Hyberparameters of CNN architectures.
<div><p>The early identification of pests and diseases in crops now presents a significant challenge. Different methods have been used to resolve this problem. Sticky traps and black light traps, used to identify diseases and for field monitoring, are examples of a manual procedure for a...
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| المؤلف الرئيسي: | |
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| منشور في: |
2024
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| الموضوعات: | |
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| _version_ | 1852024976946757632 |
|---|---|
| author | Abdullah Sheneamer (19169236) |
| author_facet | Abdullah Sheneamer (19169236) |
| author_role | author |
| dc.creator.none.fl_str_mv | Abdullah Sheneamer (19169236) |
| dc.date.none.fl_str_mv | 2024-11-22T18:23:25Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0313607.t001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Hyberparameters_of_CNN_architectures_/27891407 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biotechnology Ecology Infectious Diseases Biological Sciences not elsewhere classified transfer learning strategies plant village dataset material upon contact farmers spray pesticides black light traps average disease count specific time period including image processing different picture attributes sticky traps image processing different methods disease monitoring disease identification xlink "> various illnesses various approaches sticky trap sophisticated algorithms significant challenge serve requires safe samples preventative measure picture using pet density one method manual procedure manual methods less effective large quantities field monitoring effective method early identification early detection classification algorithms categorised based bugs stick achieved accuracy |
| dc.title.none.fl_str_mv | Hyberparameters of CNN architectures. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>The early identification of pests and diseases in crops now presents a significant challenge. Different methods have been used to resolve this problem. Sticky traps and black light traps, used to identify diseases and for field monitoring, are examples of a manual procedure for analysing the diseases. A lot of time is required, and it is less effective to manually inspect larger crop fields manually. To serve requires a professional, so it is, therefore, costly. The use of sticky traps, where by bugs stick to the material upon contact, is one method of disease monitoring. A camera is used to take a picture of the sticky trap. From the picture using the average disease count, this image is then processed to ascertain the pet density for a specific time period. Such manual methods, as well as providing an effective outcome also pose a danger to the environment. This is because farmers spray pesticides in large quantities as a preventative measure. Various approaches have been used to identify diseases, including image processing and sophisticated algorithms. The most effective method of disease identification from crops is automatic detection using methods of image processing and classification algorithms for the diseases to be categorised based on different picture attributes. With a stacking stacking hybrid learning with scratch and transfer learning strategies, which is utilised in this work, a model that has already been trained is used to learn on images of diverse fruit plant leaves from the Plant Village dataset, spanning both safe samples and various illnesses. This reasearch paper used ensemble CNN and we achieved accuracy between 99.75% to 100%.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_ab5ea31b2d16ed16f08028f3d2e71280 |
| identifier_str_mv | 10.1371/journal.pone.0313607.t001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/27891407 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Hyberparameters of CNN architectures.Abdullah Sheneamer (19169236)BiotechnologyEcologyInfectious DiseasesBiological Sciences not elsewhere classifiedtransfer learning strategiesplant village datasetmaterial upon contactfarmers spray pesticidesblack light trapsaverage disease countspecific time periodincluding image processingdifferent picture attributessticky trapsimage processingdifferent methodsdisease monitoringdisease identificationxlink ">various illnessesvarious approachessticky trapsophisticated algorithmssignificant challengeserve requiressafe samplespreventative measurepicture usingpet densityone methodmanual proceduremanual methodsless effectivelarge quantitiesfield monitoringeffective methodearly identificationearly detectionclassification algorithmscategorised basedbugs stickachieved accuracy<div><p>The early identification of pests and diseases in crops now presents a significant challenge. Different methods have been used to resolve this problem. Sticky traps and black light traps, used to identify diseases and for field monitoring, are examples of a manual procedure for analysing the diseases. A lot of time is required, and it is less effective to manually inspect larger crop fields manually. To serve requires a professional, so it is, therefore, costly. The use of sticky traps, where by bugs stick to the material upon contact, is one method of disease monitoring. A camera is used to take a picture of the sticky trap. From the picture using the average disease count, this image is then processed to ascertain the pet density for a specific time period. Such manual methods, as well as providing an effective outcome also pose a danger to the environment. This is because farmers spray pesticides in large quantities as a preventative measure. Various approaches have been used to identify diseases, including image processing and sophisticated algorithms. The most effective method of disease identification from crops is automatic detection using methods of image processing and classification algorithms for the diseases to be categorised based on different picture attributes. With a stacking stacking hybrid learning with scratch and transfer learning strategies, which is utilised in this work, a model that has already been trained is used to learn on images of diverse fruit plant leaves from the Plant Village dataset, spanning both safe samples and various illnesses. This reasearch paper used ensemble CNN and we achieved accuracy between 99.75% to 100%.</p></div>2024-11-22T18:23:25ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0313607.t001https://figshare.com/articles/dataset/Hyberparameters_of_CNN_architectures_/27891407CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/278914072024-11-22T18:23:25Z |
| spellingShingle | Hyberparameters of CNN architectures. Abdullah Sheneamer (19169236) Biotechnology Ecology Infectious Diseases Biological Sciences not elsewhere classified transfer learning strategies plant village dataset material upon contact farmers spray pesticides black light traps average disease count specific time period including image processing different picture attributes sticky traps image processing different methods disease monitoring disease identification xlink "> various illnesses various approaches sticky trap sophisticated algorithms significant challenge serve requires safe samples preventative measure picture using pet density one method manual procedure manual methods less effective large quantities field monitoring effective method early identification early detection classification algorithms categorised based bugs stick achieved accuracy |
| status_str | publishedVersion |
| title | Hyberparameters of CNN architectures. |
| title_full | Hyberparameters of CNN architectures. |
| title_fullStr | Hyberparameters of CNN architectures. |
| title_full_unstemmed | Hyberparameters of CNN architectures. |
| title_short | Hyberparameters of CNN architectures. |
| title_sort | Hyberparameters of CNN architectures. |
| topic | Biotechnology Ecology Infectious Diseases Biological Sciences not elsewhere classified transfer learning strategies plant village dataset material upon contact farmers spray pesticides black light traps average disease count specific time period including image processing different picture attributes sticky traps image processing different methods disease monitoring disease identification xlink "> various illnesses various approaches sticky trap sophisticated algorithms significant challenge serve requires safe samples preventative measure picture using pet density one method manual procedure manual methods less effective large quantities field monitoring effective method early identification early detection classification algorithms categorised based bugs stick achieved accuracy |