Detection and classification of citrus green mold caused by Penicillium digitatum using multispectral imaging
| Autor: | Mojtaba Mamarabadi, Narges Ghanei Ghooshkhaneh, Mahmood Reza Golzarian |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Citrus
medicine.medical_specialty Multispectral image Orange (colour) medicine.disease_cause Normalized Difference Vegetation Index 040501 horticulture 0404 agricultural biotechnology Food Preservation Mold medicine Plant Diseases Penicillium digitatum Spectroscopy Near-Infrared Nutrition and Dietetics biology Spectrum Analysis Penicillium 04 agricultural and veterinary sciences biology.organism_classification 040401 food science Spectral imaging Horticulture Fruit Postharvest 0405 other agricultural sciences Agronomy and Crop Science Food Science Biotechnology |
| Zdroj: | Journal of the Science of Food and Agriculture. 98:3542-3550 |
| ISSN: | 0022-5142 |
| DOI: | 10.1002/jsfa.8865 |
| Popis: | Fungal decay is a prevalent condition that mainly occurs during transportation of products to consumers (from harvest to consumption) and adversely affects postharvest operations and sales of citrus fruit. There are a variety of methods to control pathogenic fungi, including UV-assisted removal of fruit with suspected infection before storage, which is a time-consuming task and associated with human health risks. Therefore it is essential to adopt efficient and dependable alternatives for early decay detection. In this study, detection of orange decay caused by Penicillium genus fungi was examined using spectral imaging, a novel automated inspection technique for agricultural products.; Results: The reflectance parameter (including mean reflectance) and reflectance distribution parameters (including standard deviation and skewness) of surfaces were extracted from decayed and rotten regions of infected samples and healthy regions of non-infected samples. The classification accuracy of rotten, decayed and healthy regions at 4 and 5 days after fungal inoculation was 98.6 and 100% respectively using the mean and skewness of 500 nm, 800 nm, 900 nm and modified normalized difference vegetation index (MNDVI) spectra.; Conclusion: Comparison of results between healthy and infected samples showed that early real-time detection of Penicillium digitatum using multispectral imaging was possible within the near-infrared (NIR) range. © 2018 Society of Chemical Industry.; © 2018 Society of Chemical Industry. |
| Databáze: | OpenAIRE |
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