Winter Wheat Yield Estimation by Fusing CNN–MALSTM Deep Learning with Remote Sensing Indices Article Swipe
Changchun Li
,
Lei Zhang
,
Xifang Wu
,
Huabin Chai
,
Hengmao Xiang
,
Yinghua Jiao
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/agriculture14111961
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/agriculture14111961
A rapid and accurate determination of large-scale winter wheat yield is significant for food security and policy formulation. In this study, meteorological data and enhanced vegetation index (EVI) were used to estimate the winter wheat yield in Henan Province, China, by constructing a deep learning model. The deep learning model combines CNN feature extraction and makes full use of the sequence data processing capability of the LSTM and a multi-head attention mechanism to develop a novel CNN–MALSTM estimation model, which can capture the information of input sequences in different feature subspaces to enhance the expressiveness of the model. A CNN–LSTM baseline model was also constructed for comparison. Compared with the baseline model (R2 = 0.75, RMSE = 646.53 kg/ha, and MAPE = 8.82%), the proposed CNN–MALSTM model (R2 = 0.79, RMSE = 576.01 kg/ha, MAPE = 7.29%) could more accurately estimate the yield. Based on the cross-validation with one year of left-out data and the input of the fertility period by fertility period to explore the sensitivity of the model to data from different fertility periods to the final yield, an annual yield distribution map of Henan Province was constructed. Through cross-validation, the stability of the model in different years was assessed. The results showed that the model could obtain the best prediction of the yield approximately 20 days in advance. In terms of the spatial distribution of the yield in Henan Province on a yearly basis, the estimated yield showed an overall uptrend from west to east, consistent with the trend in the statistical yearbook of the yield for Henan Province. Thus, it can be concluded that the proposed CNN–MALSTM model can provide stable yield estimation results.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/agriculture14111961
- OA Status
- gold
- Cited By
- 3
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403980184
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4403980184Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/agriculture14111961Digital Object Identifier
- Title
-
Winter Wheat Yield Estimation by Fusing CNN–MALSTM Deep Learning with Remote Sensing IndicesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-11-01Full publication date if available
- Authors
-
Changchun Li, Lei Zhang, Xifang Wu, Huabin Chai, Hengmao Xiang, Yinghua JiaoList of authors in order
- Landing page
-
https://doi.org/10.3390/agriculture14111961Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3390/agriculture14111961Direct OA link when available
- Concepts
-
Yield (engineering), Estimation, Artificial intelligence, Winter wheat, Deep learning, Remote sensing, Statistics, Agronomy, Pattern recognition (psychology), Computer science, Environmental science, Mathematics, Biology, Geography, Engineering, Materials science, Systems engineering, MetallurgyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3Per-year citation counts (last 5 years)
- References (count)
-
45Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4403980184 |
|---|---|
| doi | https://doi.org/10.3390/agriculture14111961 |
| ids.doi | https://doi.org/10.3390/agriculture14111961 |
| ids.openalex | https://openalex.org/W4403980184 |
| fwci | 2.63869709 |
| type | article |
| title | Winter Wheat Yield Estimation by Fusing CNN–MALSTM Deep Learning with Remote Sensing Indices |
| biblio.issue | 11 |
| biblio.volume | 14 |
| biblio.last_page | 1961 |
| biblio.first_page | 1961 |
| topics[0].id | https://openalex.org/T10111 |
| topics[0].field.id | https://openalex.org/fields/23 |
| topics[0].field.display_name | Environmental Science |
| topics[0].score | 0.9930999875068665 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2303 |
| topics[0].subfield.display_name | Ecology |
| topics[0].display_name | Remote Sensing in Agriculture |
| topics[1].id | https://openalex.org/T13890 |
| topics[1].field.id | https://openalex.org/fields/19 |
| topics[1].field.display_name | Earth and Planetary Sciences |
| topics[1].score | 0.9696999788284302 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1902 |
| topics[1].subfield.display_name | Atmospheric Science |
| topics[1].display_name | Remote Sensing and Land Use |
| topics[2].id | https://openalex.org/T10616 |
| topics[2].field.id | https://openalex.org/fields/11 |
| topics[2].field.display_name | Agricultural and Biological Sciences |
| topics[2].score | 0.9369000196456909 |
| topics[2].domain.id | https://openalex.org/domains/1 |
| topics[2].domain.display_name | Life Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1110 |
| topics[2].subfield.display_name | Plant Science |
| topics[2].display_name | Smart Agriculture and AI |
| is_xpac | False |
| apc_list.value | 1800 |
| apc_list.currency | CHF |
| apc_list.value_usd | 1949 |
| apc_paid.value | 1800 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 1949 |
| concepts[0].id | https://openalex.org/C134121241 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6686279773712158 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q899301 |
| concepts[0].display_name | Yield (engineering) |
| concepts[1].id | https://openalex.org/C96250715 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6007658243179321 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q965330 |
| concepts[1].display_name | Estimation |
| concepts[2].id | https://openalex.org/C154945302 |
| concepts[2].level | 1 |
| concepts[2].score | 0.48334214091300964 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[2].display_name | Artificial intelligence |
| concepts[3].id | https://openalex.org/C3018661444 |
| concepts[3].level | 2 |
| concepts[3].score | 0.42024242877960205 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q6977574 |
| concepts[3].display_name | Winter wheat |
| concepts[4].id | https://openalex.org/C108583219 |
| concepts[4].level | 2 |
| concepts[4].score | 0.4139252305030823 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[4].display_name | Deep learning |
| concepts[5].id | https://openalex.org/C62649853 |
| concepts[5].level | 1 |
| concepts[5].score | 0.40426188707351685 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q199687 |
| concepts[5].display_name | Remote sensing |
| concepts[6].id | https://openalex.org/C105795698 |
| concepts[6].level | 1 |
| concepts[6].score | 0.36305832862854004 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[6].display_name | Statistics |
| concepts[7].id | https://openalex.org/C6557445 |
| concepts[7].level | 1 |
| concepts[7].score | 0.36075887084007263 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q173113 |
| concepts[7].display_name | Agronomy |
| concepts[8].id | https://openalex.org/C153180895 |
| concepts[8].level | 2 |
| concepts[8].score | 0.35143908858299255 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[8].display_name | Pattern recognition (psychology) |
| concepts[9].id | https://openalex.org/C41008148 |
| concepts[9].level | 0 |
| concepts[9].score | 0.346911758184433 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[9].display_name | Computer science |
| concepts[10].id | https://openalex.org/C39432304 |
| concepts[10].level | 0 |
| concepts[10].score | 0.34334689378738403 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q188847 |
| concepts[10].display_name | Environmental science |
| concepts[11].id | https://openalex.org/C33923547 |
| concepts[11].level | 0 |
| concepts[11].score | 0.3166927993297577 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[11].display_name | Mathematics |
| concepts[12].id | https://openalex.org/C86803240 |
| concepts[12].level | 0 |
| concepts[12].score | 0.2409641444683075 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[12].display_name | Biology |
| concepts[13].id | https://openalex.org/C205649164 |
| concepts[13].level | 0 |
| concepts[13].score | 0.21228018403053284 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[13].display_name | Geography |
| concepts[14].id | https://openalex.org/C127413603 |
| concepts[14].level | 0 |
| concepts[14].score | 0.1854274868965149 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[14].display_name | Engineering |
| concepts[15].id | https://openalex.org/C192562407 |
| concepts[15].level | 0 |
| concepts[15].score | 0.05159613490104675 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q228736 |
| concepts[15].display_name | Materials science |
| concepts[16].id | https://openalex.org/C201995342 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q682496 |
| concepts[16].display_name | Systems engineering |
| concepts[17].id | https://openalex.org/C191897082 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q11467 |
| concepts[17].display_name | Metallurgy |
| keywords[0].id | https://openalex.org/keywords/yield |
| keywords[0].score | 0.6686279773712158 |
| keywords[0].display_name | Yield (engineering) |
| keywords[1].id | https://openalex.org/keywords/estimation |
| keywords[1].score | 0.6007658243179321 |
| keywords[1].display_name | Estimation |
| keywords[2].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[2].score | 0.48334214091300964 |
| keywords[2].display_name | Artificial intelligence |
| keywords[3].id | https://openalex.org/keywords/winter-wheat |
| keywords[3].score | 0.42024242877960205 |
| keywords[3].display_name | Winter wheat |
| keywords[4].id | https://openalex.org/keywords/deep-learning |
| keywords[4].score | 0.4139252305030823 |
| keywords[4].display_name | Deep learning |
| keywords[5].id | https://openalex.org/keywords/remote-sensing |
| keywords[5].score | 0.40426188707351685 |
| keywords[5].display_name | Remote sensing |
| keywords[6].id | https://openalex.org/keywords/statistics |
| keywords[6].score | 0.36305832862854004 |
| keywords[6].display_name | Statistics |
| keywords[7].id | https://openalex.org/keywords/agronomy |
| keywords[7].score | 0.36075887084007263 |
| keywords[7].display_name | Agronomy |
| keywords[8].id | https://openalex.org/keywords/pattern-recognition |
| keywords[8].score | 0.35143908858299255 |
| keywords[8].display_name | Pattern recognition (psychology) |
| keywords[9].id | https://openalex.org/keywords/computer-science |
| keywords[9].score | 0.346911758184433 |
| keywords[9].display_name | Computer science |
| keywords[10].id | https://openalex.org/keywords/environmental-science |
| keywords[10].score | 0.34334689378738403 |
| keywords[10].display_name | Environmental science |
| keywords[11].id | https://openalex.org/keywords/mathematics |
| keywords[11].score | 0.3166927993297577 |
| keywords[11].display_name | Mathematics |
| keywords[12].id | https://openalex.org/keywords/biology |
| keywords[12].score | 0.2409641444683075 |
| keywords[12].display_name | Biology |
| keywords[13].id | https://openalex.org/keywords/geography |
| keywords[13].score | 0.21228018403053284 |
| keywords[13].display_name | Geography |
| keywords[14].id | https://openalex.org/keywords/engineering |
| keywords[14].score | 0.1854274868965149 |
| keywords[14].display_name | Engineering |
| keywords[15].id | https://openalex.org/keywords/materials-science |
| keywords[15].score | 0.05159613490104675 |
| keywords[15].display_name | Materials science |
| language | en |
| locations[0].id | doi:10.3390/agriculture14111961 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210202585 |
| locations[0].source.issn | 2077-0472 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2077-0472 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Agriculture |
| 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 | |
| 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 | Agriculture |
| locations[0].landing_page_url | https://doi.org/10.3390/agriculture14111961 |
| locations[1].id | pmh:oai:doaj.org/article:7b3d299ee00e4d30a1fee200209c96fd |
| 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 | Agriculture, Vol 14, Iss 11, p 1961 (2024) |
| locations[1].landing_page_url | https://doaj.org/article/7b3d299ee00e4d30a1fee200209c96fd |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5101700399 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-7552-0196 |
| authorships[0].author.display_name | Changchun Li |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I4210166499 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China |
| authorships[0].institutions[0].id | https://openalex.org/I4210166499 |
| authorships[0].institutions[0].ror | https://ror.org/05vr1c885 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I4210166499 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Henan Polytechnic University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Changchun Li |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China |
| authorships[1].author.id | https://openalex.org/A5100433940 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-6385-5773 |
| authorships[1].author.display_name | Lei Zhang |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I4210166499 |
| authorships[1].affiliations[0].raw_affiliation_string | School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China |
| authorships[1].institutions[0].id | https://openalex.org/I4210166499 |
| authorships[1].institutions[0].ror | https://ror.org/05vr1c885 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I4210166499 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Henan Polytechnic University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Lei Zhang |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China |
| authorships[2].author.id | https://openalex.org/A5101056140 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Xifang Wu |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I4210166499 |
| authorships[2].affiliations[0].raw_affiliation_string | School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China |
| authorships[2].institutions[0].id | https://openalex.org/I4210166499 |
| authorships[2].institutions[0].ror | https://ror.org/05vr1c885 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I4210166499 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Henan Polytechnic University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Xifang Wu |
| authorships[2].is_corresponding | True |
| authorships[2].raw_affiliation_strings | School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China |
| authorships[3].author.id | https://openalex.org/A5102213576 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Huabin Chai |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I4210166499 |
| authorships[3].affiliations[0].raw_affiliation_string | School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China |
| authorships[3].institutions[0].id | https://openalex.org/I4210166499 |
| authorships[3].institutions[0].ror | https://ror.org/05vr1c885 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I4210166499 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Henan Polytechnic University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Huabin Chai |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China |
| authorships[4].author.id | https://openalex.org/A5110098276 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Hengmao Xiang |
| authorships[4].affiliations[0].raw_affiliation_string | Shandong Provincial Institute of Land Surveying and Mapping, Jinan 250102, China |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Hengmao Xiang |
| authorships[4].is_corresponding | True |
| authorships[4].raw_affiliation_strings | Shandong Provincial Institute of Land Surveying and Mapping, Jinan 250102, China |
| authorships[5].author.id | https://openalex.org/A5106952512 |
| authorships[5].author.orcid | |
| authorships[5].author.display_name | Yinghua Jiao |
| authorships[5].affiliations[0].raw_affiliation_string | Shandong Provincial Institute of Land Surveying and Mapping, Jinan 250102, China |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Yinghua Jiao |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Shandong Provincial Institute of Land Surveying and Mapping, Jinan 250102, China |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.3390/agriculture14111961 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Winter Wheat Yield Estimation by Fusing CNN–MALSTM Deep Learning with Remote Sensing Indices |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10111 |
| primary_topic.field.id | https://openalex.org/fields/23 |
| primary_topic.field.display_name | Environmental Science |
| primary_topic.score | 0.9930999875068665 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2303 |
| primary_topic.subfield.display_name | Ecology |
| primary_topic.display_name | Remote Sensing in Agriculture |
| related_works | https://openalex.org/W842789846, https://openalex.org/W2031193684, https://openalex.org/W4375867731, https://openalex.org/W4225871616, https://openalex.org/W4246691996, https://openalex.org/W2611989081, https://openalex.org/W816774663, https://openalex.org/W2065300082, https://openalex.org/W2386800167, https://openalex.org/W2731899572 |
| cited_by_count | 3 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 3 |
| locations_count | 2 |
| best_oa_location.id | doi:10.3390/agriculture14111961 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210202585 |
| best_oa_location.source.issn | 2077-0472 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2077-0472 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Agriculture |
| 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 | |
| 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 | Agriculture |
| best_oa_location.landing_page_url | https://doi.org/10.3390/agriculture14111961 |
| primary_location.id | doi:10.3390/agriculture14111961 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210202585 |
| primary_location.source.issn | 2077-0472 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2077-0472 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Agriculture |
| 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 | |
| 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 | Agriculture |
| primary_location.landing_page_url | https://doi.org/10.3390/agriculture14111961 |
| publication_date | 2024-11-01 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W1969368146, https://openalex.org/W2520448605, https://openalex.org/W2134281019, https://openalex.org/W2944794516, https://openalex.org/W6837861438, https://openalex.org/W2808964638, https://openalex.org/W2946844666, https://openalex.org/W3211300303, https://openalex.org/W4391508342, https://openalex.org/W2010806274, https://openalex.org/W4281686257, https://openalex.org/W6775875391, https://openalex.org/W3132981178, https://openalex.org/W2097314809, https://openalex.org/W2893301845, https://openalex.org/W2805142011, https://openalex.org/W3210954228, https://openalex.org/W3049266285, https://openalex.org/W1986802129, https://openalex.org/W2742109465, https://openalex.org/W6715652707, https://openalex.org/W2997816471, https://openalex.org/W6796155543, https://openalex.org/W1986072339, https://openalex.org/W2919115771, https://openalex.org/W2076462394, https://openalex.org/W2790979755, https://openalex.org/W2805798665, https://openalex.org/W2500751094, https://openalex.org/W639708223, https://openalex.org/W4389978653, https://openalex.org/W4320890277, https://openalex.org/W3081407997, https://openalex.org/W3015657740, https://openalex.org/W2018636632, https://openalex.org/W4205970347, https://openalex.org/W3124828689, https://openalex.org/W4399314066, https://openalex.org/W3137327109, https://openalex.org/W2990480734, https://openalex.org/W2730602593, https://openalex.org/W2414117070, https://openalex.org/W3170338608, https://openalex.org/W3017707823, https://openalex.org/W4281381779 |
| referenced_works_count | 45 |
| abstract_inverted_index.= | 113, 116, 121, 128, 131, 135 |
| abstract_inverted_index.A | 0, 98 |
| abstract_inverted_index.a | 42, 68, 74, 234 |
| abstract_inverted_index.20 | 217 |
| abstract_inverted_index.In | 18, 221 |
| abstract_inverted_index.an | 180, 241 |
| abstract_inverted_index.be | 265 |
| abstract_inverted_index.by | 40, 160 |
| abstract_inverted_index.in | 36, 87, 197, 219, 230, 252 |
| abstract_inverted_index.is | 10 |
| abstract_inverted_index.it | 263 |
| abstract_inverted_index.of | 5, 58, 64, 84, 95, 150, 156, 167, 185, 194, 213, 223, 227, 256 |
| abstract_inverted_index.on | 144, 233 |
| abstract_inverted_index.to | 30, 72, 91, 163, 170, 176, 246 |
| abstract_inverted_index.(R2 | 112, 127 |
| abstract_inverted_index.CNN | 51 |
| abstract_inverted_index.The | 46, 202 |
| abstract_inverted_index.and | 2, 15, 23, 54, 67, 119, 153 |
| abstract_inverted_index.can | 80, 264, 272 |
| abstract_inverted_index.for | 12, 105, 259 |
| abstract_inverted_index.map | 184 |
| abstract_inverted_index.one | 148 |
| abstract_inverted_index.the | 32, 59, 65, 82, 93, 96, 109, 123, 141, 145, 154, 157, 165, 168, 177, 192, 195, 206, 210, 214, 224, 228, 237, 250, 253, 257, 268 |
| abstract_inverted_index.use | 57 |
| abstract_inverted_index.was | 102, 188, 200 |
| abstract_inverted_index.LSTM | 66 |
| abstract_inverted_index.MAPE | 120, 134 |
| abstract_inverted_index.RMSE | 115, 130 |
| abstract_inverted_index.also | 103 |
| abstract_inverted_index.best | 211 |
| abstract_inverted_index.data | 22, 61, 152, 171 |
| abstract_inverted_index.days | 218 |
| abstract_inverted_index.deep | 43, 47 |
| abstract_inverted_index.food | 13 |
| abstract_inverted_index.from | 172, 244 |
| abstract_inverted_index.full | 56 |
| abstract_inverted_index.more | 138 |
| abstract_inverted_index.that | 205, 267 |
| abstract_inverted_index.this | 19 |
| abstract_inverted_index.used | 29 |
| abstract_inverted_index.were | 28 |
| abstract_inverted_index.west | 245 |
| abstract_inverted_index.with | 108, 147, 249 |
| abstract_inverted_index.year | 149 |
| abstract_inverted_index.(EVI) | 27 |
| abstract_inverted_index.0.75, | 114 |
| abstract_inverted_index.0.79, | 129 |
| abstract_inverted_index.Based | 143 |
| abstract_inverted_index.Henan | 37, 186, 231, 260 |
| abstract_inverted_index.Thus, | 262 |
| abstract_inverted_index.could | 137, 208 |
| abstract_inverted_index.east, | 247 |
| abstract_inverted_index.final | 178 |
| abstract_inverted_index.index | 26 |
| abstract_inverted_index.input | 85, 155 |
| abstract_inverted_index.makes | 55 |
| abstract_inverted_index.model | 49, 101, 111, 126, 169, 196, 207, 271 |
| abstract_inverted_index.novel | 75 |
| abstract_inverted_index.rapid | 1 |
| abstract_inverted_index.terms | 222 |
| abstract_inverted_index.trend | 251 |
| abstract_inverted_index.wheat | 8, 34 |
| abstract_inverted_index.which | 79 |
| abstract_inverted_index.years | 199 |
| abstract_inverted_index.yield | 9, 35, 182, 215, 229, 239, 258, 275 |
| abstract_inverted_index.576.01 | 132 |
| abstract_inverted_index.646.53 | 117 |
| abstract_inverted_index.7.29%) | 136 |
| abstract_inverted_index.China, | 39 |
| abstract_inverted_index.annual | 181 |
| abstract_inverted_index.basis, | 236 |
| abstract_inverted_index.kg/ha, | 118, 133 |
| abstract_inverted_index.model, | 78 |
| abstract_inverted_index.model. | 45, 97 |
| abstract_inverted_index.obtain | 209 |
| abstract_inverted_index.period | 159, 162 |
| abstract_inverted_index.policy | 16 |
| abstract_inverted_index.showed | 204, 240 |
| abstract_inverted_index.stable | 274 |
| abstract_inverted_index.study, | 20 |
| abstract_inverted_index.winter | 7, 33 |
| abstract_inverted_index.yearly | 235 |
| abstract_inverted_index.yield, | 179 |
| abstract_inverted_index.yield. | 142 |
| abstract_inverted_index.8.82%), | 122 |
| abstract_inverted_index.Through | 190 |
| abstract_inverted_index.capture | 81 |
| abstract_inverted_index.develop | 73 |
| abstract_inverted_index.enhance | 92 |
| abstract_inverted_index.explore | 164 |
| abstract_inverted_index.feature | 52, 89 |
| abstract_inverted_index.overall | 242 |
| abstract_inverted_index.periods | 175 |
| abstract_inverted_index.provide | 273 |
| abstract_inverted_index.results | 203 |
| abstract_inverted_index.spatial | 225 |
| abstract_inverted_index.uptrend | 243 |
| abstract_inverted_index.Compared | 107 |
| abstract_inverted_index.Province | 187, 232 |
| abstract_inverted_index.accurate | 3 |
| abstract_inverted_index.advance. | 220 |
| abstract_inverted_index.baseline | 100, 110 |
| abstract_inverted_index.combines | 50 |
| abstract_inverted_index.enhanced | 24 |
| abstract_inverted_index.estimate | 31, 140 |
| abstract_inverted_index.learning | 44, 48 |
| abstract_inverted_index.left-out | 151 |
| abstract_inverted_index.proposed | 124, 269 |
| abstract_inverted_index.results. | 277 |
| abstract_inverted_index.security | 14 |
| abstract_inverted_index.sequence | 60 |
| abstract_inverted_index.yearbook | 255 |
| abstract_inverted_index.Province, | 38 |
| abstract_inverted_index.Province. | 261 |
| abstract_inverted_index.assessed. | 201 |
| abstract_inverted_index.attention | 70 |
| abstract_inverted_index.concluded | 266 |
| abstract_inverted_index.different | 88, 173, 198 |
| abstract_inverted_index.estimated | 238 |
| abstract_inverted_index.fertility | 158, 161, 174 |
| abstract_inverted_index.mechanism | 71 |
| abstract_inverted_index.sequences | 86 |
| abstract_inverted_index.stability | 193 |
| abstract_inverted_index.subspaces | 90 |
| abstract_inverted_index.CNN–LSTM | 99 |
| abstract_inverted_index.accurately | 139 |
| abstract_inverted_index.capability | 63 |
| abstract_inverted_index.consistent | 248 |
| abstract_inverted_index.estimation | 77, 276 |
| abstract_inverted_index.extraction | 53 |
| abstract_inverted_index.multi-head | 69 |
| abstract_inverted_index.prediction | 212 |
| abstract_inverted_index.processing | 62 |
| abstract_inverted_index.vegetation | 25 |
| abstract_inverted_index.comparison. | 106 |
| abstract_inverted_index.constructed | 104 |
| abstract_inverted_index.information | 83 |
| abstract_inverted_index.large-scale | 6 |
| abstract_inverted_index.sensitivity | 166 |
| abstract_inverted_index.significant | 11 |
| abstract_inverted_index.statistical | 254 |
| abstract_inverted_index.CNN–MALSTM | 76, 125, 270 |
| abstract_inverted_index.constructed. | 189 |
| abstract_inverted_index.constructing | 41 |
| abstract_inverted_index.distribution | 183, 226 |
| abstract_inverted_index.formulation. | 17 |
| abstract_inverted_index.approximately | 216 |
| abstract_inverted_index.determination | 4 |
| abstract_inverted_index.expressiveness | 94 |
| abstract_inverted_index.meteorological | 21 |
| abstract_inverted_index.cross-validation | 146 |
| abstract_inverted_index.cross-validation, | 191 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 96 |
| corresponding_author_ids | https://openalex.org/A5101056140, https://openalex.org/A5110098276 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I4210166499 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/2 |
| sustainable_development_goals[0].score | 0.7200000286102295 |
| sustainable_development_goals[0].display_name | Zero hunger |
| citation_normalized_percentile.value | 0.85803321 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |