Matches in SemOpenAlex for { <https://semopenalex.org/work/W2894759909> ?p ?o ?g. }
Showing items 1 to 81 of
81
with 100 items per page.
- W2894759909 abstract "Detection of rice pest and diseases, and proper management and control of pest infested rice fields may result to a higher rice crop production. According to the International Rice Research Institute, farmers lose an average of 37% of their rice crops due to pest and diseases, yearly. Using modern technologies, like smart phones, farmers can be aided in detecting and identifying the type of pests and diseases found in their rice fields. This study proposed an application that will help farmers in detecting rice insect pests and diseases using Convolutional Neural Network(CNN) and image processing. It looked into the different pests that attack rice fields; information on how they can be controlled and managed was considered; farmers' knowledge in different rice pests and diseases, and how they control these pests was regarded in this study; the study also looked into the reporting mechanism of farmers to government agencies. Using CNN and image processing, the application that detects rice pests and diseases was developed. The searching and comparison of captured images to a stack of rice pest images was implemented using a model based on CNN. Collected images were pre-processed and were used in training the model. The model was able to achieve a final training accuracy of 90.9 percent. Cross-entropy was low, which implies that the trained model can perform prediction or can classify images with low percentage of error. Through the developed application, farmers were provided with information and procedures on how to control and manage rice pest infestation. Future researchers may look into multiple pest comparison to a stack of images for faster retrieval of information." @default.
- W2894759909 created "2018-10-12" @default.
- W2894759909 creator A5028131516 @default.
- W2894759909 creator A5090070836 @default.
- W2894759909 date "2018-04-27" @default.
- W2894759909 modified "2023-09-27" @default.
- W2894759909 title "Rice Pest and Disease Detection Using Convolutional Neural Network" @default.
- W2894759909 cites W1979062633 @default.
- W2894759909 cites W1997532488 @default.
- W2894759909 cites W2053597545 @default.
- W2894759909 cites W2068064330 @default.
- W2894759909 cites W2090813402 @default.
- W2894759909 cites W2155008375 @default.
- W2894759909 cites W2167828202 @default.
- W2894759909 cites W2323043364 @default.
- W2894759909 cites W2470368200 @default.
- W2894759909 cites W2536452926 @default.
- W2894759909 cites W2548258044 @default.
- W2894759909 cites W2586383982 @default.
- W2894759909 cites W2588266782 @default.
- W2894759909 cites W2600485429 @default.
- W2894759909 cites W2734648001 @default.
- W2894759909 cites W2750506686 @default.
- W2894759909 cites W3123521989 @default.
- W2894759909 doi "https://doi.org/10.1145/3209914.3209945" @default.
- W2894759909 hasPublicationYear "2018" @default.
- W2894759909 type Work @default.
- W2894759909 sameAs 2894759909 @default.
- W2894759909 citedByCount "36" @default.
- W2894759909 countsByYear W28947599092020 @default.
- W2894759909 countsByYear W28947599092021 @default.
- W2894759909 countsByYear W28947599092022 @default.
- W2894759909 countsByYear W28947599092023 @default.
- W2894759909 crossrefType "proceedings-article" @default.
- W2894759909 hasAuthorship W2894759909A5028131516 @default.
- W2894759909 hasAuthorship W2894759909A5090070836 @default.
- W2894759909 hasConcept C115961682 @default.
- W2894759909 hasConcept C123963621 @default.
- W2894759909 hasConcept C127413603 @default.
- W2894759909 hasConcept C144027150 @default.
- W2894759909 hasConcept C154945302 @default.
- W2894759909 hasConcept C22508944 @default.
- W2894759909 hasConcept C2992726227 @default.
- W2894759909 hasConcept C41008148 @default.
- W2894759909 hasConcept C540442320 @default.
- W2894759909 hasConcept C6557445 @default.
- W2894759909 hasConcept C81363708 @default.
- W2894759909 hasConcept C86803240 @default.
- W2894759909 hasConcept C88463610 @default.
- W2894759909 hasConcept C9417928 @default.
- W2894759909 hasConceptScore W2894759909C115961682 @default.
- W2894759909 hasConceptScore W2894759909C123963621 @default.
- W2894759909 hasConceptScore W2894759909C127413603 @default.
- W2894759909 hasConceptScore W2894759909C144027150 @default.
- W2894759909 hasConceptScore W2894759909C154945302 @default.
- W2894759909 hasConceptScore W2894759909C22508944 @default.
- W2894759909 hasConceptScore W2894759909C2992726227 @default.
- W2894759909 hasConceptScore W2894759909C41008148 @default.
- W2894759909 hasConceptScore W2894759909C540442320 @default.
- W2894759909 hasConceptScore W2894759909C6557445 @default.
- W2894759909 hasConceptScore W2894759909C81363708 @default.
- W2894759909 hasConceptScore W2894759909C86803240 @default.
- W2894759909 hasConceptScore W2894759909C88463610 @default.
- W2894759909 hasConceptScore W2894759909C9417928 @default.
- W2894759909 hasLocation W28947599091 @default.
- W2894759909 hasOpenAccess W2894759909 @default.
- W2894759909 hasPrimaryLocation W28947599091 @default.
- W2894759909 hasRelatedWork W2337926734 @default.
- W2894759909 hasRelatedWork W2799614062 @default.
- W2894759909 hasRelatedWork W2978290780 @default.
- W2894759909 hasRelatedWork W3027997911 @default.
- W2894759909 hasRelatedWork W3136076031 @default.
- W2894759909 hasRelatedWork W3173182854 @default.
- W2894759909 hasRelatedWork W4281780675 @default.
- W2894759909 hasRelatedWork W4285586943 @default.
- W2894759909 hasRelatedWork W4287776258 @default.
- W2894759909 hasRelatedWork W3009789068 @default.
- W2894759909 isParatext "false" @default.
- W2894759909 isRetracted "false" @default.
- W2894759909 magId "2894759909" @default.
- W2894759909 workType "article" @default.