Matches in SemOpenAlex for { <https://semopenalex.org/work/W2280243008> ?p ?o ?g. }
Showing items 1 to 83 of
83
with 100 items per page.
- W2280243008 endingPage "46" @default.
- W2280243008 startingPage "41" @default.
- W2280243008 abstract "Expensive and laborious job of weed control can be facilitated if automatic weeding machines are employed. Site-specific managements of the weeds in the field need accurate discrimination between the crop and the weeds. There are distinct species of the weeds so called “common weeds” for cultivation in a specific region. Three species of the weeds commonly grow in corn fields are considered in this study, which are Convolvulus arvensis, Chenopodium album, and Amaranthus retroflexus. There are distinct differences between the shapes of the plants especially in early growing stages. Therefore, ten shape features of the leaves were considered for discrimination between the weeds and corn plants. An image processing algorithm was developed and combined with the artificial neural network (ANN) for classification of corn and weeds. Several images of the leaves of each plant were taken. The ten shape features extracted from the images by image processing algorithm were fed as the input to the ANN classifier. A number of the corn and weeds leaves’ images were used to train the network. Several topologies of ANN including single and multi layer perceptrons (MLPs) with various transfer functions such as MLP-GDM, MLP-RP and MLP-SCG were used. Finally, the ability of the ANN models for classifying weeds and corn plants were evaluated using new image data. Results revealed that the ANN could discriminate corn from weeds with an accuracy of 98.5%. However, the algorithm had less accuracy for classifying the weeds from each other which was limited to 78.5%." @default.
- W2280243008 created "2016-06-24" @default.
- W2280243008 creator A5006950489 @default.
- W2280243008 creator A5014844293 @default.
- W2280243008 creator A5038065006 @default.
- W2280243008 date "2010-01-01" @default.
- W2280243008 modified "2023-09-26" @default.
- W2280243008 title "Weeds and corn classification by image processing and neural network techniques." @default.
- W2280243008 cites W1965117835 @default.
- W2280243008 cites W1985552345 @default.
- W2280243008 cites W2040391664 @default.
- W2280243008 cites W2057749259 @default.
- W2280243008 cites W2091767679 @default.
- W2280243008 cites W2122919861 @default.
- W2280243008 cites W2148420953 @default.
- W2280243008 cites W2159927786 @default.
- W2280243008 hasPublicationYear "2010" @default.
- W2280243008 type Work @default.
- W2280243008 sameAs 2280243008 @default.
- W2280243008 citedByCount "0" @default.
- W2280243008 crossrefType "journal-article" @default.
- W2280243008 hasAuthorship W2280243008A5006950489 @default.
- W2280243008 hasAuthorship W2280243008A5014844293 @default.
- W2280243008 hasAuthorship W2280243008A5038065006 @default.
- W2280243008 hasConcept C115961682 @default.
- W2280243008 hasConcept C153180895 @default.
- W2280243008 hasConcept C154945302 @default.
- W2280243008 hasConcept C2775891814 @default.
- W2280243008 hasConcept C2778415121 @default.
- W2280243008 hasConcept C2778615690 @default.
- W2280243008 hasConcept C33923547 @default.
- W2280243008 hasConcept C41008148 @default.
- W2280243008 hasConcept C50644808 @default.
- W2280243008 hasConcept C60908668 @default.
- W2280243008 hasConcept C6557445 @default.
- W2280243008 hasConcept C86803240 @default.
- W2280243008 hasConcept C9417928 @default.
- W2280243008 hasConcept C95623464 @default.
- W2280243008 hasConceptScore W2280243008C115961682 @default.
- W2280243008 hasConceptScore W2280243008C153180895 @default.
- W2280243008 hasConceptScore W2280243008C154945302 @default.
- W2280243008 hasConceptScore W2280243008C2775891814 @default.
- W2280243008 hasConceptScore W2280243008C2778415121 @default.
- W2280243008 hasConceptScore W2280243008C2778615690 @default.
- W2280243008 hasConceptScore W2280243008C33923547 @default.
- W2280243008 hasConceptScore W2280243008C41008148 @default.
- W2280243008 hasConceptScore W2280243008C50644808 @default.
- W2280243008 hasConceptScore W2280243008C60908668 @default.
- W2280243008 hasConceptScore W2280243008C6557445 @default.
- W2280243008 hasConceptScore W2280243008C86803240 @default.
- W2280243008 hasConceptScore W2280243008C9417928 @default.
- W2280243008 hasConceptScore W2280243008C95623464 @default.
- W2280243008 hasIssue "2" @default.
- W2280243008 hasLocation W22802430081 @default.
- W2280243008 hasOpenAccess W2280243008 @default.
- W2280243008 hasPrimaryLocation W22802430081 @default.
- W2280243008 hasRelatedWork W1158524224 @default.
- W2280243008 hasRelatedWork W1575406023 @default.
- W2280243008 hasRelatedWork W1760545356 @default.
- W2280243008 hasRelatedWork W2131128092 @default.
- W2280243008 hasRelatedWork W2155569884 @default.
- W2280243008 hasRelatedWork W2578141825 @default.
- W2280243008 hasRelatedWork W272896212 @default.
- W2280243008 hasRelatedWork W2781967587 @default.
- W2280243008 hasRelatedWork W2804198066 @default.
- W2280243008 hasRelatedWork W2945612938 @default.
- W2280243008 hasRelatedWork W2966623866 @default.
- W2280243008 hasRelatedWork W3010358965 @default.
- W2280243008 hasRelatedWork W3018965423 @default.
- W2280243008 hasRelatedWork W3115859782 @default.
- W2280243008 hasRelatedWork W3121969053 @default.
- W2280243008 hasRelatedWork W3136376090 @default.
- W2280243008 hasRelatedWork W3163312212 @default.
- W2280243008 hasRelatedWork W3199928923 @default.
- W2280243008 hasRelatedWork W320530719 @default.
- W2280243008 hasRelatedWork W2184817953 @default.
- W2280243008 hasVolume "4" @default.
- W2280243008 isParatext "false" @default.
- W2280243008 isRetracted "false" @default.
- W2280243008 magId "2280243008" @default.
- W2280243008 workType "article" @default.