Detection and counting of root-knot nematodes using YOLO models with mosaic augmentation Article Swipe
Top Bahadur Pun
,
Arjun Neupane
,
Richard Koech
,
Kerry B. Walsh
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.biosx.2023.100407
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.biosx.2023.100407
Root-knot nematodes (RKN) are microscopic plant parasites that cause significant economic damage to crops and vegetables. Accurate assessment of RKN populations is required for effective management of this disease. Several “You Only Look Once” (YOLO versions 2 to 7) architectures were investigated for the application of RKN enumeration in microscopic images. YOLOv5-608 model attained Precision score of 0.960, Recall of 0.951, F1-score of 0.990, mAP of 0.972 without mosaic augmentation. Using mosaic dataset, this was increased to Precision of 1.00, Recall of 0.998, F1-score of 0.999, and mAP of 0.995. YOLOv5-608 model showed the highest correlation between the manual and machine counting of RKN: coefficient of determination (R2) of 0.991, root mean square error (RMSE) of 0.313, and coefficient of variation (CV) of 0.251. For free-living nematodes (FLN), this resulted in R2 of 0.994, RMSE of 0.058, and CV of 1.760. YOLOv7-608 achieved the highest correlation between manual and machine counting of overlapped RKN (R2 of 0.970, RMSE of 0.595, and CV of 0.123). In addition, this study explored a new application of mosaic augmentation to analyse microscopic images acquired with different objective lense magnifications. The proposed framework supports the rapid assessment of plant parasitic nematodes necessary to implement nematode control strategies and improve crop management practices.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.biosx.2023.100407
- OA Status
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All OpenAlex metadata
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https://openalex.org/W4387007015Canonical identifier for this work in OpenAlex
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https://doi.org/10.1016/j.biosx.2023.100407Digital Object Identifier
- Title
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Detection and counting of root-knot nematodes using YOLO models with mosaic augmentationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
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2023Year of publication
- Publication date
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2023-09-25Full publication date if available
- Authors
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Top Bahadur Pun, Arjun Neupane, Richard Koech, Kerry B. WalshList of authors in order
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https://doi.org/10.1016/j.biosx.2023.100407Publisher landing page
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goldOpen access status per OpenAlex
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https://doi.org/10.1016/j.biosx.2023.100407Direct OA link when available
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Knot (papermaking), Correlation coefficient, Mathematics, Pearson product-moment correlation coefficient, Statistics, Mean squared error, Coefficient of determination, Enumeration, Horticulture, Biology, Combinatorics, Materials science, Composite materialTop concepts (fields/topics) attached by OpenAlex
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20Total citation count in OpenAlex
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2025: 9, 2024: 7, 2023: 4Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2023595878, https://openalex.org/W4200349706, https://openalex.org/W2807169760, https://openalex.org/W4385491061, https://openalex.org/W6777046832, https://openalex.org/W2065999373, https://openalex.org/W2102148524, https://openalex.org/W6803398002, https://openalex.org/W6777941498, https://openalex.org/W2999309192, https://openalex.org/W2981380781, https://openalex.org/W3188082098, https://openalex.org/W4313531454, https://openalex.org/W3184977298, https://openalex.org/W6725739302, https://openalex.org/W6603984027, https://openalex.org/W2981211097, https://openalex.org/W6687171081, https://openalex.org/W2074070766, https://openalex.org/W2906506339, https://openalex.org/W6803088426, https://openalex.org/W2809598685, https://openalex.org/W4280526724, https://openalex.org/W2952757700, https://openalex.org/W2019655958, https://openalex.org/W3216843742, https://openalex.org/W4224290289, https://openalex.org/W6628973269, https://openalex.org/W3088751563, https://openalex.org/W2954996726, https://openalex.org/W3160230758, https://openalex.org/W3036042197, https://openalex.org/W6849520326, https://openalex.org/W4306769399, https://openalex.org/W6750297666, https://openalex.org/W3198069414, https://openalex.org/W2604213426, https://openalex.org/W2188292956, https://openalex.org/W97504207, https://openalex.org/W2749638221, https://openalex.org/W4210327135, https://openalex.org/W2773726006, https://openalex.org/W4234903585, https://openalex.org/W3207258980, https://openalex.org/W2963037989, https://openalex.org/W4386076325, https://openalex.org/W3018757597, https://openalex.org/W4236965008, https://openalex.org/W4205570975 |
| referenced_works_count | 49 |
| abstract_inverted_index.2 | 36 |
| abstract_inverted_index.a | 169 |
| abstract_inverted_index.7) | 38 |
| abstract_inverted_index.CV | 138, 161 |
| abstract_inverted_index.In | 164 |
| abstract_inverted_index.R2 | 131 |
| abstract_inverted_index.in | 48, 130 |
| abstract_inverted_index.is | 21 |
| abstract_inverted_index.of | 18, 26, 45, 56, 59, 62, 65, 78, 81, 84, 88, 102, 105, 108, 115, 119, 122, 132, 135, 139, 151, 155, 158, 162, 172, 192 |
| abstract_inverted_index.to | 12, 37, 76, 175, 197 |
| abstract_inverted_index.(R2 | 154 |
| abstract_inverted_index.For | 124 |
| abstract_inverted_index.RKN | 19, 46, 153 |
| abstract_inverted_index.The | 185 |
| abstract_inverted_index.and | 14, 86, 99, 117, 137, 148, 160, 202 |
| abstract_inverted_index.are | 3 |
| abstract_inverted_index.for | 23, 42 |
| abstract_inverted_index.mAP | 64, 87 |
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| abstract_inverted_index.the | 43, 93, 97, 143, 189 |
| abstract_inverted_index.was | 74 |
| abstract_inverted_index.(CV) | 121 |
| abstract_inverted_index.(R2) | 107 |
| abstract_inverted_index.Look | 32 |
| abstract_inverted_index.Only | 31 |
| abstract_inverted_index.RKN: | 103 |
| abstract_inverted_index.RMSE | 134, 157 |
| abstract_inverted_index.crop | 204 |
| abstract_inverted_index.mean | 111 |
| abstract_inverted_index.root | 110 |
| abstract_inverted_index.that | 7 |
| abstract_inverted_index.this | 27, 73, 128, 166 |
| abstract_inverted_index.were | 40 |
| abstract_inverted_index.with | 180 |
| abstract_inverted_index.(RKN) | 2 |
| abstract_inverted_index.(YOLO | 34 |
| abstract_inverted_index.0.972 | 66 |
| abstract_inverted_index.1.00, | 79 |
| abstract_inverted_index.Using | 70 |
| abstract_inverted_index.cause | 8 |
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| abstract_inverted_index.error | 113 |
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| abstract_inverted_index.model | 52, 91 |
| abstract_inverted_index.plant | 5, 193 |
| abstract_inverted_index.rapid | 190 |
| abstract_inverted_index.score | 55 |
| abstract_inverted_index.study | 167 |
| abstract_inverted_index.(FLN), | 127 |
| abstract_inverted_index.(RMSE) | 114 |
| abstract_inverted_index.0.058, | 136 |
| abstract_inverted_index.0.251. | 123 |
| abstract_inverted_index.0.313, | 116 |
| abstract_inverted_index.0.595, | 159 |
| abstract_inverted_index.0.951, | 60 |
| abstract_inverted_index.0.960, | 57 |
| abstract_inverted_index.0.970, | 156 |
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| abstract_inverted_index.0.994, | 133 |
| abstract_inverted_index.0.995. | 89 |
| abstract_inverted_index.0.998, | 82 |
| abstract_inverted_index.0.999, | 85 |
| abstract_inverted_index.1.760. | 140 |
| abstract_inverted_index.Recall | 58, 80 |
| abstract_inverted_index.damage | 11 |
| abstract_inverted_index.images | 178 |
| abstract_inverted_index.manual | 98, 147 |
| abstract_inverted_index.mosaic | 68, 71, 173 |
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| abstract_inverted_index.square | 112 |
| abstract_inverted_index.“You | 30 |
| abstract_inverted_index.0.123). | 163 |
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| abstract_inverted_index.Several | 29 |
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| abstract_inverted_index.control | 200 |
| abstract_inverted_index.highest | 94, 144 |
| abstract_inverted_index.images. | 50 |
| abstract_inverted_index.improve | 203 |
| abstract_inverted_index.machine | 100, 149 |
| abstract_inverted_index.without | 67 |
| abstract_inverted_index.Accurate | 16 |
| abstract_inverted_index.F1-score | 61, 83 |
| abstract_inverted_index.achieved | 142 |
| abstract_inverted_index.acquired | 179 |
| abstract_inverted_index.attained | 53 |
| abstract_inverted_index.counting | 101, 150 |
| abstract_inverted_index.dataset, | 72 |
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| abstract_inverted_index.Root-knot | 0 |
| abstract_inverted_index.addition, | 165 |
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| abstract_inverted_index.effective | 24 |
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| abstract_inverted_index.necessary | 196 |
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| abstract_inverted_index.objective | 182 |
| abstract_inverted_index.parasites | 6 |
| abstract_inverted_index.parasitic | 194 |
| abstract_inverted_index.variation | 120 |
| abstract_inverted_index.YOLOv5-608 | 51, 90 |
| abstract_inverted_index.YOLOv7-608 | 141 |
| abstract_inverted_index.assessment | 17, 191 |
| abstract_inverted_index.management | 25, 205 |
| abstract_inverted_index.overlapped | 152 |
| abstract_inverted_index.practices. | 206 |
| abstract_inverted_index.strategies | 201 |
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| abstract_inverted_index.populations | 20 |
| abstract_inverted_index.significant | 9 |
| abstract_inverted_index.vegetables. | 15 |
| abstract_inverted_index.augmentation | 174 |
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| abstract_inverted_index.architectures | 39 |
| abstract_inverted_index.augmentation. | 69 |
| abstract_inverted_index.determination | 106 |
| abstract_inverted_index.magnifications. | 184 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5036202905 |
| 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/2 |
| sustainable_development_goals[0].score | 0.6399999856948853 |
| sustainable_development_goals[0].display_name | Zero hunger |
| citation_normalized_percentile.value | 0.97586616 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |