Mango Fruit Load Estimation Using a Video Based MangoYOLO—Kalman Filter—Hungarian Algorithm Method Article Swipe
Zhenglin Wang
,
Kerry B. Walsh
,
Anand Koirala
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.3390/s19122742
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.3390/s19122742
Pre-harvest fruit yield estimation is useful to guide harvesting and marketing resourcing, but machine vision estimates based on a single view from each side of the tree (“dual-view”) underestimates the fruit yield as fruit can be hidden from view. A method is proposed involving deep learning, Kalman filter, and Hungarian algorithm for on-tree mango fruit detection, tracking, and counting from 10 frame-per-second videos captured of trees from a platform moving along the inter row at 5 km/h. The deep learning based mango fruit detection algorithm, MangoYOLO, was used to detect fruit in each frame. The Hungarian algorithm was used to correlate fruit between neighbouring frames, with the improvement of enabling multiple-to-one assignment. The Kalman filter was used to predict the position of fruit in following frames, to avoid multiple counts of a single fruit that is obscured or otherwise not detected with a frame series. A “borrow” concept was added to the Kalman filter to predict fruit position when its precise prediction model was absent, by borrowing the horizontal and vertical speed from neighbouring fruit. By comparison with human count for a video with 110 frames and 192 (human count) fruit, the method produced 9.9% double counts and 7.3% missing count errors, resulting in around 2.6% over count. In another test, a video (of 1162 frames, with 42 images centred on the tree trunk) was acquired of both sides of a row of 21 trees, for which the harvest fruit count was 3286 (i.e., average of 156 fruit/tree). The trees had thick canopies, such that the proportion of fruit hidden from view from any given perspective was high. The proposed method recorded 2050 fruit (62% of harvest) with a bias corrected Root Mean Square Error (RMSE) = 18.0 fruit/tree while the dual-view image method (also using MangoYOLO) recorded 1322 fruit (40%) with a bias corrected RMSE = 21.7 fruit/tree. The video tracking system is recommended over the dual-view imaging system for mango orchard fruit count.
Related Topics
Concepts
Kalman filter
Moving horizon estimation
Computer science
Estimation
Extended Kalman filter
Algorithm
Artificial intelligence
Engineering
Systems engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s19122742
- https://www.mdpi.com/1424-8220/19/12/2742/pdf?version=1561195227
- OA Status
- gold
- Cited By
- 109
- References
- 51
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2949837051
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2949837051Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s19122742Digital Object Identifier
- Title
-
Mango Fruit Load Estimation Using a Video Based MangoYOLO—Kalman Filter—Hungarian Algorithm MethodWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-06-18Full publication date if available
- Authors
-
Zhenglin Wang, Kerry B. Walsh, Anand KoiralaList of authors in order
- Landing page
-
https://doi.org/10.3390/s19122742Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/19/12/2742/pdf?version=1561195227Direct 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/1424-8220/19/12/2742/pdf?version=1561195227Direct OA link when available
- Concepts
-
Kalman filter, Moving horizon estimation, Computer science, Estimation, Extended Kalman filter, Algorithm, Artificial intelligence, Engineering, Systems engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
109Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 18, 2024: 26, 2023: 29, 2022: 19, 2021: 11Per-year citation counts (last 5 years)
- References (count)
-
51Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W2949837051 |
|---|---|
| doi | https://doi.org/10.3390/s19122742 |
| ids.doi | https://doi.org/10.3390/s19122742 |
| ids.mag | 2949837051 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/31216769 |
| ids.openalex | https://openalex.org/W2949837051 |
| fwci | 6.71101711 |
| mesh[0].qualifier_ui | |
| mesh[0].descriptor_ui | D000465 |
| mesh[0].is_major_topic | False |
| mesh[0].qualifier_name | |
| mesh[0].descriptor_name | Algorithms |
| mesh[1].qualifier_ui | |
| mesh[1].descriptor_ui | D000077321 |
| mesh[1].is_major_topic | True |
| mesh[1].qualifier_name | |
| mesh[1].descriptor_name | Deep Learning |
| mesh[2].qualifier_ui | Q000254 |
| mesh[2].descriptor_ui | D005638 |
| mesh[2].is_major_topic | False |
| mesh[2].qualifier_name | growth & development |
| mesh[2].descriptor_name | Fruit |
| mesh[3].qualifier_ui | |
| mesh[3].descriptor_ui | D006801 |
| mesh[3].is_major_topic | False |
| mesh[3].qualifier_name | |
| mesh[3].descriptor_name | Humans |
| mesh[4].qualifier_ui | Q000254 |
| mesh[4].descriptor_ui | D031022 |
| mesh[4].is_major_topic | False |
| mesh[4].qualifier_name | growth & development |
| mesh[4].descriptor_name | Mangifera |
| mesh[5].qualifier_ui | Q000254 |
| mesh[5].descriptor_ui | D014197 |
| mesh[5].is_major_topic | False |
| mesh[5].qualifier_name | growth & development |
| mesh[5].descriptor_name | Trees |
| mesh[6].qualifier_ui | |
| mesh[6].descriptor_ui | D014741 |
| mesh[6].is_major_topic | False |
| mesh[6].qualifier_name | |
| mesh[6].descriptor_name | Video Recording |
| 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 | D000077321 |
| mesh[8].is_major_topic | True |
| mesh[8].qualifier_name | |
| mesh[8].descriptor_name | Deep Learning |
| mesh[9].qualifier_ui | Q000254 |
| mesh[9].descriptor_ui | D005638 |
| mesh[9].is_major_topic | False |
| mesh[9].qualifier_name | growth & development |
| mesh[9].descriptor_name | Fruit |
| mesh[10].qualifier_ui | |
| mesh[10].descriptor_ui | D006801 |
| mesh[10].is_major_topic | False |
| mesh[10].qualifier_name | |
| mesh[10].descriptor_name | Humans |
| mesh[11].qualifier_ui | Q000254 |
| mesh[11].descriptor_ui | D031022 |
| mesh[11].is_major_topic | False |
| mesh[11].qualifier_name | growth & development |
| mesh[11].descriptor_name | Mangifera |
| mesh[12].qualifier_ui | Q000254 |
| mesh[12].descriptor_ui | D014197 |
| mesh[12].is_major_topic | False |
| mesh[12].qualifier_name | growth & development |
| mesh[12].descriptor_name | Trees |
| mesh[13].qualifier_ui | |
| mesh[13].descriptor_ui | D014741 |
| mesh[13].is_major_topic | False |
| mesh[13].qualifier_name | |
| mesh[13].descriptor_name | Video Recording |
| type | article |
| title | Mango Fruit Load Estimation Using a Video Based MangoYOLO—Kalman Filter—Hungarian Algorithm Method |
| biblio.issue | 12 |
| biblio.volume | 19 |
| biblio.last_page | 2742 |
| biblio.first_page | 2742 |
| topics[0].id | https://openalex.org/T14365 |
| topics[0].field.id | https://openalex.org/fields/11 |
| topics[0].field.display_name | Agricultural and Biological Sciences |
| topics[0].score | 0.9987999796867371 |
| 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 | Leaf Properties and Growth Measurement |
| 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 | 0.9976999759674072 |
| 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/T12729 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9609000086784363 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2210 |
| topics[2].subfield.display_name | Mechanical Engineering |
| topics[2].display_name | Tree Root and Stability Studies |
| 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/C157286648 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8111313581466675 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q846780 |
| concepts[0].display_name | Kalman filter |
| concepts[1].id | https://openalex.org/C50050547 |
| concepts[1].level | 4 |
| concepts[1].score | 0.6083211898803711 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q6927137 |
| concepts[1].display_name | Moving horizon estimation |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.5610447525978088 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C96250715 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5450263023376465 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q965330 |
| concepts[3].display_name | Estimation |
| concepts[4].id | https://openalex.org/C206833254 |
| concepts[4].level | 3 |
| concepts[4].score | 0.507986843585968 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q5421817 |
| concepts[4].display_name | Extended Kalman filter |
| concepts[5].id | https://openalex.org/C11413529 |
| concepts[5].level | 1 |
| concepts[5].score | 0.46593040227890015 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[5].display_name | Algorithm |
| concepts[6].id | https://openalex.org/C154945302 |
| concepts[6].level | 1 |
| concepts[6].score | 0.31820932030677795 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[6].display_name | Artificial intelligence |
| concepts[7].id | https://openalex.org/C127413603 |
| concepts[7].level | 0 |
| concepts[7].score | 0.23747330904006958 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[7].display_name | Engineering |
| concepts[8].id | https://openalex.org/C201995342 |
| concepts[8].level | 1 |
| concepts[8].score | 0.0 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q682496 |
| concepts[8].display_name | Systems engineering |
| keywords[0].id | https://openalex.org/keywords/kalman-filter |
| keywords[0].score | 0.8111313581466675 |
| keywords[0].display_name | Kalman filter |
| keywords[1].id | https://openalex.org/keywords/moving-horizon-estimation |
| keywords[1].score | 0.6083211898803711 |
| keywords[1].display_name | Moving horizon estimation |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.5610447525978088 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/estimation |
| keywords[3].score | 0.5450263023376465 |
| keywords[3].display_name | Estimation |
| keywords[4].id | https://openalex.org/keywords/extended-kalman-filter |
| keywords[4].score | 0.507986843585968 |
| keywords[4].display_name | Extended Kalman filter |
| keywords[5].id | https://openalex.org/keywords/algorithm |
| keywords[5].score | 0.46593040227890015 |
| keywords[5].display_name | Algorithm |
| keywords[6].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[6].score | 0.31820932030677795 |
| keywords[6].display_name | Artificial intelligence |
| keywords[7].id | https://openalex.org/keywords/engineering |
| keywords[7].score | 0.23747330904006958 |
| keywords[7].display_name | Engineering |
| language | en |
| locations[0].id | doi:10.3390/s19122742 |
| 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/1424-8220/19/12/2742/pdf?version=1561195227 |
| 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/s19122742 |
| locations[1].id | pmid:31216769 |
| 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/31216769 |
| locations[2].id | pmh:oai:acquire.cqu.edu.au:cqu:18585 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306400635 |
| 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 | Acquire (CQUniversity) |
| locations[2].source.host_organization | https://openalex.org/I74899385 |
| locations[2].source.host_organization_name | Central Queensland University |
| locations[2].source.host_organization_lineage | https://openalex.org/I74899385 |
| locations[2].license | cc-by |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | journal article |
| 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 | Wang, Z, Walsh, KB, Koirala, A, (2019). Mango fruit load estimation using a video based MangoYOLO-Kalman filter-Hungarian algorithm method.. Sensors (Basel, Switzerland), Vol. 19, No. 12, p. 1-17 http://dx.doi.org/10.3390/s19122742 |
| locations[2].landing_page_url | http://hdl.cqu.edu.au/10018/1309006 |
| locations[3].id | pmh:oai:doaj.org/article:328e543801f64b1e8475ebe1da663049 |
| locations[3].is_oa | True |
| 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 | cc-by-sa |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | article |
| locations[3].license_id | https://openalex.org/licenses/cc-by-sa |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | Sensors, Vol 19, Iss 12, p 2742 (2019) |
| locations[3].landing_page_url | https://doaj.org/article/328e543801f64b1e8475ebe1da663049 |
| locations[4].id | pmh:oai:figshare.com:article/13453205 |
| locations[4].is_oa | True |
| locations[4].source.id | https://openalex.org/S4306400572 |
| locations[4].source.issn | |
| locations[4].source.type | repository |
| locations[4].source.is_oa | False |
| locations[4].source.issn_l | |
| locations[4].source.is_core | False |
| locations[4].source.is_in_doaj | False |
| locations[4].source.display_name | OPAL (Open@LaTrobe) (La Trobe University) |
| locations[4].source.host_organization | https://openalex.org/I196829312 |
| locations[4].source.host_organization_name | La Trobe University |
| locations[4].source.host_organization_lineage | https://openalex.org/I196829312 |
| 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 | |
| locations[4].landing_page_url | https://figshare.com/articles/journal_contribution/Mango_fruit_load_estimation_using_a_video_based_MangoYOLO-Kalman_filter-Hungarian_algorithm_method_/13453205 |
| locations[5].id | pmh:oai:mdpi.com:/1424-8220/19/12/2742/ |
| locations[5].is_oa | True |
| locations[5].source.id | https://openalex.org/S4306400947 |
| locations[5].source.issn | |
| locations[5].source.type | repository |
| locations[5].source.is_oa | True |
| locations[5].source.issn_l | |
| locations[5].source.is_core | False |
| locations[5].source.is_in_doaj | False |
| locations[5].source.display_name | MDPI (MDPI AG) |
| locations[5].source.host_organization | https://openalex.org/I4210097602 |
| locations[5].source.host_organization_name | Multidisciplinary Digital Publishing Institute (Switzerland) |
| locations[5].source.host_organization_lineage | https://openalex.org/I4210097602 |
| locations[5].license | cc-by |
| locations[5].pdf_url | |
| locations[5].version | submittedVersion |
| locations[5].raw_type | Text |
| 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 | Sensors |
| locations[5].landing_page_url | http://dx.doi.org/10.3390/s19122742 |
| locations[6].id | pmh:oai:pubmedcentral.nih.gov:6631562 |
| 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/6631562 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5026795459 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-5812-2989 |
| authorships[0].author.display_name | Zhenglin Wang |
| authorships[0].countries | AU |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I74899385 |
| authorships[0].affiliations[0].raw_affiliation_string | Centre for Intelligent Systems, Central Queensland University, Rockhampton North 4701, Australia |
| authorships[0].institutions[0].id | https://openalex.org/I74899385 |
| authorships[0].institutions[0].ror | https://ror.org/023q4bk22 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I74899385 |
| authorships[0].institutions[0].country_code | AU |
| authorships[0].institutions[0].display_name | Central Queensland University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Zhenglin Wang |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | Centre for Intelligent Systems, Central Queensland University, Rockhampton North 4701, Australia |
| authorships[1].author.id | https://openalex.org/A5081837621 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-3033-8622 |
| authorships[1].author.display_name | Kerry B. Walsh |
| authorships[1].countries | AU |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I74899385 |
| authorships[1].affiliations[0].raw_affiliation_string | Institute of Future Farming Systems, Central Queensland University, Rockhampton North 4701, Australia |
| authorships[1].institutions[0].id | https://openalex.org/I74899385 |
| authorships[1].institutions[0].ror | https://ror.org/023q4bk22 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I74899385 |
| authorships[1].institutions[0].country_code | AU |
| authorships[1].institutions[0].display_name | Central Queensland University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Kerry Walsh |
| authorships[1].is_corresponding | True |
| authorships[1].raw_affiliation_strings | Institute of Future Farming Systems, Central Queensland University, Rockhampton North 4701, Australia |
| authorships[2].author.id | https://openalex.org/A5054056327 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-7376-2143 |
| authorships[2].author.display_name | Anand Koirala |
| authorships[2].countries | AU |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I74899385 |
| authorships[2].affiliations[0].raw_affiliation_string | Institute of Future Farming Systems, Central Queensland University, Rockhampton North 4701, Australia |
| authorships[2].institutions[0].id | https://openalex.org/I74899385 |
| authorships[2].institutions[0].ror | https://ror.org/023q4bk22 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I74899385 |
| authorships[2].institutions[0].country_code | AU |
| authorships[2].institutions[0].display_name | Central Queensland University |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Anand Koirala |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Institute of Future Farming Systems, Central Queensland University, Rockhampton North 4701, Australia |
| 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/1424-8220/19/12/2742/pdf?version=1561195227 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Mango Fruit Load Estimation Using a Video Based MangoYOLO—Kalman Filter—Hungarian Algorithm Method |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T14365 |
| primary_topic.field.id | https://openalex.org/fields/11 |
| primary_topic.field.display_name | Agricultural and Biological Sciences |
| primary_topic.score | 0.9987999796867371 |
| 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 | Leaf Properties and Growth Measurement |
| related_works | https://openalex.org/W2103062922, https://openalex.org/W2162299404, https://openalex.org/W2674597762, https://openalex.org/W2562855176, https://openalex.org/W4300640027, https://openalex.org/W1506303183, https://openalex.org/W2328410657, https://openalex.org/W2064665624, https://openalex.org/W2258371975, https://openalex.org/W1601369057 |
| cited_by_count | 109 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 18 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 26 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 29 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 19 |
| counts_by_year[4].year | 2021 |
| counts_by_year[4].cited_by_count | 11 |
| counts_by_year[5].year | 2020 |
| counts_by_year[5].cited_by_count | 5 |
| counts_by_year[6].year | 2019 |
| counts_by_year[6].cited_by_count | 1 |
| locations_count | 7 |
| best_oa_location.id | doi:10.3390/s19122742 |
| 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/1424-8220/19/12/2742/pdf?version=1561195227 |
| 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/s19122742 |
| primary_location.id | doi:10.3390/s19122742 |
| 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/1424-8220/19/12/2742/pdf?version=1561195227 |
| 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/s19122742 |
| publication_date | 2019-06-18 |
| publication_year | 2019 |
| referenced_works | https://openalex.org/W2904714431, https://openalex.org/W1607148716, https://openalex.org/W2897332308, https://openalex.org/W1970635035, https://openalex.org/W2018563411, https://openalex.org/W2035032102, https://openalex.org/W2145023731, https://openalex.org/W2130103520, https://openalex.org/W2034501924, https://openalex.org/W1563062848, https://openalex.org/W2124386111, https://openalex.org/W1677409904, https://openalex.org/W6683411478, https://openalex.org/W1545641654, https://openalex.org/W2094766409, https://openalex.org/W2156585878, https://openalex.org/W2412887352, https://openalex.org/W2163605009, https://openalex.org/W2194775991, https://openalex.org/W2097117768, https://openalex.org/W1849277567, https://openalex.org/W2936307272, https://openalex.org/W2501369945, https://openalex.org/W639708223, https://openalex.org/W2578363764, https://openalex.org/W1903029394, https://openalex.org/W2963523428, https://openalex.org/W1980180011, https://openalex.org/W2091119928, https://openalex.org/W2555576940, https://openalex.org/W2768210762, https://openalex.org/W2920621226, https://openalex.org/W2963037989, https://openalex.org/W2570343428, https://openalex.org/W2809302475, https://openalex.org/W2898675454, https://openalex.org/W2154889144, https://openalex.org/W1578285471, https://openalex.org/W2102889695, https://openalex.org/W4205587597, https://openalex.org/W2888108417, https://openalex.org/W2809489825, https://openalex.org/W3194772591, https://openalex.org/W6680096826, https://openalex.org/W2603554833, https://openalex.org/W2134974756, https://openalex.org/W2794080056, https://openalex.org/W2133059825, https://openalex.org/W1591848543, https://openalex.org/W3104664645, https://openalex.org/W2161969291 |
| referenced_works_count | 51 |
| abstract_inverted_index.5 | 75 |
| abstract_inverted_index.= | 286, 306 |
| abstract_inverted_index.A | 39, 145 |
| abstract_inverted_index.a | 18, 67, 131, 142, 181, 211, 230, 278, 302 |
| abstract_inverted_index.10 | 60 |
| abstract_inverted_index.21 | 233 |
| abstract_inverted_index.42 | 217 |
| abstract_inverted_index.By | 175 |
| abstract_inverted_index.In | 208 |
| abstract_inverted_index.as | 32 |
| abstract_inverted_index.at | 74 |
| abstract_inverted_index.be | 35 |
| abstract_inverted_index.by | 165 |
| abstract_inverted_index.in | 91, 123, 203 |
| abstract_inverted_index.is | 4, 41, 135, 313 |
| abstract_inverted_index.of | 24, 64, 108, 121, 130, 226, 229, 232, 245, 257, 275 |
| abstract_inverted_index.on | 17, 220 |
| abstract_inverted_index.or | 137 |
| abstract_inverted_index.to | 6, 88, 99, 117, 126, 150, 154 |
| abstract_inverted_index.(of | 213 |
| abstract_inverted_index.110 | 184 |
| abstract_inverted_index.156 | 246 |
| abstract_inverted_index.192 | 187 |
| abstract_inverted_index.The | 77, 94, 112, 248, 268, 309 |
| abstract_inverted_index.and | 9, 48, 57, 169, 186, 197 |
| abstract_inverted_index.any | 263 |
| abstract_inverted_index.but | 12 |
| abstract_inverted_index.can | 34 |
| abstract_inverted_index.for | 51, 180, 235, 320 |
| abstract_inverted_index.had | 250 |
| abstract_inverted_index.its | 159 |
| abstract_inverted_index.not | 139 |
| abstract_inverted_index.row | 73, 231 |
| abstract_inverted_index.the | 25, 29, 71, 106, 119, 151, 167, 191, 221, 237, 255, 290, 316 |
| abstract_inverted_index.was | 86, 97, 115, 148, 163, 224, 241, 266 |
| abstract_inverted_index.(62% | 274 |
| abstract_inverted_index.1162 | 214 |
| abstract_inverted_index.1322 | 298 |
| abstract_inverted_index.18.0 | 287 |
| abstract_inverted_index.2.6% | 205 |
| abstract_inverted_index.2050 | 272 |
| abstract_inverted_index.21.7 | 307 |
| abstract_inverted_index.3286 | 242 |
| abstract_inverted_index.7.3% | 198 |
| abstract_inverted_index.9.9% | 194 |
| abstract_inverted_index.Mean | 282 |
| abstract_inverted_index.RMSE | 305 |
| abstract_inverted_index.Root | 281 |
| abstract_inverted_index.bias | 279, 303 |
| abstract_inverted_index.both | 227 |
| abstract_inverted_index.deep | 44, 78 |
| abstract_inverted_index.each | 22, 92 |
| abstract_inverted_index.from | 21, 37, 59, 66, 172, 260, 262 |
| abstract_inverted_index.over | 206, 315 |
| abstract_inverted_index.side | 23 |
| abstract_inverted_index.such | 253 |
| abstract_inverted_index.that | 134, 254 |
| abstract_inverted_index.tree | 26, 222 |
| abstract_inverted_index.used | 87, 98, 116 |
| abstract_inverted_index.view | 20, 261 |
| abstract_inverted_index.when | 158 |
| abstract_inverted_index.with | 105, 141, 177, 183, 216, 277, 301 |
| abstract_inverted_index.(40%) | 300 |
| abstract_inverted_index.(also | 294 |
| abstract_inverted_index.Error | 284 |
| abstract_inverted_index.added | 149 |
| abstract_inverted_index.along | 70 |
| abstract_inverted_index.avoid | 127 |
| abstract_inverted_index.based | 16, 80 |
| abstract_inverted_index.count | 179, 200, 240 |
| abstract_inverted_index.frame | 143 |
| abstract_inverted_index.fruit | 1, 30, 33, 54, 82, 90, 101, 122, 133, 156, 239, 258, 273, 299, 323 |
| abstract_inverted_index.given | 264 |
| abstract_inverted_index.guide | 7 |
| abstract_inverted_index.high. | 267 |
| abstract_inverted_index.human | 178 |
| abstract_inverted_index.image | 292 |
| abstract_inverted_index.inter | 72 |
| abstract_inverted_index.km/h. | 76 |
| abstract_inverted_index.mango | 53, 81, 321 |
| abstract_inverted_index.model | 162 |
| abstract_inverted_index.sides | 228 |
| abstract_inverted_index.speed | 171 |
| abstract_inverted_index.test, | 210 |
| abstract_inverted_index.thick | 251 |
| abstract_inverted_index.trees | 65, 249 |
| abstract_inverted_index.using | 295 |
| abstract_inverted_index.video | 182, 212, 310 |
| abstract_inverted_index.view. | 38 |
| abstract_inverted_index.which | 236 |
| abstract_inverted_index.while | 289 |
| abstract_inverted_index.yield | 2, 31 |
| abstract_inverted_index.(RMSE) | 285 |
| abstract_inverted_index.(human | 188 |
| abstract_inverted_index.(i.e., | 243 |
| abstract_inverted_index.Kalman | 46, 113, 152 |
| abstract_inverted_index.Square | 283 |
| abstract_inverted_index.around | 204 |
| abstract_inverted_index.count) | 189 |
| abstract_inverted_index.count. | 207, 324 |
| abstract_inverted_index.counts | 129, 196 |
| abstract_inverted_index.detect | 89 |
| abstract_inverted_index.double | 195 |
| abstract_inverted_index.filter | 114, 153 |
| abstract_inverted_index.frame. | 93 |
| abstract_inverted_index.frames | 185 |
| abstract_inverted_index.fruit, | 190 |
| abstract_inverted_index.fruit. | 174 |
| abstract_inverted_index.hidden | 36, 259 |
| abstract_inverted_index.images | 218 |
| abstract_inverted_index.method | 40, 192, 270, 293 |
| abstract_inverted_index.moving | 69 |
| abstract_inverted_index.single | 19, 132 |
| abstract_inverted_index.system | 312, 319 |
| abstract_inverted_index.trees, | 234 |
| abstract_inverted_index.trunk) | 223 |
| abstract_inverted_index.useful | 5 |
| abstract_inverted_index.videos | 62 |
| abstract_inverted_index.vision | 14 |
| abstract_inverted_index.absent, | 164 |
| abstract_inverted_index.another | 209 |
| abstract_inverted_index.average | 244 |
| abstract_inverted_index.between | 102 |
| abstract_inverted_index.centred | 219 |
| abstract_inverted_index.concept | 147 |
| abstract_inverted_index.errors, | 201 |
| abstract_inverted_index.filter, | 47 |
| abstract_inverted_index.frames, | 104, 125, 215 |
| abstract_inverted_index.harvest | 238 |
| abstract_inverted_index.imaging | 318 |
| abstract_inverted_index.machine | 13 |
| abstract_inverted_index.missing | 199 |
| abstract_inverted_index.on-tree | 52 |
| abstract_inverted_index.orchard | 322 |
| abstract_inverted_index.precise | 160 |
| abstract_inverted_index.predict | 118, 155 |
| abstract_inverted_index.series. | 144 |
| abstract_inverted_index.acquired | 225 |
| abstract_inverted_index.captured | 63 |
| abstract_inverted_index.counting | 58 |
| abstract_inverted_index.detected | 140 |
| abstract_inverted_index.enabling | 109 |
| abstract_inverted_index.harvest) | 276 |
| abstract_inverted_index.learning | 79 |
| abstract_inverted_index.multiple | 128 |
| abstract_inverted_index.obscured | 136 |
| abstract_inverted_index.platform | 68 |
| abstract_inverted_index.position | 120, 157 |
| abstract_inverted_index.produced | 193 |
| abstract_inverted_index.proposed | 42, 269 |
| abstract_inverted_index.recorded | 271, 297 |
| abstract_inverted_index.tracking | 311 |
| abstract_inverted_index.vertical | 170 |
| abstract_inverted_index.Hungarian | 49, 95 |
| abstract_inverted_index.algorithm | 50, 96 |
| abstract_inverted_index.borrowing | 166 |
| abstract_inverted_index.canopies, | 252 |
| abstract_inverted_index.corrected | 280, 304 |
| abstract_inverted_index.correlate | 100 |
| abstract_inverted_index.detection | 83 |
| abstract_inverted_index.dual-view | 291, 317 |
| abstract_inverted_index.estimates | 15 |
| abstract_inverted_index.following | 124 |
| abstract_inverted_index.involving | 43 |
| abstract_inverted_index.learning, | 45 |
| abstract_inverted_index.marketing | 10 |
| abstract_inverted_index.otherwise | 138 |
| abstract_inverted_index.resulting | 202 |
| abstract_inverted_index.tracking, | 56 |
| abstract_inverted_index.MangoYOLO) | 296 |
| abstract_inverted_index.MangoYOLO, | 85 |
| abstract_inverted_index.algorithm, | 84 |
| abstract_inverted_index.comparison | 176 |
| abstract_inverted_index.detection, | 55 |
| abstract_inverted_index.estimation | 3 |
| abstract_inverted_index.fruit/tree | 288 |
| abstract_inverted_index.harvesting | 8 |
| abstract_inverted_index.horizontal | 168 |
| abstract_inverted_index.prediction | 161 |
| abstract_inverted_index.proportion | 256 |
| abstract_inverted_index.Pre-harvest | 0 |
| abstract_inverted_index.assignment. | 111 |
| abstract_inverted_index.fruit/tree. | 308 |
| abstract_inverted_index.improvement | 107 |
| abstract_inverted_index.perspective | 265 |
| abstract_inverted_index.recommended | 314 |
| abstract_inverted_index.resourcing, | 11 |
| abstract_inverted_index.fruit/tree). | 247 |
| abstract_inverted_index.neighbouring | 103, 173 |
| abstract_inverted_index.“borrow” | 146 |
| abstract_inverted_index.underestimates | 28 |
| abstract_inverted_index.multiple-to-one | 110 |
| abstract_inverted_index.frame-per-second | 61 |
| abstract_inverted_index.(“dual-view”) | 27 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5026795459, https://openalex.org/A5081837621 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I74899385 |
| citation_normalized_percentile.value | 0.97884679 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |