Detecting rottenness caused by Penicillium genus fungi in citrus fruits using machine learning techniques
Penicillium fungi are among the main defects that may affect the commercialization of citrus fruits. Economic losses in fruit production may become enormous if an early detection of that kind of fungi is not carried out. That early detection is usually based either on UltraViolet light carried out m...
| Autores principales: | , , , , , |
|---|---|
| Formato: | article |
| Lenguaje: | Inglés |
| Publicado: |
2017
|
| Acceso en línea: | http://hdl.handle.net/20.500.11939/5319 |
Ejemplares similares: Detecting rottenness caused by Penicillium genus fungi in citrus fruits using machine learning techniques
- Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins
- Early detection of infection by Penicillium digitatum in oranges using hyperspectral imaging and machine learning
- Hyperspectral LCTF-based system for classification of decay in mandarins caused by Penicillium digitatum and Penicillium italicum using the most relevant bands and non-linear classifiers
- Discrimination of common defects in loquat fruit cv. ‘Algerie’ using hyperspectral imaging and machine learning techniques
- Online fitted policy iteration based on extreme learning machines
- Segmentation of hyperspectral images for the detection of rotten mandarins