Empowering Greenhouse Cultivation: Dynamic Factors and Machine Learning Unite for Advanced Microclimate Prediction Article Swipe
Climate change has led to more frequent extreme weather events such as heatwaves, droughts, and storms, which significantly impact agriculture, causing crop damage. Greenhouse cultivation not only provides a manageable environment that protects crops from external weather conditions and pests but also requires precise microclimate control. However, greenhouse microclimates are complex since various heat transfer mechanisms would be difficult to model properly. This study proposes an innovative hybrid model (DF-RF-ANN), which seamlessly fuses three components: the dynamic factor (DF) model to extract unobserved factors, the random forest (RF) to identify key input factors, and a backpropagation neural network (BPNN) to predict greenhouse microclimate, including internal temperature, relative humidity, photosynthetically active radiation, and carbon dioxide. The proposed model utilized gridded meteorological big data and was applied to a greenhouse in Taichung, Taiwan. Two comparative models were configured using the BPNN and the Long short-term memory neural network (LSTM). The results demonstrate that DF-RF-ANN effectively captures the trends of the observations and generates predictions much closer to the observations compared to LSTM and BPNN. The proposed DF-RF-ANN model hits a milestone in multi-horizon and multi-factor microclimate predictions and offers a cost-effective and easily accessible approach. This approach could be particularly beneficial for small-scale farmers to make the best use of resources under extreme climatic events for contributing to sustainable development goals (SDGs) and the transition towards a green economy.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/w15203548
- https://www.mdpi.com/2073-4441/15/20/3548/pdf?version=1697024726
- OA Status
- gold
- Cited By
- 7
- References
- 59
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387530505
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4387530505Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/w15203548Digital Object Identifier
- Title
-
Empowering Greenhouse Cultivation: Dynamic Factors and Machine Learning Unite for Advanced Microclimate PredictionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-10-11Full publication date if available
- Authors
-
Wei Sun, Fi‐John ChangList of authors in order
- Landing page
-
https://doi.org/10.3390/w15203548Publisher landing page
- PDF URL
-
https://www.mdpi.com/2073-4441/15/20/3548/pdf?version=1697024726Direct 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.mdpi.com/2073-4441/15/20/3548/pdf?version=1697024726Direct OA link when available
- Concepts
-
Microclimate, Greenhouse, Environmental science, Random forest, Artificial neural network, Meteorology, Climate change, Computer science, Extreme weather, Agricultural engineering, Machine learning, Ecology, Engineering, Geography, Biology, HorticultureTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 2Per-year citation counts (last 5 years)
- References (count)
-
59Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4387530505 |
|---|---|
| doi | https://doi.org/10.3390/w15203548 |
| ids.doi | https://doi.org/10.3390/w15203548 |
| ids.openalex | https://openalex.org/W4387530505 |
| fwci | 1.84926163 |
| type | article |
| title | Empowering Greenhouse Cultivation: Dynamic Factors and Machine Learning Unite for Advanced Microclimate Prediction |
| biblio.issue | 20 |
| biblio.volume | 15 |
| biblio.last_page | 3548 |
| biblio.first_page | 3548 |
| topics[0].id | https://openalex.org/T12093 |
| topics[0].field.id | https://openalex.org/fields/11 |
| topics[0].field.display_name | Agricultural and Biological Sciences |
| topics[0].score | 0.9994999766349792 |
| topics[0].domain.id | https://openalex.org/domains/1 |
| topics[0].domain.display_name | Life Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1110 |
| topics[0].subfield.display_name | Plant Science |
| topics[0].display_name | Greenhouse Technology and Climate Control |
| topics[1].id | https://openalex.org/T10266 |
| topics[1].field.id | https://openalex.org/fields/23 |
| topics[1].field.display_name | Environmental Science |
| topics[1].score | 0.9979000091552734 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2306 |
| topics[1].subfield.display_name | Global and Planetary Change |
| topics[1].display_name | Plant Water Relations and Carbon Dynamics |
| topics[2].id | https://openalex.org/T10111 |
| topics[2].field.id | https://openalex.org/fields/23 |
| topics[2].field.display_name | Environmental Science |
| topics[2].score | 0.9851999878883362 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2303 |
| topics[2].subfield.display_name | Ecology |
| topics[2].display_name | Remote Sensing in Agriculture |
| is_xpac | False |
| apc_list.value | 2200 |
| apc_list.currency | CHF |
| apc_list.value_usd | 2382 |
| apc_paid.value | 2200 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 2382 |
| concepts[0].id | https://openalex.org/C32957820 |
| concepts[0].level | 2 |
| concepts[0].score | 0.9345178604125977 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q215108 |
| concepts[0].display_name | Microclimate |
| concepts[1].id | https://openalex.org/C32198211 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7171965837478638 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q165044 |
| concepts[1].display_name | Greenhouse |
| concepts[2].id | https://openalex.org/C39432304 |
| concepts[2].level | 0 |
| concepts[2].score | 0.6888158321380615 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q188847 |
| concepts[2].display_name | Environmental science |
| concepts[3].id | https://openalex.org/C169258074 |
| concepts[3].level | 2 |
| concepts[3].score | 0.6098461747169495 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q245748 |
| concepts[3].display_name | Random forest |
| concepts[4].id | https://openalex.org/C50644808 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5096163749694824 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[4].display_name | Artificial neural network |
| concepts[5].id | https://openalex.org/C153294291 |
| concepts[5].level | 1 |
| concepts[5].score | 0.5035526156425476 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q25261 |
| concepts[5].display_name | Meteorology |
| concepts[6].id | https://openalex.org/C132651083 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4952518045902252 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q7942 |
| concepts[6].display_name | Climate change |
| concepts[7].id | https://openalex.org/C41008148 |
| concepts[7].level | 0 |
| concepts[7].score | 0.43016090989112854 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[7].display_name | Computer science |
| concepts[8].id | https://openalex.org/C205537798 |
| concepts[8].level | 3 |
| concepts[8].score | 0.413469523191452 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q1277161 |
| concepts[8].display_name | Extreme weather |
| concepts[9].id | https://openalex.org/C88463610 |
| concepts[9].level | 1 |
| concepts[9].score | 0.37263697385787964 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q194118 |
| concepts[9].display_name | Agricultural engineering |
| concepts[10].id | https://openalex.org/C119857082 |
| concepts[10].level | 1 |
| concepts[10].score | 0.27951785922050476 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[10].display_name | Machine learning |
| concepts[11].id | https://openalex.org/C18903297 |
| concepts[11].level | 1 |
| concepts[11].score | 0.19104838371276855 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q7150 |
| concepts[11].display_name | Ecology |
| concepts[12].id | https://openalex.org/C127413603 |
| concepts[12].level | 0 |
| concepts[12].score | 0.15599611401557922 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[12].display_name | Engineering |
| concepts[13].id | https://openalex.org/C205649164 |
| concepts[13].level | 0 |
| concepts[13].score | 0.1401166021823883 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[13].display_name | Geography |
| concepts[14].id | https://openalex.org/C86803240 |
| concepts[14].level | 0 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[14].display_name | Biology |
| concepts[15].id | https://openalex.org/C144027150 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q48803 |
| concepts[15].display_name | Horticulture |
| keywords[0].id | https://openalex.org/keywords/microclimate |
| keywords[0].score | 0.9345178604125977 |
| keywords[0].display_name | Microclimate |
| keywords[1].id | https://openalex.org/keywords/greenhouse |
| keywords[1].score | 0.7171965837478638 |
| keywords[1].display_name | Greenhouse |
| keywords[2].id | https://openalex.org/keywords/environmental-science |
| keywords[2].score | 0.6888158321380615 |
| keywords[2].display_name | Environmental science |
| keywords[3].id | https://openalex.org/keywords/random-forest |
| keywords[3].score | 0.6098461747169495 |
| keywords[3].display_name | Random forest |
| keywords[4].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[4].score | 0.5096163749694824 |
| keywords[4].display_name | Artificial neural network |
| keywords[5].id | https://openalex.org/keywords/meteorology |
| keywords[5].score | 0.5035526156425476 |
| keywords[5].display_name | Meteorology |
| keywords[6].id | https://openalex.org/keywords/climate-change |
| keywords[6].score | 0.4952518045902252 |
| keywords[6].display_name | Climate change |
| keywords[7].id | https://openalex.org/keywords/computer-science |
| keywords[7].score | 0.43016090989112854 |
| keywords[7].display_name | Computer science |
| keywords[8].id | https://openalex.org/keywords/extreme-weather |
| keywords[8].score | 0.413469523191452 |
| keywords[8].display_name | Extreme weather |
| keywords[9].id | https://openalex.org/keywords/agricultural-engineering |
| keywords[9].score | 0.37263697385787964 |
| keywords[9].display_name | Agricultural engineering |
| keywords[10].id | https://openalex.org/keywords/machine-learning |
| keywords[10].score | 0.27951785922050476 |
| keywords[10].display_name | Machine learning |
| keywords[11].id | https://openalex.org/keywords/ecology |
| keywords[11].score | 0.19104838371276855 |
| keywords[11].display_name | Ecology |
| keywords[12].id | https://openalex.org/keywords/engineering |
| keywords[12].score | 0.15599611401557922 |
| keywords[12].display_name | Engineering |
| keywords[13].id | https://openalex.org/keywords/geography |
| keywords[13].score | 0.1401166021823883 |
| keywords[13].display_name | Geography |
| language | en |
| locations[0].id | doi:10.3390/w15203548 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S134216166 |
| locations[0].source.issn | 2073-4441 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2073-4441 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Water |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.mdpi.com/2073-4441/15/20/3548/pdf?version=1697024726 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Water |
| locations[0].landing_page_url | https://doi.org/10.3390/w15203548 |
| locations[1].id | pmh:oai:doaj.org/article:3fa60f054ebf4b9c9d6a422e5c9c75aa |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306401280 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Water, Vol 15, Iss 20, p 3548 (2023) |
| locations[1].landing_page_url | https://doaj.org/article/3fa60f054ebf4b9c9d6a422e5c9c75aa |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5066069021 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-2768-2171 |
| authorships[0].author.display_name | Wei Sun |
| authorships[0].countries | TW |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I16733864 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan |
| authorships[0].institutions[0].id | https://openalex.org/I16733864 |
| authorships[0].institutions[0].ror | https://ror.org/05bqach95 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I16733864 |
| authorships[0].institutions[0].country_code | TW |
| authorships[0].institutions[0].display_name | National Taiwan University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Wei Sun |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan |
| authorships[1].author.id | https://openalex.org/A5089700077 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-1655-8573 |
| authorships[1].author.display_name | Fi‐John Chang |
| authorships[1].countries | TW |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I16733864 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan |
| authorships[1].institutions[0].id | https://openalex.org/I16733864 |
| authorships[1].institutions[0].ror | https://ror.org/05bqach95 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I16733864 |
| authorships[1].institutions[0].country_code | TW |
| authorships[1].institutions[0].display_name | National Taiwan University |
| authorships[1].author_position | last |
| authorships[1].raw_author_name | Fi-John Chang |
| authorships[1].is_corresponding | True |
| authorships[1].raw_affiliation_strings | Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.mdpi.com/2073-4441/15/20/3548/pdf?version=1697024726 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Empowering Greenhouse Cultivation: Dynamic Factors and Machine Learning Unite for Advanced Microclimate Prediction |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T12093 |
| primary_topic.field.id | https://openalex.org/fields/11 |
| primary_topic.field.display_name | Agricultural and Biological Sciences |
| primary_topic.score | 0.9994999766349792 |
| primary_topic.domain.id | https://openalex.org/domains/1 |
| primary_topic.domain.display_name | Life Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1110 |
| primary_topic.subfield.display_name | Plant Science |
| primary_topic.display_name | Greenhouse Technology and Climate Control |
| related_works | https://openalex.org/W2348921879, https://openalex.org/W2278747580, https://openalex.org/W2386888651, https://openalex.org/W2901169164, https://openalex.org/W2367086696, https://openalex.org/W2945509375, https://openalex.org/W3114722316, https://openalex.org/W3102911822, https://openalex.org/W2596946144, https://openalex.org/W2383824158 |
| cited_by_count | 7 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 5 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 2 |
| locations_count | 2 |
| best_oa_location.id | doi:10.3390/w15203548 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S134216166 |
| best_oa_location.source.issn | 2073-4441 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2073-4441 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Water |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.mdpi.com/2073-4441/15/20/3548/pdf?version=1697024726 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Water |
| best_oa_location.landing_page_url | https://doi.org/10.3390/w15203548 |
| primary_location.id | doi:10.3390/w15203548 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S134216166 |
| primary_location.source.issn | 2073-4441 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2073-4441 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Water |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/2073-4441/15/20/3548/pdf?version=1697024726 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Water |
| primary_location.landing_page_url | https://doi.org/10.3390/w15203548 |
| publication_date | 2023-10-11 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2032848668, https://openalex.org/W2291555931, https://openalex.org/W2078784876, https://openalex.org/W2048943066, https://openalex.org/W2346369651, https://openalex.org/W2000205466, https://openalex.org/W1535343965, https://openalex.org/W3211676960, https://openalex.org/W2020327598, https://openalex.org/W2007181153, https://openalex.org/W1991320844, https://openalex.org/W4320728990, https://openalex.org/W4361283605, https://openalex.org/W6839013240, https://openalex.org/W4361002120, https://openalex.org/W4362014133, https://openalex.org/W2890946365, https://openalex.org/W3019878414, https://openalex.org/W2763520430, https://openalex.org/W3003741609, https://openalex.org/W1977563642, https://openalex.org/W3009964072, https://openalex.org/W4311527723, https://openalex.org/W3015908511, https://openalex.org/W4295813769, https://openalex.org/W4383955565, https://openalex.org/W4293221246, https://openalex.org/W2150186167, https://openalex.org/W4307492541, https://openalex.org/W2935764766, https://openalex.org/W2550944785, https://openalex.org/W2039237557, https://openalex.org/W3186976921, https://openalex.org/W1975940830, https://openalex.org/W3021308819, https://openalex.org/W2950297014, https://openalex.org/W2900126700, https://openalex.org/W4323256886, https://openalex.org/W2018466646, https://openalex.org/W2035549409, https://openalex.org/W2911964244, https://openalex.org/W2787894218, https://openalex.org/W4308156775, https://openalex.org/W2982451313, https://openalex.org/W2786428026, https://openalex.org/W2441507532, https://openalex.org/W2067183857, https://openalex.org/W4300402905, https://openalex.org/W2802436364, https://openalex.org/W3157758910, https://openalex.org/W2144031751, https://openalex.org/W2181523240, https://openalex.org/W2331787540, https://openalex.org/W2065821457, https://openalex.org/W1600236695, https://openalex.org/W2078104806, https://openalex.org/W2147110058, https://openalex.org/W1983185286, https://openalex.org/W4285157836 |
| referenced_works_count | 59 |
| abstract_inverted_index.a | 28, 94, 126, 177, 187, 224 |
| abstract_inverted_index.an | 65 |
| abstract_inverted_index.as | 11 |
| abstract_inverted_index.be | 57, 196 |
| abstract_inverted_index.in | 128, 179 |
| abstract_inverted_index.of | 156, 207 |
| abstract_inverted_index.to | 4, 59, 80, 88, 99, 125, 164, 168, 202, 215 |
| abstract_inverted_index.The | 114, 147, 172 |
| abstract_inverted_index.Two | 131 |
| abstract_inverted_index.and | 14, 38, 93, 111, 122, 139, 159, 170, 181, 185, 189, 220 |
| abstract_inverted_index.are | 49 |
| abstract_inverted_index.big | 120 |
| abstract_inverted_index.but | 40 |
| abstract_inverted_index.for | 199, 213 |
| abstract_inverted_index.has | 2 |
| abstract_inverted_index.key | 90 |
| abstract_inverted_index.led | 3 |
| abstract_inverted_index.not | 25 |
| abstract_inverted_index.the | 75, 84, 137, 140, 154, 157, 165, 204, 221 |
| abstract_inverted_index.use | 206 |
| abstract_inverted_index.was | 123 |
| abstract_inverted_index.(DF) | 78 |
| abstract_inverted_index.(RF) | 87 |
| abstract_inverted_index.BPNN | 138 |
| abstract_inverted_index.LSTM | 169 |
| abstract_inverted_index.Long | 141 |
| abstract_inverted_index.This | 62, 193 |
| abstract_inverted_index.also | 41 |
| abstract_inverted_index.best | 205 |
| abstract_inverted_index.crop | 21 |
| abstract_inverted_index.data | 121 |
| abstract_inverted_index.from | 34 |
| abstract_inverted_index.heat | 53 |
| abstract_inverted_index.hits | 176 |
| abstract_inverted_index.make | 203 |
| abstract_inverted_index.more | 5 |
| abstract_inverted_index.much | 162 |
| abstract_inverted_index.only | 26 |
| abstract_inverted_index.such | 10 |
| abstract_inverted_index.that | 31, 150 |
| abstract_inverted_index.were | 134 |
| abstract_inverted_index.BPNN. | 171 |
| abstract_inverted_index.could | 195 |
| abstract_inverted_index.crops | 33 |
| abstract_inverted_index.fuses | 72 |
| abstract_inverted_index.goals | 218 |
| abstract_inverted_index.green | 225 |
| abstract_inverted_index.input | 91 |
| abstract_inverted_index.model | 60, 68, 79, 116, 175 |
| abstract_inverted_index.pests | 39 |
| abstract_inverted_index.since | 51 |
| abstract_inverted_index.study | 63 |
| abstract_inverted_index.three | 73 |
| abstract_inverted_index.under | 209 |
| abstract_inverted_index.using | 136 |
| abstract_inverted_index.which | 16, 70 |
| abstract_inverted_index.would | 56 |
| abstract_inverted_index.(BPNN) | 98 |
| abstract_inverted_index.(SDGs) | 219 |
| abstract_inverted_index.active | 109 |
| abstract_inverted_index.carbon | 112 |
| abstract_inverted_index.change | 1 |
| abstract_inverted_index.closer | 163 |
| abstract_inverted_index.easily | 190 |
| abstract_inverted_index.events | 9, 212 |
| abstract_inverted_index.factor | 77 |
| abstract_inverted_index.forest | 86 |
| abstract_inverted_index.hybrid | 67 |
| abstract_inverted_index.impact | 18 |
| abstract_inverted_index.memory | 143 |
| abstract_inverted_index.models | 133 |
| abstract_inverted_index.neural | 96, 144 |
| abstract_inverted_index.offers | 186 |
| abstract_inverted_index.random | 85 |
| abstract_inverted_index.trends | 155 |
| abstract_inverted_index.(LSTM). | 146 |
| abstract_inverted_index.Climate | 0 |
| abstract_inverted_index.Taiwan. | 130 |
| abstract_inverted_index.applied | 124 |
| abstract_inverted_index.causing | 20 |
| abstract_inverted_index.complex | 50 |
| abstract_inverted_index.damage. | 22 |
| abstract_inverted_index.dynamic | 76 |
| abstract_inverted_index.extract | 81 |
| abstract_inverted_index.extreme | 7, 210 |
| abstract_inverted_index.farmers | 201 |
| abstract_inverted_index.gridded | 118 |
| abstract_inverted_index.network | 97, 145 |
| abstract_inverted_index.precise | 43 |
| abstract_inverted_index.predict | 100 |
| abstract_inverted_index.results | 148 |
| abstract_inverted_index.storms, | 15 |
| abstract_inverted_index.towards | 223 |
| abstract_inverted_index.various | 52 |
| abstract_inverted_index.weather | 8, 36 |
| abstract_inverted_index.However, | 46 |
| abstract_inverted_index.approach | 194 |
| abstract_inverted_index.captures | 153 |
| abstract_inverted_index.climatic | 211 |
| abstract_inverted_index.compared | 167 |
| abstract_inverted_index.control. | 45 |
| abstract_inverted_index.dioxide. | 113 |
| abstract_inverted_index.economy. | 226 |
| abstract_inverted_index.external | 35 |
| abstract_inverted_index.factors, | 83, 92 |
| abstract_inverted_index.frequent | 6 |
| abstract_inverted_index.identify | 89 |
| abstract_inverted_index.internal | 104 |
| abstract_inverted_index.proposed | 115, 173 |
| abstract_inverted_index.proposes | 64 |
| abstract_inverted_index.protects | 32 |
| abstract_inverted_index.provides | 27 |
| abstract_inverted_index.relative | 106 |
| abstract_inverted_index.requires | 42 |
| abstract_inverted_index.transfer | 54 |
| abstract_inverted_index.utilized | 117 |
| abstract_inverted_index.DF-RF-ANN | 151, 174 |
| abstract_inverted_index.Taichung, | 129 |
| abstract_inverted_index.approach. | 192 |
| abstract_inverted_index.difficult | 58 |
| abstract_inverted_index.droughts, | 13 |
| abstract_inverted_index.generates | 160 |
| abstract_inverted_index.humidity, | 107 |
| abstract_inverted_index.including | 103 |
| abstract_inverted_index.milestone | 178 |
| abstract_inverted_index.properly. | 61 |
| abstract_inverted_index.resources | 208 |
| abstract_inverted_index.Greenhouse | 23 |
| abstract_inverted_index.accessible | 191 |
| abstract_inverted_index.beneficial | 198 |
| abstract_inverted_index.conditions | 37 |
| abstract_inverted_index.configured | 135 |
| abstract_inverted_index.greenhouse | 47, 101, 127 |
| abstract_inverted_index.heatwaves, | 12 |
| abstract_inverted_index.innovative | 66 |
| abstract_inverted_index.manageable | 29 |
| abstract_inverted_index.mechanisms | 55 |
| abstract_inverted_index.radiation, | 110 |
| abstract_inverted_index.seamlessly | 71 |
| abstract_inverted_index.short-term | 142 |
| abstract_inverted_index.transition | 222 |
| abstract_inverted_index.unobserved | 82 |
| abstract_inverted_index.comparative | 132 |
| abstract_inverted_index.components: | 74 |
| abstract_inverted_index.cultivation | 24 |
| abstract_inverted_index.demonstrate | 149 |
| abstract_inverted_index.development | 217 |
| abstract_inverted_index.effectively | 152 |
| abstract_inverted_index.environment | 30 |
| abstract_inverted_index.predictions | 161, 184 |
| abstract_inverted_index.small-scale | 200 |
| abstract_inverted_index.sustainable | 216 |
| abstract_inverted_index.(DF-RF-ANN), | 69 |
| abstract_inverted_index.agriculture, | 19 |
| abstract_inverted_index.contributing | 214 |
| abstract_inverted_index.microclimate | 44, 183 |
| abstract_inverted_index.multi-factor | 182 |
| abstract_inverted_index.observations | 158, 166 |
| abstract_inverted_index.particularly | 197 |
| abstract_inverted_index.temperature, | 105 |
| abstract_inverted_index.microclimate, | 102 |
| abstract_inverted_index.microclimates | 48 |
| abstract_inverted_index.multi-horizon | 180 |
| abstract_inverted_index.significantly | 17 |
| abstract_inverted_index.cost-effective | 188 |
| abstract_inverted_index.meteorological | 119 |
| abstract_inverted_index.backpropagation | 95 |
| abstract_inverted_index.photosynthetically | 108 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 94 |
| corresponding_author_ids | https://openalex.org/A5089700077 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I16733864 |
| citation_normalized_percentile.value | 0.91761829 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |