Knowledge-Based System for Crop Pests and Diseases Recognition
With the rapid increase in the world’s population, there is an ever-growing need for a sustainable food supply. Agriculture is one of the pillars for worldwide food provisioning, with fruits and vegetables being essential for a healthy diet. However, in the last few years the worldwide dispersion of...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/10/8/905 |
| _version_ | 1797538101786050560 |
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| author | Miguel Ángel Rodríguez-García Francisco García-Sánchez Rafael Valencia-García |
| author_facet | Miguel Ángel Rodríguez-García Francisco García-Sánchez Rafael Valencia-García |
| author_sort | Miguel Ángel Rodríguez-García |
| collection | DOAJ |
| description | With the rapid increase in the world’s population, there is an ever-growing need for a sustainable food supply. Agriculture is one of the pillars for worldwide food provisioning, with fruits and vegetables being essential for a healthy diet. However, in the last few years the worldwide dispersion of virulent plant pests and diseases has caused significant decreases in the yield and quality of crops, in particular fruit, cereal and vegetables. Climate change and the intensification of global trade flows further accentuate the issue. Integrated Pest Management (IPM) is an approach to pest control that aims at maintaining pest insects at tolerable levels, keeping pest populations below an economic injury level. Under these circumstances, the early identification of pests and diseases becomes crucial. In this work, we present the first step towards a fully fledged, semantically enhanced decision support system for IPM. The ultimate goal is to build a complete agricultural knowledge base by gathering data from multiple, heterogeneous sources and to develop a system to assist farmers in decision making concerning the control of pests and diseases. The pest classifier framework has been evaluated in a simulated environment, obtaining an aggregated accuracy of 98.8%. |
| first_indexed | 2024-03-10T12:26:40Z |
| format | Article |
| id | doaj.art-07e1918dd9a344e6ad9fab650669d0fc |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T12:26:40Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-07e1918dd9a344e6ad9fab650669d0fc2023-11-21T14:59:17ZengMDPI AGElectronics2079-92922021-04-0110890510.3390/electronics10080905Knowledge-Based System for Crop Pests and Diseases RecognitionMiguel Ángel Rodríguez-García0Francisco García-Sánchez1Rafael Valencia-García2Department of Computer Science, Rey Juan Carlos University, 28933 Madrid, SpainDepartment of Informatics and Systems, Faculty of Computer Science, University of Murcia, 30100 Murcia, SpainDepartment of Informatics and Systems, Faculty of Computer Science, University of Murcia, 30100 Murcia, SpainWith the rapid increase in the world’s population, there is an ever-growing need for a sustainable food supply. Agriculture is one of the pillars for worldwide food provisioning, with fruits and vegetables being essential for a healthy diet. However, in the last few years the worldwide dispersion of virulent plant pests and diseases has caused significant decreases in the yield and quality of crops, in particular fruit, cereal and vegetables. Climate change and the intensification of global trade flows further accentuate the issue. Integrated Pest Management (IPM) is an approach to pest control that aims at maintaining pest insects at tolerable levels, keeping pest populations below an economic injury level. Under these circumstances, the early identification of pests and diseases becomes crucial. In this work, we present the first step towards a fully fledged, semantically enhanced decision support system for IPM. The ultimate goal is to build a complete agricultural knowledge base by gathering data from multiple, heterogeneous sources and to develop a system to assist farmers in decision making concerning the control of pests and diseases. The pest classifier framework has been evaluated in a simulated environment, obtaining an aggregated accuracy of 98.8%.https://www.mdpi.com/2079-9292/10/8/905agrisemanticscrop pest recognitionnatural language processingontology populationsemantic data integration |
| spellingShingle | Miguel Ángel Rodríguez-García Francisco García-Sánchez Rafael Valencia-García Knowledge-Based System for Crop Pests and Diseases Recognition Electronics agrisemantics crop pest recognition natural language processing ontology population semantic data integration |
| title | Knowledge-Based System for Crop Pests and Diseases Recognition |
| title_full | Knowledge-Based System for Crop Pests and Diseases Recognition |
| title_fullStr | Knowledge-Based System for Crop Pests and Diseases Recognition |
| title_full_unstemmed | Knowledge-Based System for Crop Pests and Diseases Recognition |
| title_short | Knowledge-Based System for Crop Pests and Diseases Recognition |
| title_sort | knowledge based system for crop pests and diseases recognition |
| topic | agrisemantics crop pest recognition natural language processing ontology population semantic data integration |
| url | https://www.mdpi.com/2079-9292/10/8/905 |
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