FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4316466202> ?p ?o ?g. }

• W4316466202 endingPage "260" @default.
• W4316466202 startingPage "260" @default.
• W4316466202 abstract "The timely and accurate identification of stripe rust and leaf rust is essential in effective disease control and the safe production of wheat worldwide. To investigate methods for identifying the two diseases on different wheat varieties based on image processing technology, single-leaf images of the diseases on different wheat varieties, acquired under field and laboratory environmental conditions, were processed. After image scaling, median filtering, morphological reconstruction, and lesion segmentation on the images, 140 color, texture, and shape features were extracted from the lesion images; then, feature selections were conducted using methods including ReliefF, 1R, correlation-based feature selection, and principal components analysis combined with support vector machine (SVM), back propagation neural network (BPNN), and random forest (RF), respectively. For the individual-variety disease identification SVM, BPNN, and RF models built with the optimal feature combinations, the identification accuracies of the training sets and the testing sets on the same individual varieties acquired under the same image acquisition conditions as the training sets used for modeling were 87.18–100.00%, but most of the identification accuracies of the testing sets for other individual varieties were low. For the multi-variety disease identification SVM, BPNN, and RF models built with the merged optimal feature combinations based on the multi-variety disease images acquired under field and laboratory environmental conditions, identification accuracies in the range of 82.05–100.00% were achieved on the training set, the corresponding multi-variety disease image testing set, and all the individual-variety disease image testing sets. The results indicated that the identification of images of stripe rust and leaf rust could be greatly affected by wheat varieties, but satisfactory identification performances could be achieved by building multi-variety disease identification models based on disease images from multiple varieties under different environments. This study provides an effective method for the accurate identification of stripe rust and leaf rust and could be a useful reference for the automatic identification of other plant diseases." @default.
• W4316466202 created "2023-01-16" @default.
• W4316466202 creator A5030865178 @default.
• W4316466202 creator A5039755388 @default.
• W4316466202 creator A5040239964 @default.
• W4316466202 creator A5044001009 @default.
• W4316466202 creator A5068494401 @default.
• W4316466202 date "2023-01-14" @default.
• W4316466202 modified "2023-10-01" @default.
• W4316466202 title "Identification of Stripe Rust and Leaf Rust on Different Wheat Varieties Based on Image Processing Technology" @default.
• W4316466202 cites W1808644423 @default.
• W4316466202 cites W2003007706 @default.
• W4316466202 cites W2003724897 @default.
• W4316466202 cites W2006274653 @default.
• W4316466202 cites W2012521303 @default.
• W4316466202 cites W2019610851 @default.
• W4316466202 cites W2033612021 @default.
• W4316466202 cites W2048491684 @default.
• W4316466202 cites W2057832886 @default.
• W4316466202 cites W2153635508 @default.
• W4316466202 cites W2162772680 @default.
• W4316466202 cites W2254178749 @default.
• W4316466202 cites W2277854822 @default.
• W4316466202 cites W2343197924 @default.
• W4316466202 cites W2561572938 @default.
• W4316466202 cites W2562201035 @default.
• W4316466202 cites W2585286277 @default.
• W4316466202 cites W2730129132 @default.
• W4316466202 cites W2765671382 @default.
• W4316466202 cites W2805772477 @default.
• W4316466202 cites W2891667148 @default.
• W4316466202 cites W2904855562 @default.
• W4316466202 cites W2915519176 @default.
• W4316466202 cites W2917559222 @default.
• W4316466202 cites W3080908680 @default.
• W4316466202 cites W3082480853 @default.
• W4316466202 cites W3116466548 @default.
• W4316466202 cites W3118209175 @default.
• W4316466202 cites W3141968773 @default.
• W4316466202 cites W3158308652 @default.
• W4316466202 cites W3158827491 @default.
• W4316466202 cites W3158842331 @default.
• W4316466202 cites W3161304287 @default.
• W4316466202 cites W3163327790 @default.
• W4316466202 cites W3165045544 @default.
• W4316466202 cites W3185992107 @default.
• W4316466202 cites W3201532693 @default.
• W4316466202 cites W4200563989 @default.
• W4316466202 cites W4210261474 @default.
• W4316466202 cites W4282047819 @default.
• W4316466202 cites W4283257278 @default.
• W4316466202 cites W4285004946 @default.
• W4316466202 doi "https://doi.org/10.3390/agronomy13010260" @default.
• W4316466202 hasPublicationYear "2023" @default.
• W4316466202 type Work @default.
• W4316466202 citedByCount "3" @default.
• W4316466202 countsByYear W43164662022023 @default.
• W4316466202 crossrefType "journal-article" @default.
• W4316466202 hasAuthorship W4316466202A5030865178 @default.
• W4316466202 hasAuthorship W4316466202A5039755388 @default.
• W4316466202 hasAuthorship W4316466202A5040239964 @default.
• W4316466202 hasAuthorship W4316466202A5044001009 @default.
• W4316466202 hasAuthorship W4316466202A5068494401 @default.
• W4316466202 hasBestOaLocation W43164662021 @default.
• W4316466202 hasConcept C115961682 @default.
• W4316466202 hasConcept C116834253 @default.
• W4316466202 hasConcept C12267149 @default.
• W4316466202 hasConcept C124504099 @default.
• W4316466202 hasConcept C138885662 @default.
• W4316466202 hasConcept C148483581 @default.
• W4316466202 hasConcept C153180895 @default.
• W4316466202 hasConcept C154945302 @default.
• W4316466202 hasConcept C169258074 @default.
• W4316466202 hasConcept C197781089 @default.
• W4316466202 hasConcept C199360897 @default.
• W4316466202 hasConcept C27438332 @default.
• W4316466202 hasConcept C2776401178 @default.
• W4316466202 hasConcept C41008148 @default.
• W4316466202 hasConcept C41895202 @default.
• W4316466202 hasConcept C50644808 @default.
• W4316466202 hasConcept C52622490 @default.
• W4316466202 hasConcept C59822182 @default.
• W4316466202 hasConcept C83665646 @default.
• W4316466202 hasConcept C86803240 @default.
• W4316466202 hasConcept C89600930 @default.
• W4316466202 hasConcept C9417928 @default.
• W4316466202 hasConceptScore W4316466202C115961682 @default.
• W4316466202 hasConceptScore W4316466202C116834253 @default.
• W4316466202 hasConceptScore W4316466202C12267149 @default.
• W4316466202 hasConceptScore W4316466202C124504099 @default.
• W4316466202 hasConceptScore W4316466202C138885662 @default.
• W4316466202 hasConceptScore W4316466202C148483581 @default.
• W4316466202 hasConceptScore W4316466202C153180895 @default.
• W4316466202 hasConceptScore W4316466202C154945302 @default.
• W4316466202 hasConceptScore W4316466202C169258074 @default.
• W4316466202 hasConceptScore W4316466202C197781089 @default.
• W4316466202 hasConceptScore W4316466202C199360897 @default.
• W4316466202 hasConceptScore W4316466202C27438332 @default.

• next