Developing Machine Vision in Tree-Fruit Applications—Fruit Count, Fruit Size and Branch Avoidance in Automated Harvesting Article Swipe
Chiranjivi Neupane
,
Kerry B. Walsh
,
Rafael Goulart
,
Anand Koirala
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/s24175593
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/s24175593
Recent developments in affordable depth imaging hardware and the use of 2D Convolutional Neural Networks (CNN) in object detection and segmentation have accelerated the adoption of machine vision in a range of applications, with mainstream models often out-performing previous application-specific architectures. The need for the release of training and test datasets with any work reporting model development is emphasized to enable the re-evaluation of published work. An additional reporting need is the documentation of the performance of the re-training of a given model, quantifying the impact of stochastic processes in training. Three mango orchard applications were considered: the (i) fruit count, (ii) fruit size and (iii) branch avoidance in automated harvesting. All training and test datasets used in this work are available publicly. The mAP ‘coefficient of variation’ (Standard Deviation, SD, divided by mean of predictions using models of repeated trainings × 100) was approximately 0.2% for the fruit detection model and 1 and 2% for the fruit and branch segmentation models, respectively. A YOLOv8m model achieved a mAP50 of 99.3%, outperforming the previous benchmark, the purpose-designed ‘MangoYOLO’, for the application of the real-time detection of mango fruit on images of tree canopies using an edge computing device as a viable use case. YOLOv8 and v9 models outperformed the benchmark MaskR-CNN model in terms of their accuracy and inference time, achieving up to a 98.8% mAP50 on fruit predictions and 66.2% on branches in a leafy canopy. For fruit sizing, the accuracy of YOLOv8m-seg was like that achieved using Mask R-CNN, but the inference time was much shorter, again an enabler for the field adoption of this technology. A branch avoidance algorithm was proposed, where the implementation of this algorithm in real-time on an edge computing device was enabled by the short inference time of a YOLOv8-seg model for branches and fruit. This capability contributes to the development of automated fruit harvesting.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s24175593
- https://www.mdpi.com/1424-8220/24/17/5593/pdf?version=1724915439
- OA Status
- gold
- Cited By
- 15
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402002364
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4402002364Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s24175593Digital Object Identifier
- Title
-
Developing Machine Vision in Tree-Fruit Applications—Fruit Count, Fruit Size and Branch Avoidance in Automated HarvestingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-08-29Full publication date if available
- Authors
-
Chiranjivi Neupane, Kerry B. Walsh, Rafael Goulart, Anand KoiralaList of authors in order
- Landing page
-
https://doi.org/10.3390/s24175593Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/24/17/5593/pdf?version=1724915439Direct 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/1424-8220/24/17/5593/pdf?version=1724915439Direct OA link when available
- Concepts
-
Benchmark (surveying), Computer science, Orchard, Artificial intelligence, Convolutional neural network, Segmentation, Machine learning, Tree (set theory), Inference, Mathematics, Horticulture, Biology, Mathematical analysis, Geodesy, GeographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
15Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 14, 2024: 1Per-year citation counts (last 5 years)
- References (count)
-
31Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4402002364 |
|---|---|
| doi | https://doi.org/10.3390/s24175593 |
| ids.doi | https://doi.org/10.3390/s24175593 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/39275504 |
| ids.openalex | https://openalex.org/W4402002364 |
| fwci | 11.73073615 |
| mesh[0].qualifier_ui | |
| mesh[0].descriptor_ui | D005638 |
| mesh[0].is_major_topic | True |
| mesh[0].qualifier_name | |
| mesh[0].descriptor_name | Fruit |
| mesh[1].qualifier_ui | |
| mesh[1].descriptor_ui | D031022 |
| mesh[1].is_major_topic | True |
| mesh[1].qualifier_name | |
| mesh[1].descriptor_name | Mangifera |
| 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 | Q000254 |
| mesh[3].descriptor_ui | D014197 |
| mesh[3].is_major_topic | False |
| mesh[3].qualifier_name | growth & development |
| mesh[3].descriptor_name | Trees |
| mesh[4].qualifier_ui | |
| mesh[4].descriptor_ui | D000465 |
| mesh[4].is_major_topic | False |
| mesh[4].qualifier_name | |
| mesh[4].descriptor_name | Algorithms |
| mesh[5].qualifier_ui | Q000379 |
| mesh[5].descriptor_ui | D007091 |
| mesh[5].is_major_topic | False |
| mesh[5].qualifier_name | methods |
| mesh[5].descriptor_name | Image Processing, Computer-Assisted |
| mesh[6].qualifier_ui | |
| mesh[6].descriptor_ui | D005638 |
| mesh[6].is_major_topic | True |
| mesh[6].qualifier_name | |
| mesh[6].descriptor_name | Fruit |
| mesh[7].qualifier_ui | |
| mesh[7].descriptor_ui | D031022 |
| mesh[7].is_major_topic | True |
| mesh[7].qualifier_name | |
| mesh[7].descriptor_name | Mangifera |
| mesh[8].qualifier_ui | |
| mesh[8].descriptor_ui | D016571 |
| mesh[8].is_major_topic | True |
| mesh[8].qualifier_name | |
| mesh[8].descriptor_name | Neural Networks, Computer |
| mesh[9].qualifier_ui | Q000254 |
| mesh[9].descriptor_ui | D014197 |
| mesh[9].is_major_topic | False |
| mesh[9].qualifier_name | growth & development |
| mesh[9].descriptor_name | Trees |
| mesh[10].qualifier_ui | |
| mesh[10].descriptor_ui | D000465 |
| mesh[10].is_major_topic | False |
| mesh[10].qualifier_name | |
| mesh[10].descriptor_name | Algorithms |
| mesh[11].qualifier_ui | Q000379 |
| mesh[11].descriptor_ui | D007091 |
| mesh[11].is_major_topic | False |
| mesh[11].qualifier_name | methods |
| mesh[11].descriptor_name | Image Processing, Computer-Assisted |
| mesh[12].qualifier_ui | |
| mesh[12].descriptor_ui | D005638 |
| mesh[12].is_major_topic | True |
| mesh[12].qualifier_name | |
| mesh[12].descriptor_name | Fruit |
| mesh[13].qualifier_ui | |
| mesh[13].descriptor_ui | D031022 |
| mesh[13].is_major_topic | True |
| mesh[13].qualifier_name | |
| mesh[13].descriptor_name | Mangifera |
| mesh[14].qualifier_ui | |
| mesh[14].descriptor_ui | D016571 |
| mesh[14].is_major_topic | True |
| mesh[14].qualifier_name | |
| mesh[14].descriptor_name | Neural Networks, Computer |
| mesh[15].qualifier_ui | Q000254 |
| mesh[15].descriptor_ui | D014197 |
| mesh[15].is_major_topic | False |
| mesh[15].qualifier_name | growth & development |
| mesh[15].descriptor_name | Trees |
| mesh[16].qualifier_ui | |
| mesh[16].descriptor_ui | D000465 |
| mesh[16].is_major_topic | False |
| mesh[16].qualifier_name | |
| mesh[16].descriptor_name | Algorithms |
| mesh[17].qualifier_ui | Q000379 |
| mesh[17].descriptor_ui | D007091 |
| mesh[17].is_major_topic | False |
| mesh[17].qualifier_name | methods |
| mesh[17].descriptor_name | Image Processing, Computer-Assisted |
| type | article |
| title | Developing Machine Vision in Tree-Fruit Applications—Fruit Count, Fruit Size and Branch Avoidance in Automated Harvesting |
| biblio.issue | 17 |
| biblio.volume | 24 |
| biblio.last_page | 5593 |
| biblio.first_page | 5593 |
| 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/T12729 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9894000291824341 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2210 |
| topics[1].subfield.display_name | Mechanical Engineering |
| topics[1].display_name | Tree Root and Stability Studies |
| topics[2].id | https://openalex.org/T12894 |
| topics[2].field.id | https://openalex.org/fields/11 |
| topics[2].field.display_name | Agricultural and Biological Sciences |
| topics[2].score | 0.9879000186920166 |
| topics[2].domain.id | https://openalex.org/domains/1 |
| topics[2].domain.display_name | Life Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1110 |
| topics[2].subfield.display_name | Plant Science |
| topics[2].display_name | Date Palm Research 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/C185798385 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7217556238174438 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1161707 |
| concepts[0].display_name | Benchmark (surveying) |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.6793987154960632 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C2780753983 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6246535778045654 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q236371 |
| concepts[2].display_name | Orchard |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.5571703314781189 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C81363708 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5566761493682861 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[4].display_name | Convolutional neural network |
| concepts[5].id | https://openalex.org/C89600930 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5488279461860657 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1423946 |
| concepts[5].display_name | Segmentation |
| concepts[6].id | https://openalex.org/C119857082 |
| concepts[6].level | 1 |
| concepts[6].score | 0.5387510061264038 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[6].display_name | Machine learning |
| concepts[7].id | https://openalex.org/C113174947 |
| concepts[7].level | 2 |
| concepts[7].score | 0.5186589956283569 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q2859736 |
| concepts[7].display_name | Tree (set theory) |
| concepts[8].id | https://openalex.org/C2776214188 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4457620680332184 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q408386 |
| concepts[8].display_name | Inference |
| concepts[9].id | https://openalex.org/C33923547 |
| concepts[9].level | 0 |
| concepts[9].score | 0.18072852492332458 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[9].display_name | Mathematics |
| concepts[10].id | https://openalex.org/C144027150 |
| concepts[10].level | 1 |
| concepts[10].score | 0.11812612414360046 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q48803 |
| concepts[10].display_name | Horticulture |
| concepts[11].id | https://openalex.org/C86803240 |
| concepts[11].level | 0 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[11].display_name | Biology |
| concepts[12].id | https://openalex.org/C134306372 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q7754 |
| concepts[12].display_name | Mathematical analysis |
| concepts[13].id | https://openalex.org/C13280743 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q131089 |
| concepts[13].display_name | Geodesy |
| concepts[14].id | https://openalex.org/C205649164 |
| concepts[14].level | 0 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[14].display_name | Geography |
| keywords[0].id | https://openalex.org/keywords/benchmark |
| keywords[0].score | 0.7217556238174438 |
| keywords[0].display_name | Benchmark (surveying) |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.6793987154960632 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/orchard |
| keywords[2].score | 0.6246535778045654 |
| keywords[2].display_name | Orchard |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.5571703314781189 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[4].score | 0.5566761493682861 |
| keywords[4].display_name | Convolutional neural network |
| keywords[5].id | https://openalex.org/keywords/segmentation |
| keywords[5].score | 0.5488279461860657 |
| keywords[5].display_name | Segmentation |
| keywords[6].id | https://openalex.org/keywords/machine-learning |
| keywords[6].score | 0.5387510061264038 |
| keywords[6].display_name | Machine learning |
| keywords[7].id | https://openalex.org/keywords/tree |
| keywords[7].score | 0.5186589956283569 |
| keywords[7].display_name | Tree (set theory) |
| keywords[8].id | https://openalex.org/keywords/inference |
| keywords[8].score | 0.4457620680332184 |
| keywords[8].display_name | Inference |
| keywords[9].id | https://openalex.org/keywords/mathematics |
| keywords[9].score | 0.18072852492332458 |
| keywords[9].display_name | Mathematics |
| keywords[10].id | https://openalex.org/keywords/horticulture |
| keywords[10].score | 0.11812612414360046 |
| keywords[10].display_name | Horticulture |
| language | en |
| locations[0].id | doi:10.3390/s24175593 |
| 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/1424-8220/24/17/5593/pdf?version=1724915439 |
| 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/s24175593 |
| locations[1].id | pmid:39275504 |
| 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/39275504 |
| locations[2].id | pmh:oai:doaj.org/article:713a3ee562c1432192d445294b9c08ff |
| locations[2].is_oa | False |
| 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 | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | article |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Sensors, Vol 24, Iss 17, p 5593 (2024) |
| locations[2].landing_page_url | https://doaj.org/article/713a3ee562c1432192d445294b9c08ff |
| locations[3].id | pmh:oai:mdpi.com:/1424-8220/24/17/5593/ |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S4306400947 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | True |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | MDPI (MDPI AG) |
| locations[3].source.host_organization | https://openalex.org/I4210097602 |
| locations[3].source.host_organization_name | Multidisciplinary Digital Publishing Institute (Switzerland) |
| locations[3].source.host_organization_lineage | https://openalex.org/I4210097602 |
| locations[3].license | cc-by |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | Text |
| 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 |
| locations[3].landing_page_url | https://dx.doi.org/10.3390/s24175593 |
| locations[4].id | pmh:oai:pubmedcentral.nih.gov:11397816 |
| locations[4].is_oa | True |
| locations[4].source.id | https://openalex.org/S2764455111 |
| 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 | PubMed Central |
| locations[4].source.host_organization | https://openalex.org/I1299303238 |
| locations[4].source.host_organization_name | National Institutes of Health |
| locations[4].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[4].license | other-oa |
| locations[4].pdf_url | |
| locations[4].version | submittedVersion |
| locations[4].raw_type | Text |
| locations[4].license_id | https://openalex.org/licenses/other-oa |
| locations[4].is_accepted | False |
| locations[4].is_published | False |
| locations[4].raw_source_name | Sensors (Basel) |
| locations[4].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/11397816 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5047980761 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-8383-1456 |
| authorships[0].author.display_name | Chiranjivi Neupane |
| authorships[0].countries | AU |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I74899385 |
| authorships[0].affiliations[0].raw_affiliation_string | Institute for Future Farming Systems, Central Queensland University, Rockhampton 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 | Chiranjivi Neupane |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Institute for Future Farming Systems, Central Queensland University, Rockhampton 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 for Future Farming Systems, Central Queensland University, Rockhampton 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 B. Walsh |
| authorships[1].is_corresponding | True |
| authorships[1].raw_affiliation_strings | Institute for Future Farming Systems, Central Queensland University, Rockhampton 4701, Australia |
| authorships[2].author.id | https://openalex.org/A5087777830 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-0478-3682 |
| authorships[2].author.display_name | Rafael Goulart |
| authorships[2].countries | AU |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I74899385 |
| authorships[2].affiliations[0].raw_affiliation_string | Institute for Future Farming Systems, Central Queensland University, Rockhampton 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 | middle |
| authorships[2].raw_author_name | Rafael Goulart |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Institute for Future Farming Systems, Central Queensland University, Rockhampton 4701, Australia |
| authorships[3].author.id | https://openalex.org/A5054056327 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-7376-2143 |
| authorships[3].author.display_name | Anand Koirala |
| authorships[3].countries | AU |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I74899385 |
| authorships[3].affiliations[0].raw_affiliation_string | Institute for Future Farming Systems, Central Queensland University, Rockhampton 4701, Australia |
| authorships[3].institutions[0].id | https://openalex.org/I74899385 |
| authorships[3].institutions[0].ror | https://ror.org/023q4bk22 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I74899385 |
| authorships[3].institutions[0].country_code | AU |
| authorships[3].institutions[0].display_name | Central Queensland University |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Anand Koirala |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Institute for Future Farming Systems, Central Queensland University, Rockhampton 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/1424-8220/24/17/5593/pdf?version=1724915439 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Developing Machine Vision in Tree-Fruit Applications—Fruit Count, Fruit Size and Branch Avoidance in Automated Harvesting |
| 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/W2357290554, https://openalex.org/W2334468155, https://openalex.org/W2378211422, https://openalex.org/W2590060643, https://openalex.org/W2745001401, https://openalex.org/W4321353415, https://openalex.org/W2775631375, https://openalex.org/W2130974462, https://openalex.org/W2028665553, https://openalex.org/W2212575126 |
| cited_by_count | 15 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 14 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 1 |
| locations_count | 5 |
| best_oa_location.id | doi:10.3390/s24175593 |
| 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/1424-8220/24/17/5593/pdf?version=1724915439 |
| 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/s24175593 |
| primary_location.id | doi:10.3390/s24175593 |
| 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/1424-8220/24/17/5593/pdf?version=1724915439 |
| 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/s24175593 |
| publication_date | 2024-08-29 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W4312078698, https://openalex.org/W3197808059, https://openalex.org/W4382059335, https://openalex.org/W4391688524, https://openalex.org/W2963037989, https://openalex.org/W4399893080, https://openalex.org/W4388823657, https://openalex.org/W3196367837, https://openalex.org/W2920621226, https://openalex.org/W4206638364, https://openalex.org/W2768547285, https://openalex.org/W4366284279, https://openalex.org/W4303579207, https://openalex.org/W4200107860, https://openalex.org/W4386275641, https://openalex.org/W3125812266, https://openalex.org/W2963073614, https://openalex.org/W1959344004, https://openalex.org/W2891049923, https://openalex.org/W3008904350, https://openalex.org/W2999278897, https://openalex.org/W2765312524, https://openalex.org/W3203976206, https://openalex.org/W3030569802, https://openalex.org/W4312419644, https://openalex.org/W4200598972, https://openalex.org/W4224923734, https://openalex.org/W4295215021, https://openalex.org/W4389560796, https://openalex.org/W2981630388, https://openalex.org/W3101577715 |
| referenced_works_count | 31 |
| abstract_inverted_index.1 | 152 |
| abstract_inverted_index.A | 163, 268 |
| abstract_inverted_index.a | 29, 80, 167, 199, 223, 234, 295 |
| abstract_inverted_index.2% | 154 |
| abstract_inverted_index.2D | 11 |
| abstract_inverted_index.An | 66 |
| abstract_inverted_index.an | 194, 259, 283 |
| abstract_inverted_index.as | 198 |
| abstract_inverted_index.by | 132, 289 |
| abstract_inverted_index.in | 2, 16, 28, 89, 108, 117, 212, 233, 280 |
| abstract_inverted_index.is | 57, 70 |
| abstract_inverted_index.of | 10, 25, 31, 46, 63, 73, 76, 79, 86, 126, 134, 138, 169, 181, 185, 190, 214, 242, 265, 277, 294, 308 |
| abstract_inverted_index.on | 188, 226, 231, 282 |
| abstract_inverted_index.to | 59, 222, 305 |
| abstract_inverted_index.up | 221 |
| abstract_inverted_index.v9 | 205 |
| abstract_inverted_index.× | 141 |
| abstract_inverted_index.(i) | 98 |
| abstract_inverted_index.All | 111 |
| abstract_inverted_index.For | 237 |
| abstract_inverted_index.SD, | 130 |
| abstract_inverted_index.The | 41, 123 |
| abstract_inverted_index.and | 7, 19, 48, 104, 113, 151, 153, 158, 204, 217, 229, 300 |
| abstract_inverted_index.any | 52 |
| abstract_inverted_index.are | 120 |
| abstract_inverted_index.but | 251 |
| abstract_inverted_index.for | 43, 146, 155, 178, 261, 298 |
| abstract_inverted_index.mAP | 124 |
| abstract_inverted_index.the | 8, 23, 44, 61, 71, 74, 77, 84, 97, 147, 156, 172, 175, 179, 182, 208, 240, 252, 262, 275, 290, 306 |
| abstract_inverted_index.use | 9, 201 |
| abstract_inverted_index.was | 143, 244, 255, 272, 287 |
| abstract_inverted_index.(ii) | 101 |
| abstract_inverted_index.0.2% | 145 |
| abstract_inverted_index.100) | 142 |
| abstract_inverted_index.Mask | 249 |
| abstract_inverted_index.This | 302 |
| abstract_inverted_index.edge | 195, 284 |
| abstract_inverted_index.have | 21 |
| abstract_inverted_index.like | 245 |
| abstract_inverted_index.mean | 133 |
| abstract_inverted_index.much | 256 |
| abstract_inverted_index.need | 42, 69 |
| abstract_inverted_index.size | 103 |
| abstract_inverted_index.test | 49, 114 |
| abstract_inverted_index.that | 246 |
| abstract_inverted_index.this | 118, 266, 278 |
| abstract_inverted_index.time | 254, 293 |
| abstract_inverted_index.tree | 191 |
| abstract_inverted_index.used | 116 |
| abstract_inverted_index.were | 95 |
| abstract_inverted_index.with | 33, 51 |
| abstract_inverted_index.work | 53, 119 |
| abstract_inverted_index.(CNN) | 15 |
| abstract_inverted_index.(iii) | 105 |
| abstract_inverted_index.66.2% | 230 |
| abstract_inverted_index.98.8% | 224 |
| abstract_inverted_index.Three | 91 |
| abstract_inverted_index.again | 258 |
| abstract_inverted_index.case. | 202 |
| abstract_inverted_index.depth | 4 |
| abstract_inverted_index.field | 263 |
| abstract_inverted_index.fruit | 99, 102, 148, 157, 187, 227, 238, 310 |
| abstract_inverted_index.given | 81 |
| abstract_inverted_index.leafy | 235 |
| abstract_inverted_index.mAP50 | 168, 225 |
| abstract_inverted_index.mango | 92, 186 |
| abstract_inverted_index.model | 55, 150, 165, 211, 297 |
| abstract_inverted_index.often | 36 |
| abstract_inverted_index.range | 30 |
| abstract_inverted_index.short | 291 |
| abstract_inverted_index.terms | 213 |
| abstract_inverted_index.their | 215 |
| abstract_inverted_index.time, | 219 |
| abstract_inverted_index.using | 136, 193, 248 |
| abstract_inverted_index.where | 274 |
| abstract_inverted_index.work. | 65 |
| abstract_inverted_index.99.3%, | 170 |
| abstract_inverted_index.Neural | 13 |
| abstract_inverted_index.R-CNN, | 250 |
| abstract_inverted_index.Recent | 0 |
| abstract_inverted_index.YOLOv8 | 203 |
| abstract_inverted_index.branch | 106, 159, 269 |
| abstract_inverted_index.count, | 100 |
| abstract_inverted_index.device | 197, 286 |
| abstract_inverted_index.enable | 60 |
| abstract_inverted_index.fruit. | 301 |
| abstract_inverted_index.images | 189 |
| abstract_inverted_index.impact | 85 |
| abstract_inverted_index.model, | 82 |
| abstract_inverted_index.models | 35, 137, 206 |
| abstract_inverted_index.object | 17 |
| abstract_inverted_index.viable | 200 |
| abstract_inverted_index.vision | 27 |
| abstract_inverted_index.YOLOv8m | 164 |
| abstract_inverted_index.canopy. | 236 |
| abstract_inverted_index.divided | 131 |
| abstract_inverted_index.enabled | 288 |
| abstract_inverted_index.enabler | 260 |
| abstract_inverted_index.imaging | 5 |
| abstract_inverted_index.machine | 26 |
| abstract_inverted_index.models, | 161 |
| abstract_inverted_index.orchard | 93 |
| abstract_inverted_index.release | 45 |
| abstract_inverted_index.sizing, | 239 |
| abstract_inverted_index.Networks | 14 |
| abstract_inverted_index.accuracy | 216, 241 |
| abstract_inverted_index.achieved | 166, 247 |
| abstract_inverted_index.adoption | 24, 264 |
| abstract_inverted_index.branches | 232, 299 |
| abstract_inverted_index.canopies | 192 |
| abstract_inverted_index.datasets | 50, 115 |
| abstract_inverted_index.hardware | 6 |
| abstract_inverted_index.previous | 38, 173 |
| abstract_inverted_index.repeated | 139 |
| abstract_inverted_index.shorter, | 257 |
| abstract_inverted_index.training | 47, 112 |
| abstract_inverted_index.(Standard | 128 |
| abstract_inverted_index.MaskR-CNN | 210 |
| abstract_inverted_index.achieving | 220 |
| abstract_inverted_index.algorithm | 271, 279 |
| abstract_inverted_index.automated | 109, 309 |
| abstract_inverted_index.available | 121 |
| abstract_inverted_index.avoidance | 107, 270 |
| abstract_inverted_index.benchmark | 209 |
| abstract_inverted_index.computing | 196, 285 |
| abstract_inverted_index.detection | 18, 149, 184 |
| abstract_inverted_index.inference | 218, 253, 292 |
| abstract_inverted_index.processes | 88 |
| abstract_inverted_index.proposed, | 273 |
| abstract_inverted_index.publicly. | 122 |
| abstract_inverted_index.published | 64 |
| abstract_inverted_index.real-time | 183, 281 |
| abstract_inverted_index.reporting | 54, 68 |
| abstract_inverted_index.training. | 90 |
| abstract_inverted_index.trainings | 140 |
| abstract_inverted_index.Deviation, | 129 |
| abstract_inverted_index.YOLOv8-seg | 296 |
| abstract_inverted_index.additional | 67 |
| abstract_inverted_index.affordable | 3 |
| abstract_inverted_index.benchmark, | 174 |
| abstract_inverted_index.capability | 303 |
| abstract_inverted_index.emphasized | 58 |
| abstract_inverted_index.mainstream | 34 |
| abstract_inverted_index.stochastic | 87 |
| abstract_inverted_index.YOLOv8m-seg | 243 |
| abstract_inverted_index.accelerated | 22 |
| abstract_inverted_index.application | 180 |
| abstract_inverted_index.considered: | 96 |
| abstract_inverted_index.contributes | 304 |
| abstract_inverted_index.development | 56, 307 |
| abstract_inverted_index.harvesting. | 110, 311 |
| abstract_inverted_index.performance | 75 |
| abstract_inverted_index.predictions | 135, 228 |
| abstract_inverted_index.quantifying | 83 |
| abstract_inverted_index.re-training | 78 |
| abstract_inverted_index.technology. | 267 |
| abstract_inverted_index.applications | 94 |
| abstract_inverted_index.developments | 1 |
| abstract_inverted_index.outperformed | 207 |
| abstract_inverted_index.segmentation | 20, 160 |
| abstract_inverted_index.variation’ | 127 |
| abstract_inverted_index.Convolutional | 12 |
| abstract_inverted_index.applications, | 32 |
| abstract_inverted_index.approximately | 144 |
| abstract_inverted_index.documentation | 72 |
| abstract_inverted_index.outperforming | 171 |
| abstract_inverted_index.re-evaluation | 62 |
| abstract_inverted_index.respectively. | 162 |
| abstract_inverted_index.architectures. | 40 |
| abstract_inverted_index.implementation | 276 |
| abstract_inverted_index.out-performing | 37 |
| abstract_inverted_index.‘coefficient | 125 |
| abstract_inverted_index.purpose-designed | 176 |
| abstract_inverted_index.‘MangoYOLO’, | 177 |
| abstract_inverted_index.application-specific | 39 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5081837621 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I74899385 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.4300000071525574 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.98592891 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |