Application of deep learning for real-time detection, localization, and counting of the malignant invasive weed Solanum rostratum Dunal Article Swipe
Sheng Du
,
Yanfang Yang
,
Hongbo Yuan
,
Man Cheng
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fpls.2024.1486929
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fpls.2024.1486929
Solanum rostratum Dunal (SrD) is a globally harmful invasive weed that has spread widely across many countries, posing a serious threat to agriculture and ecosystem security. A deep learning network model, TrackSolanum, was designed for real-time detection, location, and counting of SrD in the field. The TrackSolanmu network model comprises four modules: detection, tracking, localization, and counting. The detection module uses YOLO_EAND for SrD identification, the tracking module applies DeepSort for multi-target tracking of SrD in consecutive video frames, the localization module determines the position of the SrD through center-of-mass localization, and the counting module counts the plants using a target ID over-the-line invalidation method. The field test results show that for UAV video at a height of 2m, TrackSolanum achieved precision and recall of 0.950 and 0.970, with MOTA and IDF1 scores of 0.826 and 0.960, a counting error rate of 2.438%, and FPS of 17. For UAV video at a height of 3m, the model reached precision and recall of 0.846 and 0.934, MOTA and IDF1 scores of 0.708 and 0.888, a counting error rate of 4.634%, and FPS of 79. Thus, the TrackSolanum supports real-time SrD detection, offering crucial technical support for hazard assessment and precise management of SrD.
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Metadata
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- Language
- en
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All OpenAlex metadata
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https://openalex.org/W4406935698Canonical identifier for this work in OpenAlex
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https://doi.org/10.3389/fpls.2024.1486929Digital Object Identifier
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Application of deep learning for real-time detection, localization, and counting of the malignant invasive weed Solanum rostratum DunalWork title
- Type
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-01-29Full publication date if available
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Sheng Du, Yanfang Yang, Hongbo Yuan, Man ChengList of authors in order
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https://doi.org/10.3389/fpls.2024.1486929Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.3389/fpls.2024.1486929Direct OA link when available
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Computer science, Artificial intelligence, Weed, Deep learning, Computer vision, Pattern recognition (psychology), Biology, BotanyTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 4Per-year citation counts (last 5 years)
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| referenced_works | https://openalex.org/W4214934301, https://openalex.org/W3128629435, https://openalex.org/W3206704828, https://openalex.org/W2252355370, https://openalex.org/W4384028170, https://openalex.org/W4390146257, https://openalex.org/W4400052936, https://openalex.org/W4392637973, https://openalex.org/W2000932885, https://openalex.org/W2970422278, https://openalex.org/W3004599819, https://openalex.org/W4393965390, https://openalex.org/W4391300556, https://openalex.org/W3004192481, https://openalex.org/W2011970275, https://openalex.org/W2072451636, https://openalex.org/W1990942831, https://openalex.org/W4315474362, https://openalex.org/W6851864227, https://openalex.org/W6628973269, https://openalex.org/W4323050444, https://openalex.org/W4200250223, https://openalex.org/W3156941978, https://openalex.org/W4313449150, https://openalex.org/W4403770406, https://openalex.org/W3186395191, https://openalex.org/W4284968638, https://openalex.org/W4402430538, https://openalex.org/W2113087661, https://openalex.org/W2898275094, https://openalex.org/W4385301744, https://openalex.org/W4399534198, https://openalex.org/W4308974725, https://openalex.org/W4391667607, https://openalex.org/W4402202062, https://openalex.org/W4367323321, https://openalex.org/W4401806236, https://openalex.org/W4400445931, https://openalex.org/W2942564843, https://openalex.org/W3104218139, https://openalex.org/W4236965008, https://openalex.org/W4288391450 |
| referenced_works_count | 42 |
| abstract_inverted_index.A | 26 |
| abstract_inverted_index.a | 5, 18, 99, 115, 137, 151, 173 |
| abstract_inverted_index.ID | 101 |
| abstract_inverted_index.at | 114, 150 |
| abstract_inverted_index.in | 42, 75 |
| abstract_inverted_index.is | 4 |
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| abstract_inverted_index.17. | 146 |
| abstract_inverted_index.2m, | 118 |
| abstract_inverted_index.3m, | 154 |
| abstract_inverted_index.79. | 182 |
| abstract_inverted_index.FPS | 144, 180 |
| abstract_inverted_index.For | 147 |
| abstract_inverted_index.SrD | 41, 63, 74, 87, 188 |
| abstract_inverted_index.The | 45, 57, 105 |
| abstract_inverted_index.UAV | 112, 148 |
| abstract_inverted_index.and | 23, 38, 55, 91, 122, 126, 130, 135, 143, 159, 163, 166, 171, 179, 197 |
| abstract_inverted_index.for | 34, 62, 70, 111, 194 |
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| abstract_inverted_index.the | 43, 65, 79, 83, 86, 92, 96, 155, 184 |
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| abstract_inverted_index.IDF1 | 131, 167 |
| abstract_inverted_index.MOTA | 129, 165 |
| abstract_inverted_index.SrD. | 201 |
| abstract_inverted_index.deep | 27 |
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| abstract_inverted_index.rate | 140, 176 |
| abstract_inverted_index.show | 109 |
| abstract_inverted_index.test | 107 |
| abstract_inverted_index.that | 10, 110 |
| abstract_inverted_index.uses | 60 |
| abstract_inverted_index.weed | 9 |
| abstract_inverted_index.with | 128 |
| abstract_inverted_index.(SrD) | 3 |
| abstract_inverted_index.0.708 | 170 |
| abstract_inverted_index.0.826 | 134 |
| abstract_inverted_index.0.846 | 162 |
| abstract_inverted_index.0.950 | 125 |
| abstract_inverted_index.Dunal | 2 |
| abstract_inverted_index.Thus, | 183 |
| abstract_inverted_index.error | 139, 175 |
| abstract_inverted_index.field | 106 |
| abstract_inverted_index.model | 48, 156 |
| abstract_inverted_index.using | 98 |
| abstract_inverted_index.video | 77, 113, 149 |
| abstract_inverted_index.0.888, | 172 |
| abstract_inverted_index.0.934, | 164 |
| abstract_inverted_index.0.960, | 136 |
| abstract_inverted_index.0.970, | 127 |
| abstract_inverted_index.across | 14 |
| abstract_inverted_index.counts | 95 |
| abstract_inverted_index.field. | 44 |
| abstract_inverted_index.hazard | 195 |
| abstract_inverted_index.height | 116, 152 |
| abstract_inverted_index.model, | 30 |
| abstract_inverted_index.module | 59, 67, 81, 94 |
| abstract_inverted_index.plants | 97 |
| abstract_inverted_index.posing | 17 |
| abstract_inverted_index.recall | 123, 160 |
| abstract_inverted_index.scores | 132, 168 |
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| abstract_inverted_index.target | 100 |
| abstract_inverted_index.threat | 20 |
| abstract_inverted_index.widely | 13 |
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| abstract_inverted_index.4.634%, | 178 |
| abstract_inverted_index.Solanum | 0 |
| abstract_inverted_index.applies | 68 |
| abstract_inverted_index.crucial | 191 |
| abstract_inverted_index.frames, | 78 |
| abstract_inverted_index.harmful | 7 |
| abstract_inverted_index.method. | 104 |
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| abstract_inverted_index.results | 108 |
| abstract_inverted_index.serious | 19 |
| abstract_inverted_index.support | 193 |
| abstract_inverted_index.through | 88 |
| abstract_inverted_index.DeepSort | 69 |
| abstract_inverted_index.achieved | 120 |
| abstract_inverted_index.counting | 39, 93, 138, 174 |
| abstract_inverted_index.designed | 33 |
| abstract_inverted_index.globally | 6 |
| abstract_inverted_index.invasive | 8 |
| abstract_inverted_index.learning | 28 |
| abstract_inverted_index.modules: | 51 |
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| abstract_inverted_index.supports | 186 |
| abstract_inverted_index.tracking | 66, 72 |
| abstract_inverted_index.YOLO_EAND | 61 |
| abstract_inverted_index.comprises | 49 |
| abstract_inverted_index.counting. | 56 |
| abstract_inverted_index.detection | 58 |
| abstract_inverted_index.ecosystem | 24 |
| abstract_inverted_index.location, | 37 |
| abstract_inverted_index.precision | 121, 158 |
| abstract_inverted_index.real-time | 35, 187 |
| abstract_inverted_index.rostratum | 1 |
| abstract_inverted_index.security. | 25 |
| abstract_inverted_index.technical | 192 |
| abstract_inverted_index.tracking, | 53 |
| abstract_inverted_index.assessment | 196 |
| abstract_inverted_index.countries, | 16 |
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| abstract_inverted_index.TrackSolanmu | 46 |
| abstract_inverted_index.TrackSolanum | 119, 185 |
| abstract_inverted_index.invalidation | 103 |
| abstract_inverted_index.localization | 80 |
| abstract_inverted_index.multi-target | 71 |
| abstract_inverted_index.TrackSolanum, | 31 |
| abstract_inverted_index.localization, | 54, 90 |
| abstract_inverted_index.over-the-line | 102 |
| abstract_inverted_index.center-of-mass | 89 |
| abstract_inverted_index.identification, | 64 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5102059343, https://openalex.org/A5110195034 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I108160477 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/2 |
| sustainable_development_goals[0].score | 0.7300000190734863 |
| sustainable_development_goals[0].display_name | Zero hunger |
| citation_normalized_percentile.value | 0.97922188 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |