Automatic early detection of rice leaf diseases using hybrid deep learning and machine learning methods
Plant leaf disease detection is critical for long-term agricultural viability. Numerous Artificial Intelligence (AI) and Machine Learning (ML) technologies have been implemented for detecting rice diseases. However, such methods failed to identify or have slow recognition causing severe output loss....
Saved in:
| Published in | Multimedia tools and applications Vol. 82; no. 23; pp. 36091 - 36117 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.09.2023
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | Plant leaf disease detection is critical for long-term agricultural viability. Numerous Artificial Intelligence (AI) and Machine Learning (ML) technologies have been implemented for detecting rice diseases. However, such methods failed to identify or have slow recognition causing severe output loss. Therefore, an advanced and precise detection method has become necessary to overcome this issue. This study analyzes plant diseases that affect rice, comprising three different forms of diseases. Bacterial leaf blight, Brown spot, and Leaf smut are three of the six diseases that can affect rice plants. In the proposed approach a VGG-16 transfer learning with Faster R-CNN deep architecture is used to extract features. After completing the transfer learning step, the gathered characteristics are categorized using the random forest method. The random forest classifier divided the radish field into three distinct regions. The images of rice plant leaves are taken from UCI Machine Learning Repository. The proposed approach obtains an average predicting accuracy of 97.3% for rice disease imagery class prediction. The extensive experiment outcomes demonstrate the suggested technique’s validity, so it effectively detects rice diseases. |
|---|---|
| AbstractList | Plant leaf disease detection is critical for long-term agricultural viability. Numerous Artificial Intelligence (AI) and Machine Learning (ML) technologies have been implemented for detecting rice diseases. However, such methods failed to identify or have slow recognition causing severe output loss. Therefore, an advanced and precise detection method has become necessary to overcome this issue. This study analyzes plant diseases that affect rice, comprising three different forms of diseases. Bacterial leaf blight, Brown spot, and Leaf smut are three of the six diseases that can affect rice plants. In the proposed approach a VGG-16 transfer learning with Faster R-CNN deep architecture is used to extract features. After completing the transfer learning step, the gathered characteristics are categorized using the random forest method. The random forest classifier divided the radish field into three distinct regions. The images of rice plant leaves are taken from UCI Machine Learning Repository. The proposed approach obtains an average predicting accuracy of 97.3% for rice disease imagery class prediction. The extensive experiment outcomes demonstrate the suggested technique’s validity, so it effectively detects rice diseases. |
| Author | Rajpoot, Vikram Tiwari, Akhilesh Jalal, Anand Singh |
| Author_xml | – sequence: 1 givenname: Vikram surname: Rajpoot fullname: Rajpoot, Vikram organization: Department of Information Technology, Madhav Institute of Technology & Science – sequence: 2 givenname: Akhilesh surname: Tiwari fullname: Tiwari, Akhilesh organization: Department of Information Technology, Madhav Institute of Technology & Science – sequence: 3 givenname: Anand Singh orcidid: 0000-0002-7469-6608 surname: Jalal fullname: Jalal, Anand Singh email: asjalal@gla.ac.in organization: Department of Computer Engineering and Applications |
| BookMark | eNp9kE1LAzEURYNUsFb_gKuA69F8zmSWpfgFBTe6Dmny0qa0mZqki_n3zlih4MJVws09eY9zjSaxi4DQHSUPlJDmMVNKBKsI4xUVbd1W_QWaUtnwqmkYnQx3rkjVSEKv0HXOW0JoLZmYovX8WLq9KcFiMGnXYwcFbAldxJ3HKVjAOzAeu5DBZMj4mENc402_SsENZTiM7ymOoYkO743dhAjncA9l07l8gy692WW4_T1n6PP56WPxWi3fX94W82VlOW1L5S1ZGc8sFY1whHNPxEqRRglwK04bKUXNwNvWqzFgTJjWKSGJc0x4TjyfofvTv4fUfR0hF73tjikOIzVTdS2lUlIOLXZq2dTlnMDrQwp7k3pNiR6F6pNQPQjVP0J1P0DqD2RDMaOqkkzY_Y_yE5qHOXEN6bzVP9Q3HdmOfw |
| CitedBy_id | crossref_primary_10_1007_s11042_024_18723_w crossref_primary_10_1016_j_cpb_2024_100382 crossref_primary_10_3389_fpls_2023_1255015 crossref_primary_10_1007_s11042_023_16882_w crossref_primary_10_33889_IJMEMS_2024_9_4_050 crossref_primary_10_3390_agriculture14122188 crossref_primary_10_53759_7669_jmc202404095 |
| Cites_doi | 10.1109/ICACCS.2019.8728415 10.1016/j.neucom.2017.06.023 10.1016/j.procs.2020.03.404 10.1016/j.compag.2018.01.009 10.20546/ijcmas.2019.806.337 10.1109/CVPR.2014.81 10.1109/AICAI.2019.8701294 10.1016/j.compag.2018.04.023 10.1109/Confluence47617.2020.9057889 10.1007/s11356-021-16627-y 10.1109/TGRS.2021.3090410 10.1016/j.compag.2017.04.013 10.1002/jsfa.10365 10.9734/arrb/2020/v35i130180 10.1109/ICDI3C.2018.00017 10.1109/ICSP.2016.7878133 10.1007/s40974-017-0074-7 10.2139/ssrn.3351641 10.1016/j.envres.2021.111275 10.3390/su11010189 10.1109/GCAT47503.2019.8978431 10.1109/IACC48062.2019.8971580 10.1109/ICASERT.2019.8934568 10.3233/IDT-170301 10.5423/PPJ.OA.07.2019.0205 10.1109/STI47673.2019.9068096 10.1016/j.chemosphere.2021.132569 10.1109/CCECE.2017.7946787 10.1016/j.compag.2020.105220 10.1080/17517575.2018.1557256 10.48550/arXiv.1506.01497 10.7717/peerj-cs.432 10.1109/ICCV.2015.169 10.1007/s11263-015-0816-y 10.1109/ICon-CuTE47290.2019.8991476 10.1109/ICMETE.2018.00063 10.1155/2022/2845320 10.1109/ICSCET.2018.8537336 10.3389/fpls.2015.01066 10.1016/j.compag.2019.104962 10.1080/21645515.2017.1379639 10.1016/j.compag.2019.105197 10.1016/j.compag.2019.105099 10.1109/STI47673.2019.9068092 10.1080/00987913.2004.10764916 10.1007/978-981-16-8763-1_38 10.1155/2022/6504616 10.1109/IC3INA.2018.8629507 10.1016/j.compag.2019.104948 10.17582/journal.pjar/2019/32.2.359.380 |
| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| Copyright_xml | – notice: The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| DBID | AAYXX CITATION 3V. 7SC 7WY 7WZ 7XB 87Z 8AL 8AO 8FD 8FE 8FG 8FK 8FL 8G5 ABUWG AFKRA ARAPS AZQEC BENPR BEZIV BGLVJ CCPQU DWQXO FRNLG F~G GNUQQ GUQSH HCIFZ JQ2 K60 K6~ K7- L.- L7M L~C L~D M0C M0N M2O MBDVC P5Z P62 PHGZM PHGZT PKEHL PQBIZ PQBZA PQEST PQGLB PQQKQ PQUKI Q9U |
| DOI | 10.1007/s11042-023-14969-y |
| DatabaseName | CrossRef ProQuest Central (Corporate) Computer and Information Systems Abstracts ABI/INFORM Collection ABI/INFORM Global (PDF only) ProQuest Central (purchase pre-March 2016) ABI/INFORM Collection Computing Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) ABI/INFORM Collection (Alumni) ProQuest Research Library ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Central Business Premium Collection Technology Collection ProQuest One Community College ProQuest Central Korea Business Premium Collection (Alumni) ABI/INFORM Global (Corporate) ProQuest Central Student ProQuest Research Library SciTech Premium Collection ProQuest Computer Science Collection ProQuest Business Collection (Alumni Edition) ProQuest Business Collection Computer Science Database ABI/INFORM Professional Advanced Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional ABI/INFORM Global Computing Database Research Library Research Library (Corporate) Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic ProQuest One Academic Middle East (New) ProQuest One Business (UW System Shared) ProQuest One Business (Alumni) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central Basic |
| DatabaseTitle | CrossRef ABI/INFORM Global (Corporate) ProQuest Business Collection (Alumni Edition) ProQuest One Business Research Library Prep Computer Science Database ProQuest Central Student Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College Research Library (Alumni Edition) ProQuest Pharma Collection ABI/INFORM Complete ProQuest Central ABI/INFORM Professional Advanced ProQuest One Applied & Life Sciences ProQuest Central Korea ProQuest Research Library ProQuest Central (New) Advanced Technologies Database with Aerospace ABI/INFORM Complete (Alumni Edition) Advanced Technologies & Aerospace Collection Business Premium Collection ABI/INFORM Global ProQuest Computing ABI/INFORM Global (Alumni Edition) ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest SciTech Collection ProQuest Business Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition ProQuest One Business (Alumni) ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) Business Premium Collection (Alumni) |
| DatabaseTitleList | ABI/INFORM Global (Corporate) |
| Database_xml | – sequence: 1 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Computer Science |
| EISSN | 1573-7721 |
| EndPage | 36117 |
| ExternalDocumentID | 10_1007_s11042_023_14969_y |
| GroupedDBID | -4Z -59 -5G -BR -EM -Y2 -~C .4S .86 .DC .VR 06D 0R~ 0VY 123 1N0 1SB 2.D 203 28- 29M 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 30V 3EH 3V. 4.4 406 408 409 40D 40E 5QI 5VS 67Z 6NX 7WY 8AO 8FE 8FG 8FL 8G5 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAOBN AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACBXY ACDTI ACGFO ACGFS ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACREN ACSNA ACZOJ ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADYOE ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFGCZ AFKRA AFLOW AFQWF AFWTZ AFYQB AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMTXH AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN AZQEC B-. BA0 BBWZM BDATZ BENPR BEZIV BGLVJ BGNMA BPHCQ BSONS CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 DWQXO EBLON EBS EIOEI EJD ESBYG FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRNLG FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNUQQ GNWQR GQ6 GQ7 GQ8 GROUPED_ABI_INFORM_COMPLETE GUQSH GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IHE IJ- IKXTQ ITG ITH ITM IWAJR IXC IXE IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ K60 K6V K6~ K7- KDC KOV KOW LAK LLZTM M0C M0N M2O M4Y MA- N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM OVD P19 P2P P62 P9O PF0 PQBIZ PQBZA PQQKQ PROAC PT4 PT5 Q2X QOK QOS R4E R89 R9I RHV RNI RNS ROL RPX RSV RZC RZE RZK S16 S1Z S26 S27 S28 S3B SAP SCJ SCLPG SCO SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TEORI TH9 TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 Z7R Z7S Z7W Z7X Z7Y Z7Z Z81 Z83 Z86 Z88 Z8M Z8N Z8Q Z8R Z8S Z8T Z8U Z8W Z92 ZMTXR ~EX AAPKM AAYXX ABBRH ABDBE ABFSG ACMFV ACSTC ADHKG ADKFA AEZWR AFDZB AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT 7SC 7XB 8AL 8FD 8FK ABRTQ JQ2 L.- L7M L~C L~D MBDVC PKEHL PQEST PQGLB PQUKI Q9U |
| ID | FETCH-LOGICAL-c319t-fc0baf2c1474d033f04b80784edb31755462efc9f8edb3224a9d8450dd24f30f3 |
| IEDL.DBID | U2A |
| ISSN | 1380-7501 |
| IngestDate | Fri Jul 25 23:17:02 EDT 2025 Thu Apr 24 22:58:54 EDT 2025 Tue Jul 01 04:13:20 EDT 2025 Fri Feb 21 02:42:06 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 23 |
| Keywords | Rice leaf disease VGG-16 Machine learning Plant Disease Faster R-CNN Deep Learning Random Forest |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c319t-fc0baf2c1474d033f04b80784edb31755462efc9f8edb3224a9d8450dd24f30f3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-7469-6608 |
| PQID | 2866558855 |
| PQPubID | 54626 |
| PageCount | 27 |
| ParticipantIDs | proquest_journals_2866558855 crossref_primary_10_1007_s11042_023_14969_y crossref_citationtrail_10_1007_s11042_023_14969_y springer_journals_10_1007_s11042_023_14969_y |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 20230900 2023-09-00 20230901 |
| PublicationDateYYYYMMDD | 2023-09-01 |
| PublicationDate_xml | – month: 9 year: 2023 text: 20230900 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York – name: Dordrecht |
| PublicationSubtitle | An International Journal |
| PublicationTitle | Multimedia tools and applications |
| PublicationTitleAbbrev | Multimed Tools Appl |
| PublicationYear | 2023 |
| Publisher | Springer US Springer Nature B.V |
| Publisher_xml | – name: Springer US – name: Springer Nature B.V |
| References | Hasan MJ, Mahbub S, Alom MS, Abu Nasim M (2019) “Rice disease identification and classification by integrating support vector machine with deep convolutional neural network,”https://doi.org/10.1109/ICASERT.2019.8934568 de Bigirimana VP, Hua GKH, Nyamangyoku OI, Hòfte M (2015) “Rice sheath rot: An emerging ubiquitous destructive disease complex,” Front Plant Sci, https://doi.org/10.3389/fpls.2015.01066 Ginanni K (2004) Peace Corps' information collection and exchange. Ser Rev 30(3):249–251 PrasadKNLVNKumarCSPSubediBAbrahaHBSathishkumarVERice leaf diseases prediction using deep neural networks with transfer learningEnviron Res202119811127510.1016/j.envres.2021.111275 Chokey T, Jain S (2019) “Quality assessment of crops using machine learning techniques,”https://doi.org/10.1109/AICAI.2019.8701294 Arun Pandian J, Geetharamani G, Annette B (2019) “Data augmentation on plant leaf disease image dataset using image manipulation and deep learning techniques,”https://doi.org/10.1109/IACC48062.2019.8971580 BhattiUAYuZHasnainANawazSAYuanLWenLBhattiMAEvaluating the impact of roads on the diversity pattern and density of trees to improve the conservation of speciesEnviron Sci Pollut Res20222910147801479010.1007/s11356-021-16627-y Ahmed K, Shahidi TR, Irfanul Alam SM, Momen S (2019) “Rice leaf disease detection using machine learning techniques,”https://doi.org/10.1109/STI47673.2019.9068096 Lee SH, Goëau H, Bonnet P, Joly A (2020) “New perspectives on plant disease characterization based on deep learning,” Comput Electron Agric, https://doi.org/10.1016/j.compag.2020.105220 SinghAKSreenivasuSVNMahalaxmiUSBKSharmaHPatilDDAsensoEHybrid feature-based disease detection in plant leaf using convolutional neural network, Bayesian optimized SVM, and random Forest classifierJ Food Qual202220222845320284531610.1155/2022/2845320 Anjna, MS, Singh PK (2020) “Hybrid System for Detection and Classification of Plant Disease Using Qualitative Texture Features Analysis,”https://doi.org/10.1016/j.procs.2020.03.404 Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst. https://doi.org/10.48550/arXiv.1506.01497 Nanjundan J et al (2020) “Identification of new source of resistance to powdery mildew of indian mustard and studying its inheritance,” Plant Pathol J, https://doi.org/10.5423/PPJ.OA.07.2019.0205 SinhaKGhoshalDBhuniaNRice leaf disease classification using transfer learningLect Notes Networks Syst202237546747510.1007/978-981-16-8763-1_38 Govardhan M, Veena MB (2019) “Diagnosis of tomato plant diseases using random Forest,”https://doi.org/10.1109/GCAT47503.2019.8978431 BohraJSadhukhanVPRManagement of brown spot disease in rice (Helminthosporium oryzae) by spraying of cow urineInt J Chem Stud20186117211723 Bari BS, Islam MN, Rashid M, Hasan MJ, Razman MAM, Musa RM, Ab Nasir AF, Abdul PP, Majeed A (2021) A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework. PeerJ Comput Sci 7:e432–e432. https://doi.org/10.7717/peerj-cs.432 Cynthia ST, Shahrukh Hossain KM, Hasan MN, Asaduzzaman M, Das AK (2019) “Automated detection of plant diseases using image processing and faster R-CNN algorithm,”https://doi.org/10.1109/STI47673.2019.9068092 Kamal KC, Yin Z, Wu M, Wu Z (2019) “Depthwise separable convolution architectures for plant disease classification,” Comput Electron Agric, https://doi.org/10.1016/j.compag.2019.104948 Ferentinos KP (2018) “Deep learning models for plant disease detection and diagnosis,” Comput Electron Agric, https://doi.org/10.1016/j.compag.2018.01.009 Girshick R, Donahue J, Darrell T, Malik J (2013) “Rich feature hierarchies for accurate object detection and semantic segmentation,” Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit, https://doi.org/10.1109/CVPR.2014.81 Naqvi SAH (2019) “Bacterial Leaf Blight of Rice: An Overview of Epidemiology and Management with Special Reference to-Indian-Sub-Continent,” Pakistan J Agric Res, https://doi.org/10.17582/journal.pjar/2019/32.2.359.380 BhattiUAHuangMWangHZhangYMehmoodADiWRecommendation system for immunization coverage and monitoringHum Vaccin Immunother201814116517110.1080/21645515.2017.1379639 BhavatariniCMTPriyadharshiniVRadhakrishnanMA machine learning approach for plant disease classification and pesticides suggestion using rank based attribute selectionInt J Sci Eng Res202011310211024 Chen J, Zhang D, Nanehkaran Y, Li D (2020) “Detection of rice plant diseases based on deep transfer learning,” J Sci Food Agric, vol. 100, https://doi.org/10.1002/jsfa.10365 Kusumo BS, Heryana A, Mahendra O, Pardede HF (2019) “Machine learning-based for automatic detection of Corn-Plant diseases using image processing,” https://doi.org/10.1109/IC3INA.2018.8629507 Prajapati HB, Shah JP, Dabhi VK (2017) “Detection and classification of rice plant diseases,” Intell Decis Technol, https://doi.org/10.3233/IDT-170301 Shahriar SA, Imtiaz AA, Hossain MB, Husna A, Eaty MNK (2020) “Review: Rice blast disease,” Annu Res Rev Biol, https://doi.org/10.9734/arrb/2020/v35i130180 BhattiUALocal Similarity-Based Spatial-Spectral Fusion Hyperspectral Image Classification With Deep CNN and Gabor FilteringIEEE Trans Geosci Remote Sens20226011510.1109/TGRS.2021.3090410 Kim WS, Lee DH, Kim YJ (2020) “Machine vision-based automatic disease symptom detection of onion downy mildew,” Comput Electron Agric, https://doi.org/10.1016/j.compag.2019.105099 Sharif M, Khan MA, Iqbal Z, Azam MF, Lali MIU, Javed MY (2018) “Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection,” Comput Electron Agric https://doi.org/10.1016/j.compag.2018.04.023 BhattiUAHuangMWuDZhangYMehmoodAHanHRecommendation system using feature extraction and pattern recognition in clinical care systemsEnterp Inf Syst201913332935110.1080/17517575.2018.1557256 Barbosa A, Trevisan R, Hovakimyan N, Martin NF (2020) “Modeling yield response to crop management using convolutional neural networks,” Comput Electron Agric, https://doi.org/10.1016/j.compag.2019.105197 Shruthi U, Nagaveni V, Raghavendra BK (2019) “A review on machine learning classification techniques for plant disease detection,”https://doi.org/10.1109/ICACCS.2019.8728415 Girshick R (2015) “Fast R-CNN,”\, https://doi.org/10.1109/ICCV.2015.169 Truong T, Dinh A, Wahid K (2017) “An IoT environmental data collection system for fungal detection in crop fields,”https://doi.org/10.1109/CCECE.2017.7946787 Baranwal S, Khandelwal S, Arora A (2019) “Deep learning convolutional neural network for apple leaves disease detection,” SSRN Electron J, https://doi.org/10.2139/ssrn.3351641 Sharma P, Hans P, Gupta SC (2020) “Classification of plant leaf diseases using machine learning and image preprocessing techniques,”, https://doi.org/10.1109/Confluence47617.2020.9057889 Russakovsky O et al (2015) “ImageNet large scale visual recognition challenge,” Int J Comput Vis, https://doi.org/10.1007/s11263-015-0816-y Singh R, Sunder S, Kumar P (2016) Sheath blight of rice: current status and perspectives. Indian Phytopathol 69(4):340–351 Verma G, Taluja C, Saxena AK (2019) “Vision based detection and classification of disease on Rice crops using convolutional neural network,” https://doi.org/10.1109/ICon-CuTE47290.2019.8991476 Johannes A et al (2017) “Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case,” Comput Electron Agric https://doi.org/10.1016/j.compag.2017.04.013 Abduljabbar R, Dia H, Liyanage S, Bagloee SA (2019) “Applications of artificial intelligence in transport: An overview,” Sustain (Switzerland), https://doi.org/10.3390/su11010189 Saberi AnariMA hybrid model for leaf diseases classification based on the modified deep transfer learning and ensemble approach for agricultural AIoT-based monitoringComput Intell Neurosci20222022650461610.1155/2022/6504616 Tian K, Li J, Zeng J, Evans A, Zhang L (2019) “Segmentation of tomato leaf images based on adaptive clustering number of K-means algorithm,” Comput Electron Agric, https://doi.org/10.1016/j.compag.2019.104962 Chopda J, Raveshiya H, Nakum S, Nakrani V (2018) “Cotton crop disease detection using decision tree classifier,”https://doi.org/10.1109/ICSCET.2018.8537336 Sandika B, Avil S, Sanat S, Srinivasu P (2016) “Random forest based classification of diseases in grapes from images captured in uncontrolled environments,”https://doi.org/10.1109/ICSP.2016.7878133 Ramesh S, Vydeki D (2018) “Rice blast disease detection and classification using machine learning algorithm,”https://doi.org/10.1109/ICMETE.2018.00063 BhattiUAZeeshanZNizamaniMMBazaiSYuZYuanLAssessing the change of ambient air quality patterns in Jiangsu Province of China pre-to post-COVID-19Chemosphere202228813256910.1016/j.chemosphere.2021.132569 Maniyath SR et al (2018) “Plant disease detection using machine learning,”https://doi.org/10.1109/ICDI3C.2018.00017 “Faster R-CNN | ML,” (2020) geeksforgeeks Singh R, Singh GS (2017) “Traditional agriculture: a climate-smart approach for sustainable food production,” Energy Ecol Environ, https://doi.org/10.1007/s40974-017-0074-7 Lu Y, Yi S, Zeng N, Liu Y, Zhang Y (2017) “Identification of rice diseases using deep convolutional neural networks,” Neurocomputing, https://doi.org/10.1016/j.neucom.2017.06.023 Chaudhari AK, Rakholiya KB, Baria TT (2019) “Epidemiological Study of False Smut of Rice (Oryza sativa L.) in Gujarat,” Int J Curr Microbiol Appl Sci, https://doi.org/10.20546/ijcmas.2019.806.337 UA Bhatti (14969_CR11) 2022; 60 UA Bhatti (14969_CR9) 2019; 13 K Sinha (14969_CR51) 2022; 375 14969_CR3 14969_CR35 14969_CR4 14969_CR34 14969_CR1 14969_CR33 14969_CR2 14969_CR32 14969_CR31 14969_CR30 UA Bhatti (14969_CR12) 2022; 29 14969_CR39 14969_CR37 AK Singh (14969_CR50) 2022; 2022 14969_CR36 KNLVN Prasad (14969_CR38) 2021; 198 UA Bhatti (14969_CR10) 2022; 288 14969_CR7 14969_CR5 14969_CR6 14969_CR46 14969_CR45 14969_CR44 14969_CR43 14969_CR41 14969_CR40 J Bohra (14969_CR14) 2018; 6 14969_CR49 14969_CR48 14969_CR47 CMT Bhavatarini (14969_CR13) 2020; 11 14969_CR54 14969_CR53 14969_CR52 14969_CR19 14969_CR18 14969_CR17 14969_CR16 14969_CR15 M Saberi Anari (14969_CR42) 2022; 2022 14969_CR24 14969_CR23 14969_CR22 14969_CR21 14969_CR20 UA Bhatti (14969_CR8) 2018; 14 14969_CR29 14969_CR28 14969_CR27 14969_CR26 14969_CR25 |
| References_xml | – reference: PrasadKNLVNKumarCSPSubediBAbrahaHBSathishkumarVERice leaf diseases prediction using deep neural networks with transfer learningEnviron Res202119811127510.1016/j.envres.2021.111275 – reference: Girshick R, Donahue J, Darrell T, Malik J (2013) “Rich feature hierarchies for accurate object detection and semantic segmentation,” Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit, https://doi.org/10.1109/CVPR.2014.81 – reference: Maniyath SR et al (2018) “Plant disease detection using machine learning,”https://doi.org/10.1109/ICDI3C.2018.00017 – reference: Cynthia ST, Shahrukh Hossain KM, Hasan MN, Asaduzzaman M, Das AK (2019) “Automated detection of plant diseases using image processing and faster R-CNN algorithm,”https://doi.org/10.1109/STI47673.2019.9068092 – reference: Abduljabbar R, Dia H, Liyanage S, Bagloee SA (2019) “Applications of artificial intelligence in transport: An overview,” Sustain (Switzerland), https://doi.org/10.3390/su11010189 – reference: Anjna, MS, Singh PK (2020) “Hybrid System for Detection and Classification of Plant Disease Using Qualitative Texture Features Analysis,”https://doi.org/10.1016/j.procs.2020.03.404 – reference: Sandika B, Avil S, Sanat S, Srinivasu P (2016) “Random forest based classification of diseases in grapes from images captured in uncontrolled environments,”https://doi.org/10.1109/ICSP.2016.7878133 – reference: BhattiUAYuZHasnainANawazSAYuanLWenLBhattiMAEvaluating the impact of roads on the diversity pattern and density of trees to improve the conservation of speciesEnviron Sci Pollut Res20222910147801479010.1007/s11356-021-16627-y – reference: Girshick R (2015) “Fast R-CNN,”\, https://doi.org/10.1109/ICCV.2015.169 – reference: Chen J, Zhang D, Nanehkaran Y, Li D (2020) “Detection of rice plant diseases based on deep transfer learning,” J Sci Food Agric, vol. 100, https://doi.org/10.1002/jsfa.10365 – reference: Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst. https://doi.org/10.48550/arXiv.1506.01497 – reference: Ginanni K (2004) Peace Corps' information collection and exchange. Ser Rev 30(3):249–251 – reference: Shruthi U, Nagaveni V, Raghavendra BK (2019) “A review on machine learning classification techniques for plant disease detection,”https://doi.org/10.1109/ICACCS.2019.8728415 – reference: Barbosa A, Trevisan R, Hovakimyan N, Martin NF (2020) “Modeling yield response to crop management using convolutional neural networks,” Comput Electron Agric, https://doi.org/10.1016/j.compag.2019.105197 – reference: BhavatariniCMTPriyadharshiniVRadhakrishnanMA machine learning approach for plant disease classification and pesticides suggestion using rank based attribute selectionInt J Sci Eng Res202011310211024 – reference: SinghAKSreenivasuSVNMahalaxmiUSBKSharmaHPatilDDAsensoEHybrid feature-based disease detection in plant leaf using convolutional neural network, Bayesian optimized SVM, and random Forest classifierJ Food Qual202220222845320284531610.1155/2022/2845320 – reference: Baranwal S, Khandelwal S, Arora A (2019) “Deep learning convolutional neural network for apple leaves disease detection,” SSRN Electron J, https://doi.org/10.2139/ssrn.3351641 – reference: Chokey T, Jain S (2019) “Quality assessment of crops using machine learning techniques,”https://doi.org/10.1109/AICAI.2019.8701294 – reference: Hasan MJ, Mahbub S, Alom MS, Abu Nasim M (2019) “Rice disease identification and classification by integrating support vector machine with deep convolutional neural network,”https://doi.org/10.1109/ICASERT.2019.8934568 – reference: Bari BS, Islam MN, Rashid M, Hasan MJ, Razman MAM, Musa RM, Ab Nasir AF, Abdul PP, Majeed A (2021) A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework. PeerJ Comput Sci 7:e432–e432. https://doi.org/10.7717/peerj-cs.432 – reference: Shahriar SA, Imtiaz AA, Hossain MB, Husna A, Eaty MNK (2020) “Review: Rice blast disease,” Annu Res Rev Biol, https://doi.org/10.9734/arrb/2020/v35i130180 – reference: BhattiUAHuangMWuDZhangYMehmoodAHanHRecommendation system using feature extraction and pattern recognition in clinical care systemsEnterp Inf Syst201913332935110.1080/17517575.2018.1557256 – reference: Ahmed K, Shahidi TR, Irfanul Alam SM, Momen S (2019) “Rice leaf disease detection using machine learning techniques,”https://doi.org/10.1109/STI47673.2019.9068096 – reference: Singh R, Singh GS (2017) “Traditional agriculture: a climate-smart approach for sustainable food production,” Energy Ecol Environ, https://doi.org/10.1007/s40974-017-0074-7 – reference: “Faster R-CNN | ML,” (2020) geeksforgeeks – reference: Tian K, Li J, Zeng J, Evans A, Zhang L (2019) “Segmentation of tomato leaf images based on adaptive clustering number of K-means algorithm,” Comput Electron Agric, https://doi.org/10.1016/j.compag.2019.104962 – reference: Naqvi SAH (2019) “Bacterial Leaf Blight of Rice: An Overview of Epidemiology and Management with Special Reference to-Indian-Sub-Continent,” Pakistan J Agric Res, https://doi.org/10.17582/journal.pjar/2019/32.2.359.380 – reference: de Bigirimana VP, Hua GKH, Nyamangyoku OI, Hòfte M (2015) “Rice sheath rot: An emerging ubiquitous destructive disease complex,” Front Plant Sci, https://doi.org/10.3389/fpls.2015.01066 – reference: Kamal KC, Yin Z, Wu M, Wu Z (2019) “Depthwise separable convolution architectures for plant disease classification,” Comput Electron Agric, https://doi.org/10.1016/j.compag.2019.104948 – reference: BhattiUALocal Similarity-Based Spatial-Spectral Fusion Hyperspectral Image Classification With Deep CNN and Gabor FilteringIEEE Trans Geosci Remote Sens20226011510.1109/TGRS.2021.3090410 – reference: Prajapati HB, Shah JP, Dabhi VK (2017) “Detection and classification of rice plant diseases,” Intell Decis Technol, https://doi.org/10.3233/IDT-170301 – reference: Johannes A et al (2017) “Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case,” Comput Electron Agric https://doi.org/10.1016/j.compag.2017.04.013 – reference: BhattiUAZeeshanZNizamaniMMBazaiSYuZYuanLAssessing the change of ambient air quality patterns in Jiangsu Province of China pre-to post-COVID-19Chemosphere202228813256910.1016/j.chemosphere.2021.132569 – reference: Kim WS, Lee DH, Kim YJ (2020) “Machine vision-based automatic disease symptom detection of onion downy mildew,” Comput Electron Agric, https://doi.org/10.1016/j.compag.2019.105099 – reference: Nanjundan J et al (2020) “Identification of new source of resistance to powdery mildew of indian mustard and studying its inheritance,” Plant Pathol J, https://doi.org/10.5423/PPJ.OA.07.2019.0205 – reference: Kusumo BS, Heryana A, Mahendra O, Pardede HF (2019) “Machine learning-based for automatic detection of Corn-Plant diseases using image processing,” https://doi.org/10.1109/IC3INA.2018.8629507 – reference: Lee SH, Goëau H, Bonnet P, Joly A (2020) “New perspectives on plant disease characterization based on deep learning,” Comput Electron Agric, https://doi.org/10.1016/j.compag.2020.105220 – reference: BhattiUAHuangMWangHZhangYMehmoodADiWRecommendation system for immunization coverage and monitoringHum Vaccin Immunother201814116517110.1080/21645515.2017.1379639 – reference: Russakovsky O et al (2015) “ImageNet large scale visual recognition challenge,” Int J Comput Vis, https://doi.org/10.1007/s11263-015-0816-y – reference: Verma G, Taluja C, Saxena AK (2019) “Vision based detection and classification of disease on Rice crops using convolutional neural network,” https://doi.org/10.1109/ICon-CuTE47290.2019.8991476 – reference: Ramesh S, Vydeki D (2018) “Rice blast disease detection and classification using machine learning algorithm,”https://doi.org/10.1109/ICMETE.2018.00063 – reference: SinhaKGhoshalDBhuniaNRice leaf disease classification using transfer learningLect Notes Networks Syst202237546747510.1007/978-981-16-8763-1_38 – reference: Singh R, Sunder S, Kumar P (2016) Sheath blight of rice: current status and perspectives. Indian Phytopathol 69(4):340–351 – reference: Lu Y, Yi S, Zeng N, Liu Y, Zhang Y (2017) “Identification of rice diseases using deep convolutional neural networks,” Neurocomputing, https://doi.org/10.1016/j.neucom.2017.06.023 – reference: Arun Pandian J, Geetharamani G, Annette B (2019) “Data augmentation on plant leaf disease image dataset using image manipulation and deep learning techniques,”https://doi.org/10.1109/IACC48062.2019.8971580 – reference: Govardhan M, Veena MB (2019) “Diagnosis of tomato plant diseases using random Forest,”https://doi.org/10.1109/GCAT47503.2019.8978431 – reference: Truong T, Dinh A, Wahid K (2017) “An IoT environmental data collection system for fungal detection in crop fields,”https://doi.org/10.1109/CCECE.2017.7946787 – reference: Chaudhari AK, Rakholiya KB, Baria TT (2019) “Epidemiological Study of False Smut of Rice (Oryza sativa L.) in Gujarat,” Int J Curr Microbiol Appl Sci, https://doi.org/10.20546/ijcmas.2019.806.337 – reference: Ferentinos KP (2018) “Deep learning models for plant disease detection and diagnosis,” Comput Electron Agric, https://doi.org/10.1016/j.compag.2018.01.009 – reference: Sharif M, Khan MA, Iqbal Z, Azam MF, Lali MIU, Javed MY (2018) “Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection,” Comput Electron Agric https://doi.org/10.1016/j.compag.2018.04.023 – reference: Saberi AnariMA hybrid model for leaf diseases classification based on the modified deep transfer learning and ensemble approach for agricultural AIoT-based monitoringComput Intell Neurosci20222022650461610.1155/2022/6504616 – reference: Sharma P, Hans P, Gupta SC (2020) “Classification of plant leaf diseases using machine learning and image preprocessing techniques,”, https://doi.org/10.1109/Confluence47617.2020.9057889 – reference: Chopda J, Raveshiya H, Nakum S, Nakrani V (2018) “Cotton crop disease detection using decision tree classifier,”https://doi.org/10.1109/ICSCET.2018.8537336 – reference: BohraJSadhukhanVPRManagement of brown spot disease in rice (Helminthosporium oryzae) by spraying of cow urineInt J Chem Stud20186117211723 – ident: 14969_CR47 doi: 10.1109/ICACCS.2019.8728415 – ident: 14969_CR33 doi: 10.1016/j.neucom.2017.06.023 – ident: 14969_CR3 doi: 10.1016/j.procs.2020.03.404 – ident: 14969_CR22 doi: 10.1016/j.compag.2018.01.009 – ident: 14969_CR15 doi: 10.20546/ijcmas.2019.806.337 – ident: 14969_CR25 doi: 10.1109/CVPR.2014.81 – ident: 14969_CR17 doi: 10.1109/AICAI.2019.8701294 – ident: 14969_CR45 doi: 10.1016/j.compag.2018.04.023 – ident: 14969_CR46 doi: 10.1109/Confluence47617.2020.9057889 – volume: 29 start-page: 14780 issue: 10 year: 2022 ident: 14969_CR12 publication-title: Environ Sci Pollut Res doi: 10.1007/s11356-021-16627-y – volume: 60 start-page: 1 year: 2022 ident: 14969_CR11 publication-title: IEEE Trans Geosci Remote Sens doi: 10.1109/TGRS.2021.3090410 – ident: 14969_CR28 doi: 10.1016/j.compag.2017.04.013 – ident: 14969_CR16 doi: 10.1002/jsfa.10365 – ident: 14969_CR44 doi: 10.9734/arrb/2020/v35i130180 – ident: 14969_CR34 doi: 10.1109/ICDI3C.2018.00017 – ident: 14969_CR43 doi: 10.1109/ICSP.2016.7878133 – ident: 14969_CR48 doi: 10.1007/s40974-017-0074-7 – volume: 6 start-page: 1721 issue: 1 year: 2018 ident: 14969_CR14 publication-title: Int J Chem Stud – ident: 14969_CR5 doi: 10.2139/ssrn.3351641 – volume: 198 start-page: 111275 year: 2021 ident: 14969_CR38 publication-title: Environ Res doi: 10.1016/j.envres.2021.111275 – ident: 14969_CR1 doi: 10.3390/su11010189 – ident: 14969_CR26 doi: 10.1109/GCAT47503.2019.8978431 – ident: 14969_CR4 doi: 10.1109/IACC48062.2019.8971580 – ident: 14969_CR27 doi: 10.1109/ICASERT.2019.8934568 – ident: 14969_CR37 doi: 10.3233/IDT-170301 – ident: 14969_CR35 doi: 10.5423/PPJ.OA.07.2019.0205 – ident: 14969_CR2 doi: 10.1109/STI47673.2019.9068096 – volume: 288 start-page: 132569 year: 2022 ident: 14969_CR10 publication-title: Chemosphere doi: 10.1016/j.chemosphere.2021.132569 – ident: 14969_CR53 doi: 10.1109/CCECE.2017.7946787 – ident: 14969_CR32 doi: 10.1016/j.compag.2020.105220 – volume: 13 start-page: 329 issue: 3 year: 2019 ident: 14969_CR9 publication-title: Enterp Inf Syst doi: 10.1080/17517575.2018.1557256 – ident: 14969_CR49 – ident: 14969_CR40 doi: 10.48550/arXiv.1506.01497 – ident: 14969_CR7 doi: 10.7717/peerj-cs.432 – ident: 14969_CR24 doi: 10.1109/ICCV.2015.169 – ident: 14969_CR41 doi: 10.1007/s11263-015-0816-y – ident: 14969_CR54 doi: 10.1109/ICon-CuTE47290.2019.8991476 – ident: 14969_CR39 doi: 10.1109/ICMETE.2018.00063 – volume: 2022 start-page: 2845320 year: 2022 ident: 14969_CR50 publication-title: J Food Qual doi: 10.1155/2022/2845320 – ident: 14969_CR18 doi: 10.1109/ICSCET.2018.8537336 – ident: 14969_CR20 doi: 10.3389/fpls.2015.01066 – ident: 14969_CR52 doi: 10.1016/j.compag.2019.104962 – volume: 14 start-page: 165 issue: 1 year: 2018 ident: 14969_CR8 publication-title: Hum Vaccin Immunother doi: 10.1080/21645515.2017.1379639 – ident: 14969_CR6 doi: 10.1016/j.compag.2019.105197 – ident: 14969_CR30 doi: 10.1016/j.compag.2019.105099 – ident: 14969_CR19 doi: 10.1109/STI47673.2019.9068092 – volume: 11 start-page: 1021 issue: 3 year: 2020 ident: 14969_CR13 publication-title: Int J Sci Eng Res – ident: 14969_CR23 doi: 10.1080/00987913.2004.10764916 – volume: 375 start-page: 467 year: 2022 ident: 14969_CR51 publication-title: Lect Notes Networks Syst doi: 10.1007/978-981-16-8763-1_38 – volume: 2022 start-page: 6504616 year: 2022 ident: 14969_CR42 publication-title: Comput Intell Neurosci doi: 10.1155/2022/6504616 – ident: 14969_CR31 doi: 10.1109/IC3INA.2018.8629507 – ident: 14969_CR21 – ident: 14969_CR29 doi: 10.1016/j.compag.2019.104948 – ident: 14969_CR36 doi: 10.17582/journal.pjar/2019/32.2.359.380 |
| SSID | ssj0016524 |
| Score | 2.4771135 |
| Snippet | Plant leaf disease detection is critical for long-term agricultural viability. Numerous Artificial Intelligence (AI) and Machine Learning (ML) technologies... |
| SourceID | proquest crossref springer |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 36091 |
| SubjectTerms | Artificial intelligence Blight Computer Communication Networks Computer Science Data Structures and Information Theory Deep learning Machine learning Multimedia Information Systems Plant diseases Predictions Special Purpose and Application-Based Systems |
| SummonAdditionalLinks | – databaseName: ProQuest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV09T8MwED1BWWDgG1EoyAMbWMSJnSYTAtQKMSCEQOoWOf4oQ0kLDUP_Pb7EIYBEV8fxkOfzXXx37wGcCaEiJrWgLJQB5ZF1NpfyPl4J6CAyGgvesdriIb574fcjMfIXbnNfVtmcidVBracK78gvQyRmE0kixNXsnaJqFGZXvYTGKqy5IzhJOrB2M3h4fPrOI8TCy9omAXW-kfm2mbp5jmFrivNZ1P0lxCld_HZNbbz5J0VaeZ7hNmz6kJFc1xjvwIopdmGrkWMg3jp3YeMHt-AejK8_y2nFx0oMkhgTbcqq7KogU0uQSohMjLTEZ2jmBCvgx-R1gS1cbrKZEa8oMSay0OStqro07WCtPT3fh5fh4Pn2jnpVBepQSUtqVZBLGyrG-9zBEdmA50g6z43OMZgQPA6NValNcMB5eJnqhItA65DbKLDRAXSKaWEOgVjJVMQjmeZK8L5VLpqTLHaT81z0uWRdYM0HzZSnHEfli0nWkiUjCJkDIatAyBZdOP9-Z1YTbiyd3WtwyrzxzbN2q3ThosGuffz_akfLVzuGdRSbryvMetApPz7NiQtJyvzU77svAIbdJA priority: 102 providerName: ProQuest |
| Title | Automatic early detection of rice leaf diseases using hybrid deep learning and machine learning methods |
| URI | https://link.springer.com/article/10.1007/s11042-023-14969-y https://www.proquest.com/docview/2866558855 |
| Volume | 82 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV07T8MwED7xWGDgjSiUygMbRIoTO4-xRU0RSBVCVIIpcmK7DJBWJAz99_hShwACJCZLzsVDPjt3yX33HcAZ57lPheQO9YTrMF-bMxezEH8JSNdXEgnvyLYYB1cTdv3AH2xRWNmw3ZuUZP2mbovdKJaSGB_jmKg-iJ3FKqxz8-2ORK6J1__IHQTctrKNXMf4Q2pLZX5e46s7amPMb2nR2tskO7Blw0TSX-K6Cyuq2IPtpgUDsSdyDzY_6Qnuw7T_Vs1qDVaiULiYSFXVVKuCzDRB-SDyrIQmNitTEmS9T8nTAsu2jLGaE9tFYkpEIclLzbRU7eSy33R5AJNkeH955dhOCo5BIq4cnbuZ0F5OWcgMBL52WYZC80zJDAMIzgJP6TzWEU4Yry5iGTHuSukx7bvaP4S1YlaoIyBa0NxnvoiznLNQ5yaCEzQwxlnGQyZoB2jzQNPcyoxjt4vntBVIRhBSA0Jag5AuOnD-cc98KbLxp3W3wSm1B65MPdTt41HEeQcuGuzay7-vdvw_8xPYwIbzS5ZZF9aq1zd1asKSKuvBapSMerDeTwaDMY6jx5uhGQfD8e1dr96j72Y63qU |
| linkProvider | Springer Nature |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LT9wwEB5ReqA9QEuLunTb-gAnsBrHdjY5IIRot8vzBBK31PFje6DZhc2q2j_V31hP4hBAKjeujmNZnrFn7Jn5PoAtKTVnykjKYhVRwZ3fc5kY4JOAibg1mPCO2RbnyehSHF_JqyX429bCYFpleybWB7WZaHwj_xojMJtMUyn3pzcUWaMwutpSaDRqcWIXf_yVbbZ39M3LdzuOh98vDkc0sApQP6usok5HhXKxZmIg_HS4i0SBoOvCmgKNqRRJbJ3OXIoN3sKpzKRCRsbEwvHIcT_uC3gpuLfkWJk-_HEXtUhkINFNI-otMQtFOk2pHsNCGG8hqb-TJBldPDSEnXf7KCBb27nhG1gNDio5aDTqLSzZch3WWvIHEs6CdXh9D8nwHYwP5tWkRn8lFiGTibFVneRVkokjCFxErq1yJMSDZgTz7cfk1wILxnxnOyWBv2JMVGnI7zrH03aNDdP17D1cPstqb8ByOSntByBOMc0FV1mhpRg47X1HxRLfuSjkQCjWA9YuaK4DwDnybFznHTQzCiH3QshrIeSLHuzc_TNt4D2e7N1v5ZSHrT7LO8XswW4ru-7z_0fbfHq0L7Ayujg7zU-Pzk8-wiukuW9y2_qwXN3O7SfvDFXF51oDCfx8bpX_BxqiGMc |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LT9wwEB7RRarKoTxaxLZAfaCn1iJO7DwOCPFa8dIKVUXiljp-LAfILmxQtX-tv66exCEFqdy4Oo5lecaesWfm-wC2hFARk1pQFsqA8si6PZfxBJ8EdBAZjQnvmG0xjI8v-emVuJqDP20tDKZVtmdifVDrscI38u0QgdlEmgqxbX1axMXhYHdyR5FBCiOtLZ1GoyJnZvbbXd-mOyeHTtZfw3Bw9PPgmHqGAepmmFXUqqCQNlSMJ9xNLbIBLxCAnRtdoGEVPA6NVZlNscFZO5nplItA65DbKLCRG_cNzCd4K-rB_P7R8OLHYwwjFp5SNw2os8vMl-w0hXsMy2KcvaTuhhJndPbULHa-7rPwbG31Bkvw3rurZK_Rr2WYM-UKLLZUEMSfDCuw8A-u4QcY7T1U4xoLlhgEUCbaVHXKV0nGliCMEbkx0hIfHZoSzL4fkesZlo-5zmZCPJvFiMhSk9s649N0jQ3v9fQjXL7Keq9CrxyXZg2IlUxFPJJZoQRPrHKepGSx61wUIuGS9YG1C5orD3eOrBs3eQfUjELInRDyWgj5rA_fHv-ZNGAfL_Zeb-WU-40_zTs17cP3Vnbd5_-P9unl0b7AW6fu-fnJ8OwzvEPO-ybRbR161f2D2XCeUVVsehUk8Ou1tf4v6O4eWQ |
| 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=Automatic+early+detection+of+rice+leaf+diseases+using+hybrid+deep+learning+and+machine+learning+methods&rft.jtitle=Multimedia+tools+and+applications&rft.au=Rajpoot%2C+Vikram&rft.au=Tiwari%2C+Akhilesh&rft.au=Jalal%2C+Anand+Singh&rft.date=2023-09-01&rft.issn=1380-7501&rft.eissn=1573-7721&rft.volume=82&rft.issue=23&rft.spage=36091&rft.epage=36117&rft_id=info:doi/10.1007%2Fs11042-023-14969-y&rft.externalDBID=n%2Fa&rft.externalDocID=10_1007_s11042_023_14969_y |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1380-7501&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1380-7501&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1380-7501&client=summon |