[5].author.display_name | Jan Hejátko |
| authorships[5].countries | CZ |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I1310815967, https://openalex.org/I4210090417 |
| authorships[5].affiliations[0].raw_affiliation_string | Plant Sciences Core Facility, CEITEC—Central European Institute of Technology, Masaryk University, 60200 Brno, Czech Republic |
| authorships[5].institutions[0].id | https://openalex.org/I1310815967 |
| authorships[5].institutions[0].ror | https://ror.org/01p7k1986 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I1310815967 |
| authorships[5].institutions[0].country_code | CZ |
| authorships[5].institutions[0].display_name | Central European Institute of Technology |
| authorships[5].institutions[1].id | https://openalex.org/I4210090417 |
| authorships[5].institutions[1].ror | https://ror.org/009nz6031 |
| authorships[5].institutions[1].type | education |
| authorships[5].institutions[1].lineage | https://openalex.org/I1310815967, https://openalex.org/I21449261, https://openalex.org/I4210090417 |
| authorships[5].institutions[1].country_code | CZ |
| authorships[5].institutions[1].display_name | Central European Institute of Technology – Masaryk University |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Jan Hejatko |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Plant Sciences Core Facility, CEITEC—Central European Institute of Technology, Masaryk University, 60200 Brno, Czech Republic |
| authorships[6].author.id | https://openalex.org/A5070897424 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-6153-727X |
| authorships[6].author.display_name | Evgeny Gladilin |
| authorships[6].countries | DE |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I4210127902 |
| authorships[6].affiliations[0].raw_affiliation_string | Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Gatersleben, Germany |
| authorships[6].institutions[0].id | https://openalex.org/I4210127902 |
| authorships[6].institutions[0].ror | https://ror.org/02skbsp27 |
| authorships[6].institutions[0].type | facility |
| authorships[6].institutions[0].lineage | https://openalex.org/I315704651, https://openalex.org/I4210127902 |
| authorships[6].institutions[0].country_code | DE |
| authorships[6].institutions[0].display_name | Leibniz Institute of Plant Genetics and Crop Plant Research |
| authorships[6].author_position | last |
| authorships[6].raw_author_name | Evgeny Gladilin |
| authorships[6].is_corresponding | True |
| authorships[6].raw_affiliation_strings | Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Gatersleben, Germany |
| 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/1424-8220/21/22/7441/pdf?version=1636458973 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Towards Automated Analysis of Grain Spikes in Greenhouse Images Using Neural Network Approaches: A Comparative Investigation of Six Methods |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11796 |
| primary_topic.field.id | https://openalex.org/fields/11 |
| primary_topic.field.display_name | Agricultural and Biological Sciences |
| primary_topic.score | 0.998199999332428 |
| 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 | Horticultural and Viticultural Research |
| related_works | https://openalex.org/W2174331923, https://openalex.org/W2050175337, https://openalex.org/W4408447381, https://openalex.org/W2329992713, https://openalex.org/W3050304047, https://openalex.org/W2317338006, https://openalex.org/W2385487847, https://openalex.org/W2062974934, https://openalex.org/W1997160662, https://openalex.org/W1522196789 |
| cited_by_count | 6 |
| counts_by_year[0].year | 2024 |
| counts_by_year[0].cited_by_count | 4 |
| counts_by_year[1].year | 2023 |
| counts_by_year[1].cited_by_count | 1 |
| counts_by_year[2].year | 2022 |
| counts_by_year[2].cited_by_count | 1 |
| locations_count | 7 |
| best_oa_location.id | doi:10.3390/s21227441 |
| 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/1424-8220/21/22/7441/pdf?version=1636458973 |
| 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/s21227441 |
| primary_location.id | doi:10.3390/s21227441 |
| 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/1424-8220/21/22/7441/pdf?version=1636458973 |
| 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/s21227441 |
| publication_date | 2021-11-09 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W2891869273, https://openalex.org/W2120202249, https://openalex.org/W2901871634, https://openalex.org/W3009441850, https://openalex.org/W2783264619, https://openalex.org/W2903707524, https://openalex.org/W2925510773, https://openalex.org/W2743389070, https://openalex.org/W2337787003, https://openalex.org/W3012947883, https://openalex.org/W2767103329, https://openalex.org/W2761162227, https://openalex.org/W3036736187, https://openalex.org/W6684191040, https://openalex.org/W2186094539, https://openalex.org/W2108598243, https://openalex.org/W2193145675, https://openalex.org/W2088049833, https://openalex.org/W2031489346, https://openalex.org/W6620707391, https://openalex.org/W2194775991, https://openalex.org/W2168977926, https://openalex.org/W2143058286, https://openalex.org/W1901129140, https://openalex.org/W2964309882, https://openalex.org/W4251033893, https://openalex.org/W2557728737, https://openalex.org/W2964098128, https://openalex.org/W3104610662, https://openalex.org/W639708223, https://openalex.org/W2618530766, https://openalex.org/W2412782625, https://openalex.org/W2163605009, https://openalex.org/W3106250896 |
| referenced_works_count | 34 |
| abstract_inverted_index.A | 72 |
| abstract_inverted_index.a | 61, 122, 214, 222, 226, 258 |
| abstract_inverted_index.To | 101 |
| abstract_inverted_index.as | 10 |
| abstract_inverted_index.be | 94, 182 |
| abstract_inverted_index.by | 80, 162 |
| abstract_inverted_index.in | 16, 42, 57, 69, 112, 135, 168, 177, 278 |
| abstract_inverted_index.is | 13, 78 |
| abstract_inverted_index.of | 2, 29, 37, 50, 87, 105, 108, 125, 166, 193, 209, 225, 229, 240, 266, 280 |
| abstract_inverted_index.on | 26, 34, 221 |
| abstract_inverted_index.to | 216, 270 |
| abstract_inverted_index.we | 120, 256 |
| abstract_inverted_index.90% | 160 |
| abstract_inverted_index.Our | 146 |
| abstract_inverted_index.RGB | 136 |
| abstract_inverted_index.all | 51 |
| abstract_inverted_index.and | 4, 20, 47, 53, 116, 133, 141, 164, 171, 191, 197, 236, 253, 276 |
| abstract_inverted_index.are | 249 |
| abstract_inverted_index.but | 83 |
| abstract_inverted_index.can | 92, 181 |
| abstract_inverted_index.few | 227 |
| abstract_inverted_index.for | 75, 130, 200, 245 |
| abstract_inverted_index.new | 178 |
| abstract_inverted_index.one | 142 |
| abstract_inverted_index.rye | 172 |
| abstract_inverted_index.set | 208, 224 |
| abstract_inverted_index.six | 126 |
| abstract_inverted_index.the | 27, 35, 38, 70, 97, 103, 189, 206, 241, 264 |
| abstract_inverted_index.top | 36 |
| abstract_inverted_index.was | 66 |
| abstract_inverted_index.also | 84 |
| abstract_inverted_index.crop | 89, 179 |
| abstract_inverted_index.deep | 140, 152 |
| abstract_inverted_index.five | 139 |
| abstract_inverted_index.from | 96, 205 |
| abstract_inverted_index.more | 62, 184 |
| abstract_inverted_index.over | 159 |
| abstract_inverted_index.show | 155 |
| abstract_inverted_index.such | 9 |
| abstract_inverted_index.than | 186 |
| abstract_inverted_index.that | 91, 150 |
| abstract_inverted_index.tool | 260 |
| abstract_inverted_index.bushy | 88 |
| abstract_inverted_index.crops | 115 |
| abstract_inverted_index.field | 43 |
| abstract_inverted_index.fully | 52, 271 |
| abstract_inverted_index.grain | 11, 39, 114 |
| abstract_inverted_index.here, | 119 |
| abstract_inverted_index.image | 76, 247 |
| abstract_inverted_index.past. | 71 |
| abstract_inverted_index.plant | 7, 18, 99 |
| abstract_inverted_index.shows | 263 |
| abstract_inverted_index.small | 3 |
| abstract_inverted_index.spike | 110, 175, 246, 273 |
| abstract_inverted_index.still | 212 |
| abstract_inverted_index.task, | 64 |
| abstract_inverted_index.truth | 233 |
| abstract_inverted_index.which | 65, 201, 262 |
| abstract_inverted_index.works | 23 |
| abstract_inverted_index.barley | 170 |
| abstract_inverted_index.ground | 232 |
| abstract_inverted_index.hardly | 93 |
| abstract_inverted_index.highly | 14 |
| abstract_inverted_index.images | 59, 279 |
| abstract_inverted_index.models | 219 |
| abstract_inverted_index.neural | 127, 144, 217, 268 |
| abstract_inverted_index.plants | 40 |
| abstract_inverted_index.rarely | 67 |
| abstract_inverted_index.spikes | 32, 56, 86, 167 |
| abstract_inverted_index.wheat, | 169 |
| abstract_inverted_index.Besides | 251 |
| abstract_inverted_index.address | 102 |
| abstract_inverted_index.deviate | 204 |
| abstract_inverted_index.focused | 25 |
| abstract_inverted_index.further | 237 |
| abstract_inverted_index.growing | 41 |
| abstract_inverted_index.images, | 137 |
| abstract_inverted_index.images. | 173, 234 |
| abstract_inverted_index.labeled | 231 |
| abstract_inverted_index.limited | 223 |
| abstract_inverted_index.matured | 85 |
| abstract_inverted_index.methods | 129, 154 |
| abstract_inverted_index.network | 128, 218 |
| abstract_inverted_index.optical | 117 |
| abstract_inverted_index.organs, | 8 |
| abstract_inverted_index.pattern | 131 |
| abstract_inverted_index.plants. | 282 |
| abstract_inverted_index.provide | 257 |
| abstract_inverted_index.regular | 210 |
| abstract_inverted_index.renders | 60 |
| abstract_inverted_index.results | 148 |
| abstract_inverted_index.science | 19 |
| abstract_inverted_index.setups, | 118 |
| abstract_inverted_index.shallow | 143 |
| abstract_inverted_index.spikes, | 12, 199, 211 |
| abstract_inverted_index.trained | 220 |
| abstract_inverted_index.visible | 31, 55, 196 |
| abstract_inverted_index.However, | 45, 174 |
| abstract_inverted_index.Previous | 22 |
| abstract_inverted_index.accuracy | 161 |
| abstract_inverted_index.accurate | 46 |
| abstract_inverted_index.advanced | 151 |
| abstract_inverted_index.analysis | 1, 49, 77, 107, 248 |
| abstract_inverted_index.automate | 272 |
| abstract_inverted_index.biomass. | 100 |
| abstract_inverted_index.demanded | 15 |
| abstract_inverted_index.emerging | 33 |
| abstract_inverted_index.hundreds | 228 |
| abstract_inverted_index.learning | 153 |
| abstract_inverted_index.manually | 230 |
| abstract_inverted_index.matured, | 194 |
| abstract_inverted_index.network. | 145 |
| abstract_inverted_index.networks | 269 |
| abstract_inverted_index.occluded | 82, 198 |
| abstract_inverted_index.superior | 156 |
| abstract_inverted_index.training | 207 |
| abstract_inverted_index.variable | 6 |
| abstract_inverted_index.Automated | 0 |
| abstract_inverted_index.GUI-based | 259 |
| abstract_inverted_index.achieving | 158 |
| abstract_inverted_index.addressed | 68 |
| abstract_inverted_index.arbitrary | 109 |
| abstract_inverted_index.automated | 48, 106 |
| abstract_inverted_index.breeding. | 21 |
| abstract_inverted_index.challenge | 104, 215 |
| abstract_inverted_index.cultivars | 90 |
| abstract_inverted_index.detection | 28, 132, 163, 176, 190 |
| abstract_inverted_index.different | 113 |
| abstract_inverted_index.including | 138 |
| abstract_inverted_index.optically | 5 |
| abstract_inverted_index.partially | 54, 195 |
| abstract_inverted_index.performed | 121, 183 |
| abstract_inverted_index.potential | 238 |
| abstract_inverted_index.presented | 242 |
| abstract_inverted_index.primarily | 24 |
| abstract_inverted_index.remaining | 98 |
| abstract_inverted_index.represent | 213 |
| abstract_inverted_index.accurately | 185 |
| abstract_inverted_index.detection, | 274 |
| abstract_inverted_index.difficulty | 74 |
| abstract_inverted_index.discussed. | 250 |
| abstract_inverted_index.frameworks | 244 |
| abstract_inverted_index.greenhouse | 58 |
| abstract_inverted_index.particular | 73 |
| abstract_inverted_index.phenotypes | 111, 180, 202 |
| abstract_inverted_index.(SpikeApp), | 261 |
| abstract_inverted_index.Limitations | 235 |
| abstract_inverted_index.algorithmic | 243 |
| abstract_inverted_index.application | 265 |
| abstract_inverted_index.challenging | 63 |
| abstract_inverted_index.comparative | 123 |
| abstract_inverted_index.conditions. | 44 |
| abstract_inverted_index.demonstrate | 149 |
| abstract_inverted_index.phenotyping | 277 |
| abstract_inverted_index.pre-trained | 267 |
| abstract_inverted_index.prominently | 30 |
| abstract_inverted_index.represented | 79 |
| abstract_inverted_index.theoretical | 252 |
| abstract_inverted_index.Furthermore, | 188 |
| abstract_inverted_index.experimental | 147, 254 |
| abstract_inverted_index.improvements | 239 |
| abstract_inverted_index.performance, | 157 |
| abstract_inverted_index.quantitative | 17 |
| abstract_inverted_index.segmentation | 134, 165, 192, 275 |
| abstract_inverted_index.investigation | 124 |
| abstract_inverted_index.leaf-covered, | 81 |
| abstract_inverted_index.segmentation. | 187 |
| abstract_inverted_index.substantially | 203 |
| abstract_inverted_index.differentiated | 95 |
| abstract_inverted_index.investigations, | 255 |
| abstract_inverted_index.greenhouse-grown | 281 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5101451622, https://openalex.org/A5070897424 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 7 |
| corresponding_institution_ids | https://openalex.org/I1310815967, https://openalex.org/I4210090417, https://openalex.org/I4210127902 |
| citation_normalized_percentile.value | 0.83931743 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |