FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3204243169> ?p ?o ?g. }

• W3204243169 endingPage "524" @default.
• W3204243169 startingPage "507" @default.
• W3204243169 abstract "Abstract The agricultural production rate plays a pivotal role in the economic development of a country. However, plant diseases are the most significant impediment to the production and quality of food. The identification of plant diseases at an early stage is crucial for global health and wellbeing. The traditional diagnosis process involves visual assessment of an individual plant by a pathologist through on-site visits. However, manual examination for crop diseases is restricted because of less accuracy and the small accessibility of human resources. To tackle such issues, there is a demand to design automated approaches capable of efficiently detecting and categorizing numerous plant diseases. Precise identification and classification of plant diseases is a tedious job due because of the occurrence of low-intensity information in the image background and foreground, the huge color resemblance in the healthy and diseased plant areas, the occurrence of noise in the samples, and changes in the position, chrominance, structure, and size of plant leaves. To tackle the above-mentioned problems, we have introduced a robust plant disease classification system by introducing a Custom CenterNet framework with DenseNet-77 as a base network. The presented method follows three steps. In the first step, annotations are developed to get the region of interest. Secondly, an improved CenterNet is introduced in which DenseNet-77 is proposed for deep keypoints extraction. Finally, the one-stage detector CenterNet is used to detect and categorize several plant diseases. To conduct the performance analysis, we have used the PlantVillage Kaggle database, which is the standard dataset for plant diseases and challenges in terms of intensity variations, color changes, and differences found in the shapes and sizes of leaves. Both the qualitative and quantitative analysis confirms that the presented method is more proficient and reliable to identify and classify plant diseases than other latest approaches." @default.
• W3204243169 created "2021-10-11" @default.
• W3204243169 creator A5010686488 @default.
• W3204243169 creator A5037364292 @default.
• W3204243169 creator A5046972993 @default.
• W3204243169 creator A5053995495 @default.
• W3204243169 creator A5074765021 @default.
• W3204243169 date "2021-09-28" @default.
• W3204243169 modified "2023-10-12" @default.
• W3204243169 title "A novel deep learning method for detection and classification of plant diseases" @default.
• W3204243169 cites W1536680647 @default.
• W3204243169 cites W1861492603 @default.
• W3204243169 cites W1973445088 @default.
• W3204243169 cites W1977838479 @default.
• W3204243169 cites W1978331315 @default.
• W3204243169 cites W1995771589 @default.
• W3204243169 cites W2097117768 @default.
• W3204243169 cites W2130416988 @default.
• W3204243169 cites W2136922672 @default.
• W3204243169 cites W2162772680 @default.
• W3204243169 cites W2473156356 @default.
• W3204243169 cites W2511067925 @default.
• W3204243169 cites W2779798802 @default.
• W3204243169 cites W2789255992 @default.
• W3204243169 cites W2795016359 @default.
• W3204243169 cites W2886330227 @default.
• W3204243169 cites W2899663673 @default.
• W3204243169 cites W2901380936 @default.
• W3204243169 cites W2901609506 @default.
• W3204243169 cites W2907625092 @default.
• W3204243169 cites W2915770592 @default.
• W3204243169 cites W2938959907 @default.
• W3204243169 cites W2960854401 @default.
• W3204243169 cites W2963351448 @default.
• W3204243169 cites W2966160658 @default.
• W3204243169 cites W2979948002 @default.
• W3204243169 cites W2981318525 @default.
• W3204243169 cites W2987984071 @default.
• W3204243169 cites W2989604896 @default.
• W3204243169 cites W2990704421 @default.
• W3204243169 cites W3001224656 @default.
• W3204243169 cites W3010358965 @default.
• W3204243169 cites W3010621180 @default.
• W3204243169 cites W3013403470 @default.
• W3204243169 cites W3013480166 @default.
• W3204243169 cites W3016522735 @default.
• W3204243169 cites W3018328097 @default.
• W3204243169 cites W3029652632 @default.
• W3204243169 cites W3035982802 @default.
• W3204243169 cites W3036241636 @default.
• W3204243169 cites W3037528678 @default.
• W3204243169 cites W3095722810 @default.
• W3204243169 cites W3098283929 @default.
• W3204243169 cites W3106250896 @default.
• W3204243169 cites W3129478584 @default.
• W3204243169 cites W3136563782 @default.
• W3204243169 cites W3138345935 @default.
• W3204243169 cites W3145512623 @default.
• W3204243169 cites W3150039746 @default.
• W3204243169 cites W3176021848 @default.
• W3204243169 cites W3181256602 @default.
• W3204243169 cites W4213102919 @default.
• W3204243169 cites W639708223 @default.
• W3204243169 doi "https://doi.org/10.1007/s40747-021-00536-1" @default.
• W3204243169 hasPublicationYear "2021" @default.
• W3204243169 type Work @default.
• W3204243169 sameAs 3204243169 @default.
• W3204243169 citedByCount "52" @default.
• W3204243169 countsByYear W32042431692022 @default.
• W3204243169 countsByYear W32042431692023 @default.
• W3204243169 crossrefType "journal-article" @default.
• W3204243169 hasAuthorship W3204243169A5010686488 @default.
• W3204243169 hasAuthorship W3204243169A5037364292 @default.
• W3204243169 hasAuthorship W3204243169A5046972993 @default.
• W3204243169 hasAuthorship W3204243169A5053995495 @default.
• W3204243169 hasAuthorship W3204243169A5074765021 @default.
• W3204243169 hasBestOaLocation W32042431691 @default.
• W3204243169 hasConcept C111919701 @default.
• W3204243169 hasConcept C116834253 @default.
• W3204243169 hasConcept C150903083 @default.
• W3204243169 hasConcept C153180895 @default.
• W3204243169 hasConcept C154945302 @default.
• W3204243169 hasConcept C3019235130 @default.
• W3204243169 hasConcept C41008148 @default.
• W3204243169 hasConcept C59822182 @default.
• W3204243169 hasConcept C86803240 @default.
• W3204243169 hasConcept C94124525 @default.
• W3204243169 hasConcept C98045186 @default.
• W3204243169 hasConceptScore W3204243169C111919701 @default.
• W3204243169 hasConceptScore W3204243169C116834253 @default.
• W3204243169 hasConceptScore W3204243169C150903083 @default.
• W3204243169 hasConceptScore W3204243169C153180895 @default.
• W3204243169 hasConceptScore W3204243169C154945302 @default.
• W3204243169 hasConceptScore W3204243169C3019235130 @default.
• W3204243169 hasConceptScore W3204243169C41008148 @default.
• W3204243169 hasConceptScore W3204243169C59822182 @default.
• W3204243169 hasConceptScore W3204243169C86803240 @default.
• W3204243169 hasConceptScore W3204243169C94124525 @default.

• next