In-Orchard Sizing of Mango Fruit: 1. Comparison of Machine Vision Based Methods for On-The-Go Estimation Article Swipe
Chiranjivi Neupane
,
Anand Koirala
,
Kerry B. Walsh
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/horticulturae8121223
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/horticulturae8121223
Estimation of fruit size on-tree is useful for yield estimation, harvest timing and market planning. Automation of measurement of fruit size on-tree is possible using RGB-depth (RGB-D) cameras, if partly occluded fruit can be removed from consideration. An RGB-D Time of Flight camera was used in an imaging system that can be driven through an orchard. Three approaches were compared, being: (i) refined bounding box dimensions of a YOLO object detector; (ii) bounding box dimensions of an instance segmentation model (Mask R-CNN) applied to canopy images, and (iii) instance segmentation applied to extracted bounding boxes from a YOLO detection model. YOLO versions 3, 4 and 7 and their tiny variants were compared to an in-house variant, MangoYOLO, for this application, with YOLO v4-tiny adopted. Criteria developed to exclude occluded fruit by filtering based on depth, mask size, ellipse to mask area ratio and difference between refined bounding box height and ellipse major axis. The lowest root mean square error (RMSE) of 4.7 mm and 5.1 mm on the lineal length dimensions of a population (n = 104) of Honey Gold and Keitt varieties of mango fruit, respectively, and the lowest fruit exclusion rate was achieved using method (ii), while the RMSE on estimated fruit weight was 113 g on a population weight range between 180 and 1130 g. An example use is provided, with the method applied to video of an orchard row to produce a weight frequency distribution related to packing tray size.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/horticulturae8121223
- https://www.mdpi.com/2311-7524/8/12/1223/pdf?version=1671699518
- OA Status
- gold
- Cited By
- 27
- References
- 29
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4312078698
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4312078698Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/horticulturae8121223Digital Object Identifier
- Title
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In-Orchard Sizing of Mango Fruit: 1. Comparison of Machine Vision Based Methods for On-The-Go EstimationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-12-19Full publication date if available
- Authors
-
Chiranjivi Neupane, Anand Koirala, Kerry B. WalshList of authors in order
- Landing page
-
https://doi.org/10.3390/horticulturae8121223Publisher landing page
- PDF URL
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https://www.mdpi.com/2311-7524/8/12/1223/pdf?version=1671699518Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.mdpi.com/2311-7524/8/12/1223/pdf?version=1671699518Direct OA link when available
- Concepts
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Orchard, Mathematics, Minimum bounding box, RGB color model, Mean squared error, Population, Ellipse, Segmentation, Artificial intelligence, Computer vision, Statistics, Horticulture, Computer science, Image (mathematics), Geometry, Demography, Biology, SociologyTop concepts (fields/topics) attached by OpenAlex
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27Total citation count in OpenAlex
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2025: 6, 2024: 13, 2023: 8Per-year citation counts (last 5 years)
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29Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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