The use of polarized light and image analysis in evaluations of the severity of fungal infection in barley grain
| Autor: | Jacek Reiner, Piotr Zapotoczny, Mariusz Mrzygłód, Piotr Lampa |
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| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
Artificial neural network Image (category theory) Forestry 04 agricultural and veterinary sciences Horticulture 01 natural sciences Computer Science Applications Machine vision system Pattern recognition (psychology) 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Genus Fusarium BARLEY GRAIN Biological system Agronomy and Crop Science 010606 plant biology & botany Mathematics |
| Zdroj: | Computers and Electronics in Agriculture. 169:105154 |
| ISSN: | 0168-1699 |
| DOI: | 10.1016/j.compag.2019.105154 |
| Popis: | Barley grain infected with fungi of the genus Fusarium was analyzed with the use of a machine vision system. Mold-affected, black-tipped and healthy kernels were viewed under polarized light and non-polarized light as the reference method, and the resulting images were compared. Texture attributes were calculated in selected regions of interest (ROIs) and used in statistical modeling. The results were analyzed with the use of various pattern recognition methods and artificial neural networks. Mold-affected and black-tipped grains were more effectively separated from healthy kernels under polarized light, and this identification technique was 20–30% to 50% more accurate than the reference method. |
| Databáze: | OpenAIRE |
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