Deep learning-enhanced remote sensing-integrated crop modeling for rice yield prediction Article Swipe
Seungtaek Jeong
,
Jonghan Ko
,
Jong-Oh Ban
,
Taehwan Shin
,
Jong‐Min Yeom
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.ecoinf.2024.102886
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.ecoinf.2024.102886
This study introduces a novel crop modeling approach based on cutting-edge computational tools to advance crop production monitoring methodologies, and, thereby, tackle global food security issues. Our approach pioneers integrating deep learning and remote sensing with process-based crop models to enhance rice yield predictions while leveraging the strengths and weaknesses of each model. We developed and evaluated four models based on distinct deep neural network architectures: feed-forward neural network, long short-term memory (LSTM), gated recurrent units, and bidirectional LSTM. All the models demonstrated high predictive accuracies, with percent biases of 0.74–2.62 and Nash–Sutcliffe model efficiencies of 0.954–0.996; however, the LSTM performed best among the four models. Notably, the models' performances varied when applied to regional datasets that were not included in the training phase; this highlighted the critical need for diverse training data to enhance model robustness. This research marks a significant advancement in agricultural modeling by combining state-of-the-art computational techniques with established methodologies, setting a new standard for crop yield prediction.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.ecoinf.2024.102886
- OA Status
- gold
- Cited By
- 22
- References
- 52
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4404201893Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.ecoinf.2024.102886Digital Object Identifier
- Title
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Deep learning-enhanced remote sensing-integrated crop modeling for rice yield predictionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-11-09Full publication date if available
- Authors
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Seungtaek Jeong, Jonghan Ko, Jong-Oh Ban, Taehwan Shin, Jong‐Min YeomList of authors in order
- Landing page
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https://doi.org/10.1016/j.ecoinf.2024.102886Publisher 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/10.1016/j.ecoinf.2024.102886Direct OA link when available
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Yield (engineering), Crop, Crop yield, Deep learning, Computer science, Agricultural engineering, Artificial intelligence, Agronomy, Machine learning, Biology, Engineering, Materials science, MetallurgyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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22Total citation count in OpenAlex
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2025: 20, 2024: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.tools | 12 |
| abstract_inverted_index.while | 44 |
| abstract_inverted_index.yield | 42, 160 |
| abstract_inverted_index.biases | 88 |
| abstract_inverted_index.global | 22 |
| abstract_inverted_index.memory | 71 |
| abstract_inverted_index.model. | 52 |
| abstract_inverted_index.models | 38, 58, 81 |
| abstract_inverted_index.neural | 63, 67 |
| abstract_inverted_index.phase; | 123 |
| abstract_inverted_index.remote | 33 |
| abstract_inverted_index.tackle | 21 |
| abstract_inverted_index.units, | 75 |
| abstract_inverted_index.varied | 110 |
| abstract_inverted_index.(LSTM), | 72 |
| abstract_inverted_index.advance | 14 |
| abstract_inverted_index.applied | 112 |
| abstract_inverted_index.diverse | 130 |
| abstract_inverted_index.enhance | 40, 134 |
| abstract_inverted_index.issues. | 25 |
| abstract_inverted_index.models' | 108 |
| abstract_inverted_index.models. | 105 |
| abstract_inverted_index.network | 64 |
| abstract_inverted_index.percent | 87 |
| abstract_inverted_index.sensing | 34 |
| abstract_inverted_index.setting | 154 |
| abstract_inverted_index.Notably, | 106 |
| abstract_inverted_index.approach | 7, 27 |
| abstract_inverted_index.critical | 127 |
| abstract_inverted_index.datasets | 115 |
| abstract_inverted_index.distinct | 61 |
| abstract_inverted_index.however, | 97 |
| abstract_inverted_index.included | 119 |
| abstract_inverted_index.learning | 31 |
| abstract_inverted_index.modeling | 6, 145 |
| abstract_inverted_index.network, | 68 |
| abstract_inverted_index.pioneers | 28 |
| abstract_inverted_index.regional | 114 |
| abstract_inverted_index.research | 138 |
| abstract_inverted_index.security | 24 |
| abstract_inverted_index.standard | 157 |
| abstract_inverted_index.thereby, | 20 |
| abstract_inverted_index.training | 122, 131 |
| abstract_inverted_index.combining | 147 |
| abstract_inverted_index.developed | 54 |
| abstract_inverted_index.evaluated | 56 |
| abstract_inverted_index.performed | 100 |
| abstract_inverted_index.recurrent | 74 |
| abstract_inverted_index.strengths | 47 |
| abstract_inverted_index.introduces | 2 |
| abstract_inverted_index.leveraging | 45 |
| abstract_inverted_index.monitoring | 17 |
| abstract_inverted_index.predictive | 84 |
| abstract_inverted_index.production | 16 |
| abstract_inverted_index.short-term | 70 |
| abstract_inverted_index.techniques | 150 |
| abstract_inverted_index.weaknesses | 49 |
| abstract_inverted_index.0.74–2.62 | 90 |
| abstract_inverted_index.accuracies, | 85 |
| abstract_inverted_index.advancement | 142 |
| abstract_inverted_index.established | 152 |
| abstract_inverted_index.highlighted | 125 |
| abstract_inverted_index.integrating | 29 |
| abstract_inverted_index.prediction. | 161 |
| abstract_inverted_index.predictions | 43 |
| abstract_inverted_index.robustness. | 136 |
| abstract_inverted_index.significant | 141 |
| abstract_inverted_index.agricultural | 144 |
| abstract_inverted_index.cutting-edge | 10 |
| abstract_inverted_index.demonstrated | 82 |
| abstract_inverted_index.efficiencies | 94 |
| abstract_inverted_index.feed-forward | 66 |
| abstract_inverted_index.performances | 109 |
| abstract_inverted_index.bidirectional | 77 |
| abstract_inverted_index.computational | 11, 149 |
| abstract_inverted_index.process-based | 36 |
| abstract_inverted_index.0.954–0.996; | 96 |
| abstract_inverted_index.architectures: | 65 |
| abstract_inverted_index.methodologies, | 18, 153 |
| abstract_inverted_index.Nash–Sutcliffe | 92 |
| abstract_inverted_index.state-of-the-art | 148 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 94 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/2 |
| sustainable_development_goals[0].score | 0.7799999713897705 |
| sustainable_development_goals[0].display_name | Zero hunger |
| citation_normalized_percentile.value | 0.99112967 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |