Hyperspectral prediction of pigment content in tomato leaves based on logistic-optimized sparrow search algorithm and back propagation neural network

Leaf pigment content can reflect the nutrient elements content of the cultivation medium indirectly. To rapidly and accurately predict the pigment content of tomato leaves, chlorophyll a, chlorophyll b, chlorophyll and carotenoid were extracted from leaves of tomato seedlings cultured at different n...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Journal of agricultural engineering (Pisa, Italy) Ročník 54; číslo 4
Hlavní autori:	Zhao, Jiangui, Zhu, Tingyu, Qiu, Zhichao, Li, Tao, Wang, Guoliang, Li, Zhiwei, Du, Huiling
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Bologna PAGEPress Publications 01.01.2023
Predmet:	Algorithms Back propagation Back propagation networks Carotenoids Chlorophyll Convergence Fertilization Hyperspectral imaging Infrared imaging Leaves logistic chaotic mapping Low concentrations Machine learning Mapping Neural networks Nitrogen Nutrient concentrations Nutrient content Pigments pigments content Predictions Root-mean-square errors Search algorithms Seedlings sparrow search algorithm tomato leaf Tomatoes
ISSN:	1974-7071, 2239-6268
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	Leaf pigment content can reflect the nutrient elements content of the cultivation medium indirectly. To rapidly and accurately predict the pigment content of tomato leaves, chlorophyll a, chlorophyll b, chlorophyll and carotenoid were extracted from leaves of tomato seedlings cultured at different nitrogen concentrations. The visible/near-infrared(VIS/NIR) hyperspectral imaging (HSI) non-destructive measurement technology, 430-900 nm and 950-1650 nm, with total variables of 794, was used to obtain the reflection spectra of leaves. An improved strategy of the sparrow search algorithm (SSA) based on Logistic chaotic mapping was proposed and optimized the back propagation (BP) neural network to predict the pigment content of leaves. Different pretreatment methods were used to effectively improve the prediction accuracy of the model. The results showed that when the nitrogen concentration in the nutrient solution was 302.84 mg·L-1, the pigment content of leaves reached the maximum. Meanwhile, the inhibition effect of high concentration was much stronger than that of low concentration. To address the problem that the SSA is prone to get in premature convergence due to the reduction of population diversity at the end of the iteration, the initialization of the SSA population by Logistic chaotic mapping improves the initial solution quality, convergence speed and search capacity. The root mean squared error (RMSE), coefficient of determination (R2) and relative percent deviation (RPD) of chlorophyll a were 0.77, 0.77 and 2.08, respectively. The RMSE, R2 and RPD of chlorophyll b were 0.30, 0.66 and 1.71, respectively. The RMSE, R2 and RPD of chlorophyll were 0.88, 0.81 and 2.28, respectively. The RMSE, R2 and RPD of carotenoid were 0.14, 0.75 and 2.00, respectively. The HSI technology combined with machine learning algorithms can achieve rapid and accurate prediction of crop physiological information, providing data support for the precise management of fertilization in facility agriculture, which is conducive to improving the quality and output of tomatoes.
AbstractList	Leaf pigment content can reflect the nutrient content of the cultivation medium indirectly. To rapidly and accurately predict the pigment content of tomato leaves, chlorophyll a, chlorophyll b, chlorophyll and carotenoid were extracted from the leaves of tomato seedlings cultured at different nitrogen concentrations. The visible/near-infrared hyperspectral imaging non-destructive measurement technology, 430-900 nm and 950-1650 nm, with total variables of 794, was used to obtain the reflection spectra of leaves. An improved strategy of the sparrow search algorithm (SSA) based on logistic chaotic mapping was proposed, and it optimized the back propagation neural network to predict the pigment content of leaves. Different pretreatment methods were used to effectively improve the prediction accuracy of the model. The results showed that when the nitrogen concentration in the nutrient solution was 302.84 mg·L-1, the pigment content of the leaves reached its maximum. Meanwhile, the inhibition effect of high concentrations was much stronger than that of low concentrations. To address the problem that the SSA is prone to premature convergence due to the reduction of population diversity at the end of the iteration, the initialization of the SSA population by logistic chaotic mapping improves the initial solution quality, convergence speed, and search capacity. The root mean squared error (RMSE), coefficient of determition (R2) and relative percent deviation (RPD) of chlorophyll a were 0.77, 0.77, and 2.08, respectively. The RMSE, R2 and RPD of chlorophyll b were 0.30, 0.66, and 1.71, respectively. The RMSE, R2 and RPD of chlorophyll were 0.88, 0.81, and 2.28, respectively. The RMSE, R2 and RPD of carotenoid were 0.14, 0.75, and 2.00, respectively. Hyperspectral imaging technology combined with machine learning algorithms can achieve rapid and accurate prediction of crop physiological information, providing data support for the precise magement of fertilization in facility agriculture, which is conducive to improving the quality and output of tomatoes. Leaf pigment content can reflect the nutrient elements content of the cultivation medium indirectly. To rapidly and accurately predict the pigment content of tomato leaves, chlorophyll a, chlorophyll b, chlorophyll and carotenoid were extracted from leaves of tomato seedlings cultured at different nitrogen concentrations. The visible/near-infrared(VIS/NIR) hyperspectral imaging (HSI) non-destructive measurement technology, 430-900 nm and 950-1650 nm, with total variables of 794, was used to obtain the reflection spectra of leaves. An improved strategy of the sparrow search algorithm (SSA) based on Logistic chaotic mapping was proposed and optimized the back propagation (BP) neural network to predict the pigment content of leaves. Different pretreatment methods were used to effectively improve the prediction accuracy of the model. The results showed that when the nitrogen concentration in the nutrient solution was 302.84 mg·L-1, the pigment content of leaves reached the maximum. Meanwhile, the inhibition effect of high concentration was much stronger than that of low concentration. To address the problem that the SSA is prone to get in premature convergence due to the reduction of population diversity at the end of the iteration, the initialization of the SSA population by Logistic chaotic mapping improves the initial solution quality, convergence speed and search capacity. The root mean squared error (RMSE), coefficient of determination (R2) and relative percent deviation (RPD) of chlorophyll a were 0.77, 0.77 and 2.08, respectively. The RMSE, R2 and RPD of chlorophyll b were 0.30, 0.66 and 1.71, respectively. The RMSE, R2 and RPD of chlorophyll were 0.88, 0.81 and 2.28, respectively. The RMSE, R2 and RPD of carotenoid were 0.14, 0.75 and 2.00, respectively. The HSI technology combined with machine learning algorithms can achieve rapid and accurate prediction of crop physiological information, providing data support for the precise management of fertilization in facility agriculture, which is conducive to improving the quality and output of tomatoes.
Author	Li, Tao Zhu, Tingyu Qiu, Zhichao Zhao, Jiangui Wang, Guoliang Du, Huiling Li, Zhiwei
Author_xml	– sequence: 1 givenname: Jiangui surname: Zhao fullname: Zhao, Jiangui – sequence: 2 givenname: Tingyu surname: Zhu fullname: Zhu, Tingyu – sequence: 3 givenname: Zhichao surname: Qiu fullname: Qiu, Zhichao – sequence: 4 givenname: Tao surname: Li fullname: Li, Tao – sequence: 5 givenname: Guoliang surname: Wang fullname: Wang, Guoliang – sequence: 6 givenname: Zhiwei surname: Li fullname: Li, Zhiwei – sequence: 7 givenname: Huiling surname: Du fullname: Du, Huiling
BookMark	eNptUUtv1DAQjlCRWEqP3C1xzuJHEttHVAGtVIlLe7Ymzjj1NomD7aUq_4P_i7MLF8RppJnvMTPf2-piCQtW1XtG9w1V7OMBcM8pF3vWcvWq2nEudN3xTl1UO6ZlU0sq2ZvqKqUDpZRxraUWu-rXzcuKMa1oc4SJrBEHb7MPCwmOrH6cccnEhiVv1S8khxlyIBPCD0ykh4QDKeApjD5lb-uwZj_7n6WbVogxPJOEEO0jgWkM0efHmcAyFKJ9KmZhhRFObgseN_8F83OIT--q1w6mhFd_6mX18OXz_fVNffft6-31p7vaCklz7YaeIh8QoVG61c71UvGmtb1mahgalEoAKuY6hVLqpu86zdEBVYLyXlknLqvbs-4Q4GDW6GeILyaAN6dGiKOBWM6a0KietVaophMATatcbyVrXCettK5MhqL14axVzvp-xJTNIRzjUtY3gknBuW60KChxRtkYUorojPX59ILyfz8ZRs0Wpylxmi1Os8VZWPU_rL-7_h__G5KUqA8
CitedBy_id	crossref_primary_10_1007_s11769_024_1421_1 crossref_primary_10_1016_j_foodchem_2025_143913
Cites_doi	10.1016/j.jcrysgro.2013.06.018 10.1016/S0176-1617(11)81192-2 10.1109/ACCESS.2021.3052960 10.3389/fpls.2018.01406 10.1016/j.egyr.2021.12.022 10.1364/AO.462436 10.1016/j.chaos.2007.10.049 10.1016/j.biosystemseng.2007.01.008 10.1109/ACCESS.2021.3075547 10.1155/2021/6860503 10.1016/j.biosystemseng.2013.07.003 10.1002/ejic.201500702 10.1016/j.foodchem.2021.129141 10.1080/01904169709365318 10.1080/21642583.2019.1708830 10.1016/j.scienta.2014.07.002 10.1109/ACCESS.2022.3182241 10.1002/jsfa.9399 10.1016/j.talanta.2005.03.025 10.1016/j.future.2020.10.009 10.1081/PLN-100106021 10.1093/jxb/eraa432 10.1016/j.jcrysgro.2010.04.051
ContentType	Journal Article
Copyright	2023. This work is licensed under https://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml	– notice: 2023. This work is licensed under https://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID	AAYXX CITATION 3V. 7X2 8FE 8FG 8FH 8FK ABJCF ABUWG AEUYN AFKRA ATCPS AZQEC BENPR BGLVJ BHPHI CCPQU DWQXO HCIFZ L6V M0K M7S PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PTHSS DOA
DOI	10.4081/jae.2023.1528
DatabaseName	CrossRef ProQuest Central (Corporate) Agricultural Science Collection ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection ProQuest Central (Alumni) (purchase pre-March 2016) Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland Agricultural & Environmental Science Collection ProQuest Central Essentials ProQuest Central Technology Collection Natural Science Collection ProQuest One Community College ProQuest Central SciTech Premium Collection ProQuest Engineering Collection Agriculture Science Database Engineering Database ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection Directory of Open Access Journals
DatabaseTitle	CrossRef Agricultural Science Database Publicly Available Content Database Technology Collection ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Natural Science Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Sustainability ProQuest Engineering Collection Natural Science Collection ProQuest Central Korea Agricultural & Environmental Science Collection ProQuest Central (New) Engineering Collection Engineering Database ProQuest One Academic Eastern Edition Agricultural Science Collection ProQuest Technology Collection ProQuest SciTech Collection ProQuest One Academic UKI Edition Materials Science & Engineering Collection ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni)
DatabaseTitleList	Agricultural Science Database CrossRef
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Agriculture
EISSN	2239-6268
ExternalDocumentID	oai_doaj_org_article_8b15c38463aa458fbc714f67c7cf15cd 10_4081_jae_2023_1528
GroupedDBID	5VS 67V 7X2 AAYXX ABDBF ABJCF ACUHS ADBBV AEUYN AFFHD AFKRA ALMA_UNASSIGNED_HOLDINGS ATCPS BCNDV BENPR BGLVJ BHPHI CCPQU CITATION GROUPED_DOAJ HCIFZ KQ8 M0K M7S PHGZM PHGZT PIMPY PQGLB PTHSS 3V. 8FE 8FG 8FH 8FK ABUWG AZQEC DWQXO L6V PKEHL PQEST PQQKQ PQUKI PRINS
ID	FETCH-LOGICAL-c370t-fdb0e2deea48959ffb78245cb918dd4e783ae81f68e7794b6692efa08302b8cf3
IEDL.DBID	DOA
ISICitedReferencesCount	3
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001163540100003&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1974-7071
IngestDate	Fri Oct 03 12:52:31 EDT 2025 Fri Jul 25 11:46:10 EDT 2025 Tue Nov 18 21:49:00 EST 2025 Sat Nov 29 03:38:46 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	4
Language	English
License	https://creativecommons.org/licenses/by-nc/4.0
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c370t-fdb0e2deea48959ffb78245cb918dd4e783ae81f68e7794b6692efa08302b8cf3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
OpenAccessLink	https://doaj.org/article/8b15c38463aa458fbc714f67c7cf15cd
PQID	3173229493
PQPubID	4728908
ParticipantIDs	doaj_primary_oai_doaj_org_article_8b15c38463aa458fbc714f67c7cf15cd proquest_journals_3173229493 crossref_citationtrail_10_4081_jae_2023_1528 crossref_primary_10_4081_jae_2023_1528
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	Bologna
PublicationPlace_xml	– name: Bologna
PublicationTitle	Journal of agricultural engineering (Pisa, Italy)
PublicationYear	2023
Publisher	PAGEPress Publications
Publisher_xml	– name: PAGEPress Publications
References	13220 13221 13224 13225 13222 13223 13228 13229 13226 13227 13231 13232 13230 13219 13235 13214 13236 13233 13234 13217 13218 13215 13237 13216 13238
References_xml	– ident: 13220 doi: 10.1016/j.jcrysgro.2013.06.018 – ident: 13231 doi: 10.1016/S0176-1617(11)81192-2 – ident: 13236 doi: 10.1109/ACCESS.2021.3052960 – ident: 13229 – ident: 13223 doi: 10.3389/fpls.2018.01406 – ident: 13215 doi: 10.1016/j.egyr.2021.12.022 – ident: 13238 doi: 10.1364/AO.462436 – ident: 13221 doi: 10.1016/j.chaos.2007.10.049 – ident: 13232 doi: 10.1016/j.biosystemseng.2007.01.008 – ident: 13227 doi: 10.1109/ACCESS.2021.3075547 – ident: 13234 doi: 10.1155/2021/6860503 – ident: 13226 doi: 10.1016/j.biosystemseng.2013.07.003 – ident: 13222 doi: 10.1002/ejic.201500702 – ident: 13224 doi: 10.1016/j.foodchem.2021.129141 – ident: 13217 doi: 10.1080/01904169709365318 – ident: 13233 doi: 10.1080/21642583.2019.1708830 – ident: 13214 doi: 10.1016/j.scienta.2014.07.002 – ident: 13219 doi: 10.1109/ACCESS.2022.3182241 – ident: 13230 doi: 10.1002/jsfa.9399 – ident: 13218 doi: 10.1016/j.talanta.2005.03.025 – ident: 13237 doi: 10.1016/j.future.2020.10.009 – ident: 13216 doi: 10.1081/PLN-100106021 – ident: 13228 doi: 10.1093/jxb/eraa432 – ident: 13225 doi: 10.1016/j.jcrysgro.2010.04.051 – ident: 13235
SSID	ssj0001299793 ssib055683572 ssib044743255
Score	2.243608
Snippet	Leaf pigment content can reflect the nutrient elements content of the cultivation medium indirectly. To rapidly and accurately predict the pigment content of... Leaf pigment content can reflect the nutrient content of the cultivation medium indirectly. To rapidly and accurately predict the pigment content of tomato...
SourceID	doaj proquest crossref
SourceType	Open Website Aggregation Database Enrichment Source Index Database
SubjectTerms	Algorithms Back propagation Back propagation networks Carotenoids Chlorophyll Convergence Fertilization Hyperspectral imaging Infrared imaging Leaves logistic chaotic mapping Low concentrations Machine learning Mapping Neural networks Nitrogen Nutrient concentrations Nutrient content Pigments pigments content Predictions Root-mean-square errors Search algorithms Seedlings sparrow search algorithm tomato leaf Tomatoes
SummonAdditionalLinks	– databaseName: Engineering Database dbid: M7S link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELagcIAD5SmWFuQD4kTUTezE9gmViqqnCgmQerP8DKHbZMmGHvo_-L_MON5WCMGFUyR7Ejua8cx4PP6GkNcefFSjoiiiKWPBnYIlFRpZMCbq0DBw4ZVJxSbE6ak8O1Mfc8Btk9MqtzoxKWo_OIyRH4CdA9lTXLF36-8FVo3C09VcQuM2uYMoCWVK3fu0lSfOwTxWN_cuEWyL1fmeaI7BKJFweUuFaYlgbmcYTg6G8uCbQRjNimGJIPmb2Uro_n8o72SRjnf_918ekgfZF6WHs_A8Ire66TG5f9iOGY8jPCE_T2CfOl_HHIF0PeK5DvKSDpGuuxZjixTT3fHZ9XQawAMe6CqYy7ChaCI9BeL5nlHnigE01EV3Ba2gyRD-kc5LjZpVCzOcvl5Q03t40Z3DYLChb5PkUITdhPH7OWn9Kfly_OHz0UmRKzkUjonlVERvl6HyIRguVa1itOCY8NpZVUrveRCSmSDL2MggQEHYplFViGaJ4GRWusiekZ1-6MNzQqUwqrENx7NpcPWYLV3pDEIGVULBpxbk7ZZR2mWYc6y2sdKw3UG-auCrRr5q5OuCvLkmX8_4Hn8jfI9cvyZCWO7UMIytzqtcS1vWjoFLx4zhtYzWiZLHRjjhIvT4BdnfCoTOumKjb6Thxb-798g9nM4cANonO9P4I7wkd93l1G3GV0n0fwGilgxF priority: 102 providerName: ProQuest
Title	Hyperspectral prediction of pigment content in tomato leaves based on logistic-optimized sparrow search algorithm and back propagation neural network
URI	https://www.proquest.com/docview/3173229493 https://doaj.org/article/8b15c38463aa458fbc714f67c7cf15cd
Volume	54
WOSCitedRecordID	wos001163540100003&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: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2239-6268 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001299793 issn: 1974-7071 databaseCode: DOA dateStart: 20070101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2239-6268 dateEnd: 99991231 omitProxy: false ssIdentifier: ssib044743255 issn: 1974-7071 databaseCode: M~E dateStart: 20120101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Agriculture Science Database customDbUrl: eissn: 2239-6268 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001299793 issn: 1974-7071 databaseCode: M0K dateStart: 20090601 isFulltext: true titleUrlDefault: https://search.proquest.com/agriculturejournals providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 2239-6268 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001299793 issn: 1974-7071 databaseCode: M7S dateStart: 20090601 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2239-6268 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001299793 issn: 1974-7071 databaseCode: BENPR dateStart: 20090601 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2239-6268 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001299793 issn: 1974-7071 databaseCode: PIMPY dateStart: 20090601 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELZQywEOVaEgtrSVDxUnoiaxE9vHtmpVhLpaAZXKybIdewlsk1U29MD_4P92Jk4fCCEuvThSPIktz3gefnxDyH4FPqpRQSTBZCHhTsGU8qVMGBOFLxm48MoMySbEdCovL9XsQaovPBMW4YHjwB1ImxWOgZVkxvBCButExkMpnHABairUvqlQD4IpkCTOwTDm9zcuEWaLFeMN0XH1RYkBkTdTeCARDG0E4ORgIg--GwTQzBkmB5J_GKwB1_8vtT3YotNNsjE6kfQwdv4FeeKbl-T54bwbgTT8Fvl9BgFmvEfZAemyww0ZZAJtA13Wc1wUpHhOHZ91Q_sWXNeWLry59iuKtq2iQBwvCNUuaUG1XNW_4C2oIMRtpHGOULOYt13df7uipqngQ_cDGoNIfD6wnCJeJrTfxNPmr8jF6cmX47NkTMGQOCbSPgmVTX1eeW-4VIUKwYJHwQtnVSarinshmfEyC6X0Ama2LUuV-2BSRBWz0gX2mqw1bePfECqFUaUtOW4qg4_GbOYyZxDrJxcKfjUh72_HWbsRnxzTZCw0xCnIFg1s0cgWjWyZkHd35MsIzPEvwiNk2h0R4mkPL0DK9Chl-n9SNiE7tyzX4yRfaXC9QB0qrtj2Y7TxljzDTsf1nR2y1nc__S556q77etXtkfWjk-ns094g51Cepx-xFJ-hZvbhfPb1BhcgA9Q
linkProvider	Directory of Open Access Journals
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VLRJw4I1YKOADcCLqJnZi-4BQeVS7arvaQ5HKKTiOvQS2myUbiuB_8Df4jczk0QohuPXAKZIzsRPn88z4Md8APM7RRzXay8Cb0AfCahxSLlEB5zJ2CUcXXpsm2YScTtXRkZ5twM8-FoaOVfY6sVHUeWlpjXwb7RxiTwvNX6w-B5Q1inZX-xQaLSz23LevOGVbP5-8xv_7JIp23xy-GgddVoHAcjmqA59nIxflzhmhdKy9z9BIithmOlR5LpxU3DgV-kQ5iWDNkkRHzpsREWVlynqO9V6ATUFgH8DmbHIwe9cjWAg0yNFZpCfRe_G4i0ztVn20bJiAQ00HIdHAt8SfAk3z9kdDxJ0Rp6RE6jdD2eQT-MNcNDZw99r_1nvX4WrnbbOddnjcgI2ivglXduZVxzjibsGPMc7E24DTCkVXFe1cEVpZ6dmqmNPqKaMD_XQtlqwu0ccv2cKZE7dm5ATkDIXbSKrCBiXq4OPiO5airiaCS9Z-NzOLOfZI_eGYmWWOD9pP2FiJ-rwZG4yIRbH9ZXss_za8PZd-uQODZbl0d4EpaXSSJYJ239GZ5VloQ2uIFCmSGqsawrMeGKntiNwpn8gixQkd4ShFHKWEo5RwNISnp-KrlsHkb4IvCWWnQkQ83hSU1Tzt9FiqsjC2HJ1WboyIlc-sDIVPpJXW4518CFs9ANNOG67TM_Td-_ftR3BpfHiwn-5Ppnv34TK9WrvctQWDuvriHsBFe1IX6-phN_AYvD9vtP4C-whshg
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=Hyperspectral+prediction+of+pigment+content+in+tomato+leaves+based+on+logistic-optimized+sparrow+search+algorithm+and+back+propagation+neural+network&rft.jtitle=Journal+of+agricultural+engineering+%28Pisa%2C+Italy%29&rft.au=Zhao%2C+Jiangui&rft.au=Zhu%2C+Tingyu&rft.au=Qiu%2C+Zhichao&rft.au=Li%2C+Tao&rft.date=2023-01-01&rft.issn=1974-7071&rft.eissn=2239-6268&rft_id=info:doi/10.4081%2Fjae.2023.1528&rft.externalDBID=n%2Fa&rft.externalDocID=10_4081_jae_2023_1528
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1974-7071&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1974-7071&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1974-7071&client=summon