Initial Machine Learning Framework Development of Agriculture Cyber Physical Systems Article Swipe
Gregorius Airlangga
,
Alan Liu
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/1196/1/012065
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/1196/1/012065
A cyber-physical system (CPS) shows its potential in integrating the computer systems with the physical environment. The progress in such systems has brought large potential to improve human life quality, such as improved health care services, energy consumption and food supply chain system (FSC). In particular, FSC plays an important role in human beings. It comes from the complex agricultural system and lead up to human dining table. The fusion of CPS and agricultural system could improve the quality of food and environment. Therefore, many studies have been conducted to tackle the challenges in this domain, such as lack of information systems and infrastructures, poor collaboration toward larger Internet of Things solutions and dynamic changes of intrinsic and extrinsic conditions of enabler technology in precision agriculture. In this work, we focus on developing an initial framework that could improve prediction rate and handling imprecise data due to the dynamic problem in precision agriculture. As an evaluation, first we predict a rainfall event using weather sensors data and after that the prediction result will be continue to set up as an additional attribute for predicting the action of water sprinkle monitoring system. The results confirm the good accuracy for farmers and could be applicable in real-time.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1742-6596/1196/1/012065
- OA Status
- diamond
- Cited By
- 9
- References
- 9
- Related Works
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- OpenAlex ID
- https://openalex.org/W2936005232
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W2936005232Canonical identifier for this work in OpenAlex
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https://doi.org/10.1088/1742-6596/1196/1/012065Digital Object Identifier
- Title
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Initial Machine Learning Framework Development of Agriculture Cyber Physical SystemsWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
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2019-03-01Full publication date if available
- Authors
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Gregorius Airlangga, Alan LiuList of authors in order
- Landing page
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https://doi.org/10.1088/1742-6596/1196/1/012065Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
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https://doi.org/10.1088/1742-6596/1196/1/012065Direct OA link when available
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Enabling, Precision agriculture, Cyber-physical system, Computer science, Agriculture, Domain (mathematical analysis), Table (database), Risk analysis (engineering), Data science, Business, Data mining, Biology, Mathematical analysis, Psychology, Psychotherapist, Ecology, Operating system, MathematicsTop concepts (fields/topics) attached by OpenAlex
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9Total citation count in OpenAlex
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2024: 1, 2023: 3, 2022: 1, 2021: 1, 2020: 2Per-year citation counts (last 5 years)
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9Number 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.result | 171 |
| abstract_inverted_index.supply | 40 |
| abstract_inverted_index.system | 2, 42, 60, 74 |
| abstract_inverted_index.table. | 67 |
| abstract_inverted_index.tackle | 90 |
| abstract_inverted_index.toward | 106 |
| abstract_inverted_index.beings. | 53 |
| abstract_inverted_index.brought | 22 |
| abstract_inverted_index.changes | 114 |
| abstract_inverted_index.complex | 58 |
| abstract_inverted_index.confirm | 193 |
| abstract_inverted_index.domain, | 95 |
| abstract_inverted_index.dynamic | 113, 148 |
| abstract_inverted_index.enabler | 121 |
| abstract_inverted_index.farmers | 198 |
| abstract_inverted_index.improve | 26, 76, 138 |
| abstract_inverted_index.initial | 134 |
| abstract_inverted_index.predict | 158 |
| abstract_inverted_index.problem | 149 |
| abstract_inverted_index.quality | 78 |
| abstract_inverted_index.results | 192 |
| abstract_inverted_index.sensors | 164 |
| abstract_inverted_index.studies | 85 |
| abstract_inverted_index.system. | 190 |
| abstract_inverted_index.systems | 11, 20, 101 |
| abstract_inverted_index.weather | 163 |
| abstract_inverted_index.Internet | 108 |
| abstract_inverted_index.accuracy | 196 |
| abstract_inverted_index.computer | 10 |
| abstract_inverted_index.continue | 174 |
| abstract_inverted_index.handling | 142 |
| abstract_inverted_index.improved | 32 |
| abstract_inverted_index.physical | 14 |
| abstract_inverted_index.progress | 17 |
| abstract_inverted_index.quality, | 29 |
| abstract_inverted_index.rainfall | 160 |
| abstract_inverted_index.sprinkle | 188 |
| abstract_inverted_index.attribute | 181 |
| abstract_inverted_index.conducted | 88 |
| abstract_inverted_index.extrinsic | 118 |
| abstract_inverted_index.framework | 135 |
| abstract_inverted_index.important | 49 |
| abstract_inverted_index.imprecise | 143 |
| abstract_inverted_index.intrinsic | 116 |
| abstract_inverted_index.potential | 6, 24 |
| abstract_inverted_index.precision | 124, 151 |
| abstract_inverted_index.services, | 35 |
| abstract_inverted_index.solutions | 111 |
| abstract_inverted_index.Therefore, | 83 |
| abstract_inverted_index.additional | 180 |
| abstract_inverted_index.applicable | 202 |
| abstract_inverted_index.challenges | 92 |
| abstract_inverted_index.conditions | 119 |
| abstract_inverted_index.developing | 132 |
| abstract_inverted_index.monitoring | 189 |
| abstract_inverted_index.predicting | 183 |
| abstract_inverted_index.prediction | 139, 170 |
| abstract_inverted_index.real-time. | 204 |
| abstract_inverted_index.technology | 122 |
| abstract_inverted_index.consumption | 37 |
| abstract_inverted_index.evaluation, | 155 |
| abstract_inverted_index.information | 100 |
| abstract_inverted_index.integrating | 8 |
| abstract_inverted_index.particular, | 45 |
| abstract_inverted_index.agricultural | 59, 73 |
| abstract_inverted_index.agriculture. | 125, 152 |
| abstract_inverted_index.environment. | 15, 82 |
| abstract_inverted_index.collaboration | 105 |
| abstract_inverted_index.cyber-physical | 1 |
| abstract_inverted_index.infrastructures, | 103 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5101726185, https://openalex.org/A5032437973 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I148099254, https://openalex.org/I161010349 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/2 |
| sustainable_development_goals[0].score | 0.5699999928474426 |
| sustainable_development_goals[0].display_name | Zero hunger |
| citation_normalized_percentile.value | 0.85691288 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |