Rapeseed Stand Count Estimation at Leaf Development Stages With UAV Imagery and Convolutional Neural Networks Article Swipe
Jian Zhang
,
Biquan Zhao
,
Chenghai Yang
,
Yeyin Shi
,
Qingxi Liao
,
Guangsheng Zhou
,
Chufeng Wang
,
Tianjin Xie
,
Zhao Jiang
,
Dongyan Zhang
,
Wanneng Yang
,
Chenglong Huang
,
Jing Xie
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3389/fpls.2020.00617
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3389/fpls.2020.00617
Rapeseed is an important oil crop in China. Timely estimation of rapeseed stand count at early growth stages provides useful information for precision fertilization, irrigation, and yield prediction. Based on the nature of rapeseed, the number of tillering leaves is strongly related to its growth stages. However, no field study has been reported on estimating rapeseed stand count by the number of leaves recognized with convolutional neural networks (CNNs) in unmanned aerial vehicle (UAV) imagery. The objectives of this study were to provide a case for rapeseed stand counting with reference to the existing knowledge of the number of leaves per plant and to determine the optimal timing for counting after rapeseed emergence at leaf development stages with one to seven leaves. A CNN model was developed to recognize leaves in UAV-based imagery, and rapeseed stand count was estimated with the number of recognized leaves. The performance of leaf detection was compared using sample sizes of 16, 24, 32, 40, and 48 pixels. Leaf overcounting occurred when a leaf was much bigger than others as this bigger leaf was recognized as several smaller leaves. Results showed CNN-based leaf count achieved the best performance at the four- to six-leaf stage with F-scores greater than 90% after calibration with overcounting rate. On average, 806 out of 812 plants were correctly estimated on 53 days after planting (DAP) at the four- to six-leaf stage, which was considered as the optimal observation timing. For the 32-pixel patch size, root mean square error (RMSE) was 9 plants with relative RMSE (rRMSE) of 2.22% on 53 DAP, while the mean RMSE was 12 with mean rRMSE of 2.89% for all patch sizes. A sample size of 32 pixels was suggested to be optimal accounting for balancing performance and efficiency. The results of this study confirmed that it was feasible to estimate rapeseed stand count in field automatically, rapidly, and accurately. This study provided a special perspective in phenotyping and cultivation management for estimating seedling count for crops that have recognizable leaves at their early growth stage, such as soybean and potato.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fpls.2020.00617
- https://www.frontiersin.org/articles/10.3389/fpls.2020.00617/pdf
- OA Status
- gold
- Cited By
- 36
- References
- 73
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3034275306
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3034275306Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fpls.2020.00617Digital Object Identifier
- Title
-
Rapeseed Stand Count Estimation at Leaf Development Stages With UAV Imagery and Convolutional Neural NetworksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-06-10Full publication date if available
- Authors
-
Jian Zhang, Biquan Zhao, Chenghai Yang, Yeyin Shi, Qingxi Liao, Guangsheng Zhou, Chufeng Wang, Tianjin Xie, Zhao Jiang, Dongyan Zhang, Wanneng Yang, Chenglong Huang, Jing XieList of authors in order
- Landing page
-
https://doi.org/10.3389/fpls.2020.00617Publisher landing page
- PDF URL
-
https://www.frontiersin.org/articles/10.3389/fpls.2020.00617/pdfDirect 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.frontiersin.org/articles/10.3389/fpls.2020.00617/pdfDirect OA link when available
- Concepts
-
Rapeseed, Sowing, Convolutional neural network, Irrigation, Human fertilization, Mathematics, Biology, Agronomy, Statistics, Computer science, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
36Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 7, 2024: 13, 2023: 7, 2022: 5, 2021: 2Per-year citation counts (last 5 years)
- References (count)
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73Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.feasible | 302 |
| abstract_inverted_index.imagery, | 132 |
| abstract_inverted_index.imagery. | 74 |
| abstract_inverted_index.networks | 67 |
| abstract_inverted_index.occurred | 165 |
| abstract_inverted_index.planting | 223 |
| abstract_inverted_index.provided | 316 |
| abstract_inverted_index.provides | 18 |
| abstract_inverted_index.rapeseed | 11, 55, 86, 111, 134, 305 |
| abstract_inverted_index.rapidly, | 311 |
| abstract_inverted_index.relative | 253 |
| abstract_inverted_index.reported | 52 |
| abstract_inverted_index.seedling | 327 |
| abstract_inverted_index.six-leaf | 197, 229 |
| abstract_inverted_index.strongly | 40 |
| abstract_inverted_index.unmanned | 70 |
| abstract_inverted_index.CNN-based | 186 |
| abstract_inverted_index.UAV-based | 131 |
| abstract_inverted_index.balancing | 289 |
| abstract_inverted_index.confirmed | 298 |
| abstract_inverted_index.correctly | 217 |
| abstract_inverted_index.detection | 149 |
| abstract_inverted_index.determine | 104 |
| abstract_inverted_index.developed | 126 |
| abstract_inverted_index.emergence | 112 |
| abstract_inverted_index.estimated | 138, 218 |
| abstract_inverted_index.important | 3 |
| abstract_inverted_index.knowledge | 94 |
| abstract_inverted_index.precision | 22 |
| abstract_inverted_index.rapeseed, | 33 |
| abstract_inverted_index.recognize | 128 |
| abstract_inverted_index.reference | 90 |
| abstract_inverted_index.suggested | 283 |
| abstract_inverted_index.tillering | 37 |
| abstract_inverted_index.accounting | 287 |
| abstract_inverted_index.considered | 233 |
| abstract_inverted_index.estimating | 54, 326 |
| abstract_inverted_index.estimation | 9 |
| abstract_inverted_index.management | 324 |
| abstract_inverted_index.objectives | 76 |
| abstract_inverted_index.recognized | 63, 143, 179 |
| abstract_inverted_index.accurately. | 313 |
| abstract_inverted_index.calibration | 205 |
| abstract_inverted_index.cultivation | 323 |
| abstract_inverted_index.development | 115 |
| abstract_inverted_index.efficiency. | 292 |
| abstract_inverted_index.information | 20 |
| abstract_inverted_index.irrigation, | 24 |
| abstract_inverted_index.observation | 237 |
| abstract_inverted_index.performance | 146, 192, 290 |
| abstract_inverted_index.perspective | 319 |
| abstract_inverted_index.phenotyping | 321 |
| abstract_inverted_index.prediction. | 27 |
| abstract_inverted_index.overcounting | 164, 207 |
| abstract_inverted_index.recognizable | 333 |
| abstract_inverted_index.convolutional | 65 |
| abstract_inverted_index.automatically, | 310 |
| abstract_inverted_index.fertilization, | 23 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 93 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 13 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/2 |
| sustainable_development_goals[0].score | 0.6299999952316284 |
| sustainable_development_goals[0].display_name | Zero hunger |
| citation_normalized_percentile.value | 0.88860912 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |