Prediction of the corn grains yield through artificial intelligence
Currently, the determination of the quality of the cereals is done manually by grain classifier experts prior to the marketing stage. In this paper we present a web software tool that allows determining the quality level of a corn sample automatically from an image of it. Image processing algorithms...
- Autores:
-
Varela, Noel
Silva, Jesus
Bonerge Pineda, Omar
Cabrera, Danelys
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/8785
- Acceso en línea:
- https://hdl.handle.net/11323/8785
https://doi.org/10.1016/j.procs.2020.03.080
https://repositorio.cuc.edu.co/
- Palabra clave:
- Cereal quality
Image processing
Web tool
- Rights
- openAccess
- License
- CC0 1.0 Universal
| Summary: | Currently, the determination of the quality of the cereals is done manually by grain classifier experts prior to the marketing stage. In this paper we present a web software tool that allows determining the quality level of a corn sample automatically from an image of it. Image processing algorithms were implemented to correct distortions caused mainly by the capture process. The K-Means classification algorithm was used and a function was developed to calculate the hectolitre weight in relation to the sample area. The results obtained by the application for grades 1 and 2, are close to those measured by the experts. However, those for grade 3 have not been similar since the subsamples selected were not representative. |
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