Increasing the Accuracy of Soil Nutrient Prediction by Improving Genetic Algorithm Backpropagation Neural Networks
Soil nutrient prediction has been eliciting increasing attention in agricultural production. Backpropagation (BP) neural networks have demonstrated remarkable ability in many prediction scenarios. However, directly utilizing BP neural networks in soil nutrient prediction may not yield promising resu...
Uložené v:
| Vydané v: | Symmetry (Basel) Ročník 15; číslo 1; s. 151 |
|---|---|
| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Basel
MDPI AG
01.01.2023
|
| Predmet: | |
| ISSN: | 2073-8994, 2073-8994 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Soil nutrient prediction has been eliciting increasing attention in agricultural production. Backpropagation (BP) neural networks have demonstrated remarkable ability in many prediction scenarios. However, directly utilizing BP neural networks in soil nutrient prediction may not yield promising results due to the random assignment of initial weights and thresholds and the tendency to fall into local extreme points. In this study, a BP neural network model optimized by an improved genetic algorithm (IGA) was proposed to predict soil nutrient time series with high accuracy. First, the crossover and mutation operations of the genetic algorithm (GA) were improved. Next, the IGA was used to optimize the BP model. The symmetric nature of the model lies in its feedforward and feedback connections, i.e., the same weights must be used for the forward and backward passes. An empirical evaluation was performed using annual soil nutrient data from China. Soil pH, total nitrogen, organic matter, fast-acting potassium, and effective phosphorus were selected as evaluation indicators. The prediction results of the IGA–BP, GA–BP, and BP neural network models were compared and analyzed. For the IGA–BP prediction model, the coefficient of determination for soil pH was 0.8, while those for total nitrogen, organic matter, fast-acting potassium, and effective phosphorus were all greater than 0.98, exhibiting a strong generalization ability. The root-mean-square errors of the IGA–BP prediction models were reduced to 50% of the BP models. The results indicated that the IGA–BP method can accurately predict soil nutrient content for future time series. |
|---|---|
| AbstractList | Soil nutrient prediction has been eliciting increasing attention in agricultural production. Backpropagation (BP) neural networks have demonstrated remarkable ability in many prediction scenarios. However, directly utilizing BP neural networks in soil nutrient prediction may not yield promising results due to the random assignment of initial weights and thresholds and the tendency to fall into local extreme points. In this study, a BP neural network model optimized by an improved genetic algorithm (IGA) was proposed to predict soil nutrient time series with high accuracy. First, the crossover and mutation operations of the genetic algorithm (GA) were improved. Next, the IGA was used to optimize the BP model. The symmetric nature of the model lies in its feedforward and feedback connections, i.e., the same weights must be used for the forward and backward passes. An empirical evaluation was performed using annual soil nutrient data from China. Soil pH, total nitrogen, organic matter, fast-acting potassium, and effective phosphorus were selected as evaluation indicators. The prediction results of the IGA-BP, GA-BP, and BP neural network models were compared and analyzed. For the IGA-BP prediction model, the coefficient of determination for soil pH was 0.8, while those for total nitrogen, organic matter, fast-acting potassium, and effective phosphorus were all greater than 0.98, exhibiting a strong generalization ability. The root-mean-square errors of the IGA-BP prediction models were reduced to 50% of the BP models. The results indicated that the IGA-BP method can accurately predict soil nutrient content for future time series. |
| Audience | Academic |
| Author | Jiang, Cuiqing Wang, Zhao Che, Wanliu Liu, Yanqing Lu, Cuiping |
| Author_xml | – sequence: 1 givenname: Yanqing surname: Liu fullname: Liu, Yanqing – sequence: 2 givenname: Cuiqing surname: Jiang fullname: Jiang, Cuiqing – sequence: 3 givenname: Cuiping surname: Lu fullname: Lu, Cuiping – sequence: 4 givenname: Zhao surname: Wang fullname: Wang, Zhao – sequence: 5 givenname: Wanliu surname: Che fullname: Che, Wanliu |
| BookMark | eNptkUtPAyEUhYnRxKpd-QdIXJpRGIZ5LGvjo0lTTdT1hGEuFTsDFRhN_73UumiMsIDA-Q73cE_QobEGEDqn5Iqxilz7TU85oYRyeoBGKSlYUlZVdri3P0Zj799JHJzwLCcj5GZGOhBemyUOb4AnUg5OyA22Cj9b3eHFEJwGE_CTg1bLoK3BzQbP-rWzn1vqHgwELfGkW1qnw1uPb4Rcxdu1WIof-QKiZXSC8GXdyp-hIyU6D-Pf9RS93t2-TB-S-eP9bDqZJ5KxIiRN3hQijUW2jFPWZnnDGLRNyRuVgySkpFQKXkmhlMgYxNxVlVOu2ibPGVOCnaKLnW-s5WMAH-p3OzgTn6zTIi_SsiKERtXVTrUUHdTaKBti_jhb6LWMP6x0PJ8UPKVZWbAiAnQHSGe9d6BqqcNP0Ajqrqak3raj3mtHZC7_MGune-E2_6q_AargjpI |
| CitedBy_id | crossref_primary_10_3390_buildings14030641 crossref_primary_10_1016_j_chemolab_2024_105289 crossref_primary_10_1016_j_jrmge_2025_02_027 crossref_primary_10_1016_j_procs_2025_07_184 crossref_primary_10_1002_jpln_202300310 crossref_primary_10_3390_su16198598 crossref_primary_10_1002_fes3_477 crossref_primary_10_1108_IJSI_07_2024_0104 crossref_primary_10_1109_ACCESS_2025_3547255 crossref_primary_10_3233_IDT_240423 crossref_primary_10_3390_sym15071397 crossref_primary_10_2118_223123_PA crossref_primary_10_1002_ael2_20134 crossref_primary_10_32604_csse_2023_038912 crossref_primary_10_1109_ACCESS_2024_3452710 crossref_primary_10_3390_math11102274 |
| Cites_doi | 10.1016/j.jterra.2013.04.002 10.3390/app11115235 10.3390/s18103408 10.1016/j.compag.2018.05.012 10.3390/rs12020215 10.1007/s00521-016-2707-8 10.1142/S0218126619500038 10.1016/j.energy.2017.03.094 10.1016/j.biosystemseng.2006.05.011 10.1016/j.compag.2020.105860 10.3390/s18082674 10.1007/s10705-017-9870-x 10.1016/j.compag.2008.05.021 10.1016/j.compeleceng.2017.02.016 10.1016/j.chaos.2015.05.032 10.1166/jctn.2015.4188 |
| ContentType | Journal Article |
| Copyright | COPYRIGHT 2023 MDPI AG 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: COPYRIGHT 2023 MDPI AG – notice: 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | AAYXX CITATION 7SC 7SR 7U5 8BQ 8FD 8FE 8FG ABJCF ABUWG AFKRA AZQEC BENPR BGLVJ CCPQU DWQXO H8D HCIFZ JG9 JQ2 L6V L7M L~C L~D M7S PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PTHSS |
| DOI | 10.3390/sym15010151 |
| DatabaseName | CrossRef Computer and Information Systems Abstracts Engineered Materials Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central (Alumni Edition) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central Technology Collection ProQuest One Community College ProQuest Central Korea Aerospace Database SciTech Premium Collection Materials Research Database ProQuest Computer Science Collection ProQuest Engineering Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Engineering Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection |
| DatabaseTitle | CrossRef Publicly Available Content Database Materials Research Database Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences Aerospace Database Engineered Materials Abstracts ProQuest Engineering Collection ProQuest Central Korea ProQuest Central (New) Advanced Technologies Database with Aerospace Engineering Collection Engineering Database ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest SciTech Collection METADEX Computer and Information Systems Abstracts Professional ProQuest One Academic UKI Edition Materials Science & Engineering Collection Solid State and Superconductivity Abstracts ProQuest One Academic ProQuest One Academic (New) |
| DatabaseTitleList | CrossRef Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Sciences (General) |
| EISSN | 2073-8994 |
| ExternalDocumentID | A752148737 10_3390_sym15010151 |
| GeographicLocations | China Anhui China |
| GeographicLocations_xml | – name: China – name: Anhui China |
| GroupedDBID | 5VS 8FE 8FG AADQD AAYXX ABDBF ABJCF ACUHS ADBBV ADMLS AFFHD AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS AMVHM BCNDV BENPR BGLVJ CCPQU CITATION E3Z ESX GX1 HCIFZ IAO ITC J9A KQ8 L6V M7S MODMG M~E OK1 PHGZM PHGZT PIMPY PQGLB PROAC PTHSS TR2 TUS 7SC 7SR 7U5 8BQ 8FD ABUWG AZQEC DWQXO H8D JG9 JQ2 L7M L~C L~D PKEHL PQEST PQQKQ PQUKI PRINS |
| ID | FETCH-LOGICAL-c337t-b6b7a2460d3513d46b33edb85bf6ec00811ca59caffa43e10199615fdb6633fa3 |
| IEDL.DBID | BENPR |
| ISICitedReferencesCount | 17 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000918899400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2073-8994 |
| IngestDate | Fri Jul 25 11:51:27 EDT 2025 Tue Nov 04 17:47:39 EST 2025 Sat Nov 29 07:09:59 EST 2025 Tue Nov 18 21:51:05 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c337t-b6b7a2460d3513d46b33edb85bf6ec00811ca59caffa43e10199615fdb6633fa3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| OpenAccessLink | https://www.proquest.com/docview/2767289001?pq-origsite=%requestingapplication% |
| PQID | 2767289001 |
| PQPubID | 2032326 |
| ParticipantIDs | proquest_journals_2767289001 gale_infotracacademiconefile_A752148737 crossref_citationtrail_10_3390_sym15010151 crossref_primary_10_3390_sym15010151 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-01-01 |
| PublicationDateYYYYMMDD | 2023-01-01 |
| PublicationDate_xml | – month: 01 year: 2023 text: 2023-01-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Basel |
| PublicationPlace_xml | – name: Basel |
| PublicationTitle | Symmetry (Basel) |
| PublicationYear | 2023 |
| Publisher | MDPI AG |
| Publisher_xml | – name: MDPI AG |
| References | Wang (ref_4) 2020; 43 Meng (ref_25) 2020; 89 ref_11 Tian (ref_1) 2016; 5 Ning (ref_23) 2018; 54 Hengl (ref_9) 2017; 109 Fang (ref_2) 2018; 11 Zhang (ref_26) 2016; 10 Zheng (ref_22) 2015; 12 ref_16 Shi (ref_7) 2021; 180 Chlingaryan (ref_5) 2018; 151 Beyki (ref_21) 2015; 77 Liu (ref_18) 2011; 5 Li (ref_12) 2017; 28 Dong (ref_24) 2010; 47 Jian (ref_28) 2017; 60 Li (ref_17) 2017; 48 Jahn (ref_3) 2006; 94 Kim (ref_14) 2008; 64 Saeedi (ref_10) 2019; 28 Li (ref_19) 2014; 134 Cross (ref_15) 2013; 50 Shcn (ref_20) 2015; 37 ref_8 Zhang (ref_27) 2021; 32 ref_6 Zeng (ref_13) 2017; 127 |
| References_xml | – volume: 50 start-page: 165 year: 2013 ident: ref_15 article-title: Estimating terrain parameters for a rigid wheeled rover using neural networks publication-title: J. Terrach doi: 10.1016/j.jterra.2013.04.002 – volume: 32 start-page: 1423 year: 2021 ident: ref_27 article-title: Binocular camera calibration using improved genetic algorithm to optimize neural network publication-title: China Mech. Eng. – ident: ref_11 doi: 10.3390/app11115235 – ident: ref_16 doi: 10.3390/s18103408 – volume: 151 start-page: 61 year: 2018 ident: ref_5 article-title: Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2018.05.012 – ident: ref_8 doi: 10.3390/rs12020215 – volume: 28 start-page: 613 year: 2017 ident: ref_12 article-title: Rule-based back propagation neural networks for various precision rough set presented KANSEI knowledge prediction: A case study on shoe product form features extraction publication-title: Neural Comput. Appl. doi: 10.1007/s00521-016-2707-8 – volume: 28 start-page: 1950003 year: 2019 ident: ref_10 article-title: Feed-forward back-propagation neural networks in side-channel information characterization publication-title: J. Circuits Syst. Comput. doi: 10.1142/S0218126619500038 – volume: 5 start-page: 12 year: 2011 ident: ref_18 article-title: Fuzzy comprehensive fertility evaluation based on BP artificial network publication-title: J. Chin. Soil Fertil. – volume: 48 start-page: 292 year: 2017 ident: ref_17 article-title: Research on soil moisture forecast model based on BP neural network-a case study at feidong county publication-title: Chin. J. Soil Sci. – volume: 10 start-page: 1566 year: 2016 ident: ref_26 article-title: Water quality prediction method based on IGA-BP publication-title: Chin. J. Environ. Eng. – volume: 127 start-page: 381 year: 2017 ident: ref_13 article-title: Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network publication-title: Energy doi: 10.1016/j.energy.2017.03.094 – volume: 94 start-page: 505 year: 2006 ident: ref_3 article-title: Mid-infrared spectroscopic determination of soil nitrate content publication-title: J. Biosyst. Eng. doi: 10.1016/j.biosystemseng.2006.05.011 – volume: 89 start-page: 22 year: 2020 ident: ref_25 article-title: Identification of the shear parameters for lunar regolith based on a GA-BP neural network publication-title: J. Terramechanics – volume: 180 start-page: 105860 year: 2021 ident: ref_7 article-title: Rice nitrogen nutrition estimation with RGB images and machine learning methods publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2020.105860 – ident: ref_6 doi: 10.3390/s18082674 – volume: 134 start-page: 111 year: 2014 ident: ref_19 article-title: An SA–GA–BP neural network-based color correction algorithm for TCM tongue images publication-title: Neuro Comput. – volume: 37 start-page: 36 year: 2015 ident: ref_20 article-title: Forecast of amount of farmland irrigation based on BP neutral network publication-title: J. Agric. Mech. Res. – volume: 109 start-page: 77 year: 2017 ident: ref_9 article-title: Soil nutrient maps of sub-saharan africa: Assessment of soil nutrient content at 250 m spatial resolution using machine learning publication-title: Nutr. Cycl. Agroecosyst. doi: 10.1007/s10705-017-9870-x – volume: 64 start-page: 268 year: 2008 ident: ref_14 article-title: Artificial neural network estimation of soil erosion and nutrient concentrations in runoff from land application areas publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2008.05.021 – volume: 60 start-page: 58 year: 2017 ident: ref_28 article-title: An improved back propagation neural network prediction model for subsurface drip irrigation system publication-title: Comput. Electr. Eng. doi: 10.1016/j.compeleceng.2017.02.016 – volume: 5 start-page: 110 year: 2016 ident: ref_1 article-title: Improving soil fertility and building sustainable agriculture publication-title: Tu Rang Fei Liao – volume: 43 start-page: 4 year: 2020 ident: ref_4 article-title: Design of soil nutrient content prediction model based on big data statistics publication-title: Mod. Electron. Tech. – volume: 54 start-page: 54 year: 2018 ident: ref_23 article-title: Research of adaptive genetic neural network algorithm in soil moisture prediction publication-title: Comput. Eng. Appl. – volume: 11 start-page: 28 year: 2018 ident: ref_2 article-title: Precision agriculture: Development benefits, international experience and China’s practice publication-title: Agric. Econ. – volume: 47 start-page: 42 year: 2010 ident: ref_24 article-title: Study on soil available zinc with GA-RBF neural network based spatial interpolation method publication-title: Acta Pedol. Sin. – volume: 77 start-page: 247 year: 2015 ident: ref_21 article-title: Chaotic logic gate: A new approach in set and design by genetic algorithm publication-title: Chaos Solitons Fractals doi: 10.1016/j.chaos.2015.05.032 – volume: 12 start-page: 2846 year: 2015 ident: ref_22 article-title: Fault diagnosis research for servo valve based on GA-BP neural network publication-title: J. Comput. Theor. Nanosci. doi: 10.1166/jctn.2015.4188 |
| SSID | ssj0000505460 |
| Score | 2.3479314 |
| Snippet | Soil nutrient prediction has been eliciting increasing attention in agricultural production. Backpropagation (BP) neural networks have demonstrated remarkable... |
| SourceID | proquest gale crossref |
| SourceType | Aggregation Database Enrichment Source Index Database |
| StartPage | 151 |
| SubjectTerms | Accuracy Agricultural production Algorithms Analysis Back propagation Back propagation networks Corn Crops Empirical analysis Genetic algorithms Irrigation Machine learning Moisture content Neural networks Nitrogen Nutrients Organic matter Phosphorus Potassium Prediction models Soil acidity Soil chemistry Soils Time series Water quality |
| Title | Increasing the Accuracy of Soil Nutrient Prediction by Improving Genetic Algorithm Backpropagation Neural Networks |
| URI | https://www.proquest.com/docview/2767289001 |
| Volume | 15 |
| WOSCitedRecordID | wos000918899400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2073-8994 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000505460 issn: 2073-8994 databaseCode: M~E dateStart: 20080101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 2073-8994 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000505460 issn: 2073-8994 databaseCode: M7S dateStart: 20090301 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2073-8994 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000505460 issn: 2073-8994 databaseCode: BENPR dateStart: 20090301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2073-8994 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000505460 issn: 2073-8994 databaseCode: PIMPY dateStart: 20090301 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LT-MwEB5B2cNeeC2I8pIPSLBIEU3tJM0JFVQE0m4UwYLgFNmODRWlhaQg9cJvZ6ZxC0hoL1xyiB1n5M-el8czADuxVUbIXHu-jmNPSBN7yudouAplVWCUyMe3XK_-REnSur6OU-dwK11Y5YQnjhl1PtDkIz9oRmFEh2IN__DxyaOqUXS66kpozMIcZSoTNZg76iTp-dTLQnXaRNioLuZxtO8PytED6kC4EAP_kyj6miGPpczJwnfpW4R5p1-ydrUglmDG9Jdhye3gku25NNO_f0GBrIEi0lF2MdQCWVvr50LqERtYdjHo9lhCifpRJrG0oNMcQpCpEZu6IRiNhb9h7d4tUjK8e2BHUt9jKzKpMeCMUn8gNUkVa16uwOVJ59_xqecqMHia82joqVBFsokTmPPA57kIFecmV61A2dBoUid8LYNYS2ul4AZnFc0nP7C5QkWGW8lXodYf9M0aMOFLofCjPOBokgZCxk3NUVezTRW3TIPXYX8CRqZdenKqktHL0Ewh5LIPyNVhZ9r5scrK8XW3XUI1o72KY2nprhwgRZT1KmtHqLygxcajOmxOUM3cJi6zd0jX_9-8AT-pCn3lmdmE2rB4NlvwQ78Mu2Wx7dbkNoWVXtDztYPv0rO_6c0bIDDx8Q |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3dT9swED-xMmm8sMGGKLDND6B9SBFN7CT1A5q6D0RFiSqNTewpsx0bEKWFpAz1n-Jv5K5Juk1CvPHAs52LHf9yn747gE3ptBUqM55vpPSEstLTPkfDVWinQ6tFNs1y_dmLk6R9dCT7c3BT58LQtcqaJ04ZdTYy5CPfDuIopqBYy_90celR1yiKrtYtNEpY7NvJNZpsxU73K57vVhDsfjv8sudVXQU8w3k89nSkYxWIqJXx0OeZiDTnNtPtULvIGhKRvlGhNMo5JbhFyKJJ4Icu0yicuVMc6T6BeYFgbzdgvt896P-aeXWoLxySLhMBOZet7WJyjjoXUgn9_0Tf3QJgKtV2nz-27_ECFiv9mXVKwC_BnB0uw1LFoQr2viqj_eEl5Mj66MY9ymaGWi7rGHOVKzNhI8e-j04HLKFGBChzWT-naBUhlOkJm7lZGNHC17DO4Bh3Pj45Z5-VOcNRZMJTQDMqbYKrScq79MUr-PEge1-BxnA0tKvAhK-ExoeykKPJHQolA8NRF3WBlm3b4k34WB9-aqry69QFZJCiGUZISf9BShM2Z5Mvyqojd097RyhKiRchLaOqlApcEVX1SjsxKmdokfK4CRs1itKKSRXpXwit3T_8Fp7tHR700l432V-HhQD1vNILtQGNcX5lX8NT82d8WuRvqv-Bwe-HhtwtnIVL2A |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3NT9swFH8CNqFdYDAmurHhA2gbUtQmdpL6ME3doBoCRZXYJm6Z7disWmkhKUP91_jreC8f3ZAQNw4723mxk5_fl98HwI502gqVGc83UnpCWelpn6PhKrTTodUiK7NcfxzHSdI9PZWDBbhpcmEorLLhiSWjziaGfOTtII5iuhTr-G1Xh0UM9vufLi496iBFN61NO40KIkd2do3mW_HxcB__9W4Q9A--ffnq1R0GPMN5PPV0pGMViKiT8dDnmYg05zbT3VC7yBoSl75RoTTKOSW4RfiieeCHLtMoqLlTHOkuwpNYoJ5Qhg2ezP071CEOCVcpgZzLTruYnaP2hTRC_44QvF8UlPKtv_o_f5nnsFJr1axXHYM1WLDjdVir-VbB3tfFtT-8gBwZIsXho8RmqPuynjFXuTIzNnHsZDIcsYTaE6AkZoOc7rAIt0zP2Nz5wogWvob1Rme48-mvc_ZZmd84iqy5hDmjgie4mqSKsC824Puj7P0lLI0nY7sJTPhKaHwoCzka4qFQMjAcNVQXaNm1Hd6CvQYIqamLslNvkFGKxhmhJv0HNS3YmU--qGqR3D_tHSEqJQ6FtIyqEy1wRVTrK-3FqLKhncrjFmw1iEpr1lWkf-H06uHhbVhGnKXHh8nRa3gWoPJXuaa2YGmaX9k38NT8mQ6L_G15MBj8fGy83QLG2VMf |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Increasing+the+Accuracy+of+Soil+Nutrient+Prediction+by+Improving+Genetic+Algorithm+Backpropagation+Neural+Networks&rft.jtitle=Symmetry+%28Basel%29&rft.au=Liu%2C+Yanqing&rft.au=Jiang%2C+Cuiqing&rft.au=Lu%2C+Cuiping&rft.au=Wang%2C+Zhao&rft.date=2023-01-01&rft.pub=MDPI+AG&rft.issn=2073-8994&rft.eissn=2073-8994&rft.volume=15&rft.issue=1&rft_id=info:doi/10.3390%2Fsym15010151&rft.externalDocID=A752148737 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2073-8994&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2073-8994&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2073-8994&client=summon |