Matches in SemOpenAlex for { <https://semopenalex.org/work/W4322727400> ?p ?o ?g. }
Showing items 1 to 68 of
68
with 100 items per page.
- W4322727400 endingPage "517" @default.
- W4322727400 startingPage "509" @default.
- W4322727400 abstract "One of the primary factors influencing agriculture crop yield production is the occurrence of plant diseases. Recent advancement in deep learning techniques has found their utilization in crop disease recognition which provides high accuracy. In this context, this paper represents a hybrid approach for an automatic plants disease classification model which utilizes a transfer learning neural network ensemble with a machine learning classifier. Transfer learning is an approved technique of deep learning where pre-trained architectures solve natural language processing problems or computer vision classification problems. This research work has performed a multi-class classification of distinct plant diseases on the plant Village dataset using VGG16 and ResNet50 (pre-trained deep learning models) ensembled with Random Forest Classifier. In this Hybrid approach, the last layers of the pre-trained deep learning neural networks are replaced by the Random Forest classifier, and a comparative analysis is performed with state-of-art algorithms, which depicts that Resnet50 ensembled with Random Forest provides an accuracy of 0.980. Also, comparing it with Traditional machine learning models, the accuracy for Random Forest on the same dataset is 0.927." @default.
- W4322727400 created "2023-03-03" @default.
- W4322727400 creator A5009422676 @default.
- W4322727400 creator A5087301326 @default.
- W4322727400 date "2023-01-01" @default.
- W4322727400 modified "2023-09-27" @default.
- W4322727400 title "A Hybrid Convolutional Neural Network–Random Forest Model for Plant Disease Diagnosis" @default.
- W4322727400 cites W2117305887 @default.
- W4322727400 cites W2194775991 @default.
- W4322727400 cites W2473156356 @default.
- W4322727400 cites W2550043609 @default.
- W4322727400 cites W2610446774 @default.
- W4322727400 cites W2895025043 @default.
- W4322727400 cites W2983575492 @default.
- W4322727400 cites W3006296545 @default.
- W4322727400 cites W3036085849 @default.
- W4322727400 cites W3082480853 @default.
- W4322727400 cites W3149839483 @default.
- W4322727400 cites W3158527823 @default.
- W4322727400 cites W3159001133 @default.
- W4322727400 cites W3167125732 @default.
- W4322727400 cites W3197906550 @default.
- W4322727400 cites W3215512454 @default.
- W4322727400 doi "https://doi.org/10.1007/978-981-19-7447-2_45" @default.
- W4322727400 hasPublicationYear "2023" @default.
- W4322727400 type Work @default.
- W4322727400 citedByCount "0" @default.
- W4322727400 crossrefType "book-chapter" @default.
- W4322727400 hasAuthorship W4322727400A5009422676 @default.
- W4322727400 hasAuthorship W4322727400A5087301326 @default.
- W4322727400 hasConcept C108583219 @default.
- W4322727400 hasConcept C119857082 @default.
- W4322727400 hasConcept C150899416 @default.
- W4322727400 hasConcept C154945302 @default.
- W4322727400 hasConcept C169258074 @default.
- W4322727400 hasConcept C41008148 @default.
- W4322727400 hasConcept C45942800 @default.
- W4322727400 hasConcept C50644808 @default.
- W4322727400 hasConcept C81363708 @default.
- W4322727400 hasConcept C95623464 @default.
- W4322727400 hasConceptScore W4322727400C108583219 @default.
- W4322727400 hasConceptScore W4322727400C119857082 @default.
- W4322727400 hasConceptScore W4322727400C150899416 @default.
- W4322727400 hasConceptScore W4322727400C154945302 @default.
- W4322727400 hasConceptScore W4322727400C169258074 @default.
- W4322727400 hasConceptScore W4322727400C41008148 @default.
- W4322727400 hasConceptScore W4322727400C45942800 @default.
- W4322727400 hasConceptScore W4322727400C50644808 @default.
- W4322727400 hasConceptScore W4322727400C81363708 @default.
- W4322727400 hasConceptScore W4322727400C95623464 @default.
- W4322727400 hasLocation W43227274001 @default.
- W4322727400 hasOpenAccess W4322727400 @default.
- W4322727400 hasPrimaryLocation W43227274001 @default.
- W4322727400 hasRelatedWork W2963958939 @default.
- W4322727400 hasRelatedWork W3158264953 @default.
- W4322727400 hasRelatedWork W3173182854 @default.
- W4322727400 hasRelatedWork W3192840557 @default.
- W4322727400 hasRelatedWork W4220785415 @default.
- W4322727400 hasRelatedWork W4286768673 @default.
- W4322727400 hasRelatedWork W4288084884 @default.
- W4322727400 hasRelatedWork W4310989423 @default.
- W4322727400 hasRelatedWork W4322727400 @default.
- W4322727400 hasRelatedWork W4366224123 @default.
- W4322727400 isParatext "false" @default.
- W4322727400 isRetracted "false" @default.
- W4322727400 workType "book-chapter" @default.