Matches in SemOpenAlex for { <https://semopenalex.org/work/W4385215439> ?p ?o ?g. }
Showing items 1 to 87 of
87
with 100 items per page.
- W4385215439 endingPage "390" @default.
- W4385215439 startingPage "373" @default.
- W4385215439 abstract "Bacteria, viruses, and fungus may inhibit plant growth. Hence, early illness detection and prevention are crucial. Today, many machine learning methods are being used to identify and classify rice plant illnesses, but as technology advances, deep learning methods, using DL algorithms, are becoming more efficient and accurate. DL provides detailed segmentation analysis. Experts first examined and analyzed plant diseases without technology. This requires more effort and more costly processing time. Deep learning image processing can identify and categorize rice plant illnesses. Due to the massive agriculture business, physically evaluating rice plants was impossible. It automatically identifies and categorizes plant diseases. In addition, research needs are highlighted to improve plant disease diagnosis and early symptom detection. The report also discusses rice deficiency judgment's existing successes, limits, and future research. Pre-processing, photo segmentation, feature extraction, and classification algorithms are used to identify damaged rice plant leaves. The CNN algorithm is a deep learning approach. It has solved computer vision problems including picture categorization, object segmentation, image analysis, and more. This work employed GoogleNet with CNN model and transfer learning to detect diseases in rice leaf pictures. Optimizing the proposed model for categorization yielded high accuracy for big 97.60% and small 96.80%. GoogleNet with CNN outperforms competing algorithms in accuracy and exactness for both large and small datasets." @default.
- W4385215439 created "2023-07-25" @default.
- W4385215439 creator A5022026406 @default.
- W4385215439 creator A5044787255 @default.
- W4385215439 creator A5061793649 @default.
- W4385215439 date "2023-01-01" @default.
- W4385215439 modified "2023-09-27" @default.
- W4385215439 title "An Automated System for Rice Plant Diagnosis Using Deep Learning" @default.
- W4385215439 cites W2548247561 @default.
- W4385215439 cites W2569479441 @default.
- W4385215439 cites W2610446774 @default.
- W4385215439 cites W2783621519 @default.
- W4385215439 cites W2790578909 @default.
- W4385215439 cites W2792879751 @default.
- W4385215439 cites W2796750794 @default.
- W4385215439 cites W2895319015 @default.
- W4385215439 cites W2910688461 @default.
- W4385215439 cites W2938366133 @default.
- W4385215439 cites W2945197573 @default.
- W4385215439 cites W2994112889 @default.
- W4385215439 cites W3006924826 @default.
- W4385215439 cites W3015562698 @default.
- W4385215439 cites W3033196717 @default.
- W4385215439 cites W3117334264 @default.
- W4385215439 cites W3155728539 @default.
- W4385215439 cites W4283715040 @default.
- W4385215439 cites W4313270469 @default.
- W4385215439 doi "https://doi.org/10.1007/978-981-99-2100-3_30" @default.
- W4385215439 hasPublicationYear "2023" @default.
- W4385215439 type Work @default.
- W4385215439 citedByCount "0" @default.
- W4385215439 crossrefType "book-chapter" @default.
- W4385215439 hasAuthorship W4385215439A5022026406 @default.
- W4385215439 hasAuthorship W4385215439A5044787255 @default.
- W4385215439 hasAuthorship W4385215439A5061793649 @default.
- W4385215439 hasConcept C108583219 @default.
- W4385215439 hasConcept C115961682 @default.
- W4385215439 hasConcept C119857082 @default.
- W4385215439 hasConcept C124504099 @default.
- W4385215439 hasConcept C150903083 @default.
- W4385215439 hasConcept C153180895 @default.
- W4385215439 hasConcept C154945302 @default.
- W4385215439 hasConcept C2776151529 @default.
- W4385215439 hasConcept C2992726227 @default.
- W4385215439 hasConcept C3019235130 @default.
- W4385215439 hasConcept C41008148 @default.
- W4385215439 hasConcept C52622490 @default.
- W4385215439 hasConcept C6557445 @default.
- W4385215439 hasConcept C86803240 @default.
- W4385215439 hasConcept C89600930 @default.
- W4385215439 hasConcept C94124525 @default.
- W4385215439 hasConcept C9417928 @default.
- W4385215439 hasConceptScore W4385215439C108583219 @default.
- W4385215439 hasConceptScore W4385215439C115961682 @default.
- W4385215439 hasConceptScore W4385215439C119857082 @default.
- W4385215439 hasConceptScore W4385215439C124504099 @default.
- W4385215439 hasConceptScore W4385215439C150903083 @default.
- W4385215439 hasConceptScore W4385215439C153180895 @default.
- W4385215439 hasConceptScore W4385215439C154945302 @default.
- W4385215439 hasConceptScore W4385215439C2776151529 @default.
- W4385215439 hasConceptScore W4385215439C2992726227 @default.
- W4385215439 hasConceptScore W4385215439C3019235130 @default.
- W4385215439 hasConceptScore W4385215439C41008148 @default.
- W4385215439 hasConceptScore W4385215439C52622490 @default.
- W4385215439 hasConceptScore W4385215439C6557445 @default.
- W4385215439 hasConceptScore W4385215439C86803240 @default.
- W4385215439 hasConceptScore W4385215439C89600930 @default.
- W4385215439 hasConceptScore W4385215439C94124525 @default.
- W4385215439 hasConceptScore W4385215439C9417928 @default.
- W4385215439 hasLocation W43852154391 @default.
- W4385215439 hasOpenAccess W4385215439 @default.
- W4385215439 hasPrimaryLocation W43852154391 @default.
- W4385215439 hasRelatedWork W2112454231 @default.
- W4385215439 hasRelatedWork W2734888972 @default.
- W4385215439 hasRelatedWork W2790662084 @default.
- W4385215439 hasRelatedWork W4223943233 @default.
- W4385215439 hasRelatedWork W4285827401 @default.
- W4385215439 hasRelatedWork W4295854770 @default.
- W4385215439 hasRelatedWork W4312200629 @default.
- W4385215439 hasRelatedWork W4320731732 @default.
- W4385215439 hasRelatedWork W4380075502 @default.
- W4385215439 hasRelatedWork W4386549364 @default.
- W4385215439 isParatext "false" @default.
- W4385215439 isRetracted "false" @default.
- W4385215439 workType "book-chapter" @default.