Identifying barley varieties by computer vision

Szczypiński, Piotr M.; Klepaczko, Artur; Zapotoczny, Piotr

doi:10.1016/j.compag.2014.09.016

Cited by 62 publications

(33 citation statements)

References 25 publications

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“…Digital image analysis supports the determination of the morphological, textural and optical parameters in the acquired images, and their qualitative and quantitative interpretation [19][20][21][22][23]. Jirsa and Polišenska [15] relied on digital image analysis to distinguish between Fusarium-infected and healthy wheat kernels based on a classification model with colour descriptors R (red), G (green), B (blue) in the RGB colour space and descriptor H (Hue) in the HSL (Hue, Saturation, Luminance) colour space.…”

Section: Introductionmentioning

confidence: 99%

Classification of Fusarium-infected and healthy wheat kernels based on features from hyperspectral images and flatbed scanner images: a comparative analysis

Ropelewska

Zapotoczny

2018

Eur Food Res Technol

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Wheat infections caused by fungi of the genus Fusarium decrease yields and have serious economic consequences. The produced mycotoxins have harmful effects on human and animal health. The aim of this study was to develop classification models based on selected textural parameters to distinguish between infected and healthy wheat kernels. The classification accuracy of kernels positioned on the ventral side was determined at 78-100% in the model based on textural parameters from hyperspectral images, and at 95-100% based on images generated by a flatbed scanner. Kernels positioned on the dorsal side were correctly classified in 78-98% based on hyperspectral images, and in 92-100% based on colour images. In the models combining textural parameters from the ventral and dorsal sides of wheat kernels, classification accuracy reached 76-98% in hyperspectral images, and 94-100% in images generated by a flatbed scanner. The imaging technique-flatbed scanner and the ventral side of the kernels provided higher classification accuracy results. The results will contribute to further research aiming to develop models for the determination of fungal chemotypes and/or fungal species based on selected textural features of wheat kernels.

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Section: Introductionmentioning

confidence: 99%

Classification of Fusarium-infected and healthy wheat kernels based on features from hyperspectral images and flatbed scanner images: a comparative analysis

Ropelewska

Zapotoczny

2018

Eur Food Res Technol

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“…Luminance and red–green–blue (RGB) pixel distribution are key traits in image analysis for good‐quality information about object reflectance. Many research protocols include measures of grey luminance for information about plant nutrition and health; many others include analysis of RGB colour bands to evaluate the degree of maturity of fruits and vegetables and even food quality . Physiological measures of plants may start from simple digital images and then indirectly relate luminance to specific metabolites, such as fructose, chlorophyll and anthocyanins.…”

Section: Introductionmentioning

confidence: 99%

Estimation of cotyledon isoflavone abundance by a grey luminance-based model in variously hilum-coloured soybean varieties

Barion

Mosca

Vamerali

2016

J. Sci. Food Agric.

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“…Grain is classified based on individual features or a combination of features (Saini, Singh, & Prakash, ). In cereals, digital image analysis is applied mainly to classify cereal species and varieties (Mahesh, Manickavasagan, Jayas, Paliwal, & White, ; Majumdar & Jayas, ; Paliwal, Visen, Jayas, & White, ; Pazoki & Pazoki, ; Pazoki, Pazoki, & Sorkhilalehloo, ; Szczypiński, Klepaczko, & Zapotoczny, ; Zapotoczny, , ), to detect noncereal grains (Sun, ) and nongrain particles (Sun, ; Wallays, Missotten, De Baerdemaeker, & Saeys, ), and to identify discolored, shriveled, poorly developed, and insect‐damaged kernels (Fox, Sulman, Johnson, Young, & Inkerman, ; Shahin & Symons, ; Sun, ). Digital image analysis involving statistical data processing methods delivers more rapid and more accurate results than conventional visual inspection.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of wheat kernels infected by fungi of the genus Fusarium based on morphological features

Ropelewska

2019

Journal of Food Safety

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The aim of this study was to propose a nondestructive method for evaluating wheat kernels infected by fungi of the genus Fusarium based on the morphological features of kernels and for distinguishing infected kernels from healthy kernels. Images were acquired with a flatbed scanner to determine 59 geometric parameters (linear dimensions and shape factors). Wheat kernels were classified as infected or healthy with the use of Decision trees, Rules, Bayes, Lazy, Meta, and Functions classifiers in WEKA 3.9 software. The classification accuracy of the model based on 59 attributes ranged from 58.12% to 73.37%. The selection of the best attributes shortened the time of the analysis and improved classification results. Linear Forward Selection and MLP classifiers were characterized by the highest classification accuracy. Practical applications The infection of cereal grains by fungi of the genus Fusarium is a food security challenge that can compromise the quantity and quality of yields in many agricultural regions. Selected fungi produce mycotoxins with carcinogenic, mutagenic, teratogenic, and immunotoxic effects for humans and animals. In this study, a nondestructive method was applied to evaluate wheat kernels infected by fungi of the genus Fusarium based on the morphological features of kernels and to distinguish infected kernels from healthy kernels. The proposed method can be used for rapid, inexpensive, and effective detection of fungal infections in cereal grain.

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Identifying barley varieties by computer vision

Cited by 62 publications

References 25 publications

Classification of Fusarium-infected and healthy wheat kernels based on features from hyperspectral images and flatbed scanner images: a comparative analysis

Classification of Fusarium-infected and healthy wheat kernels based on features from hyperspectral images and flatbed scanner images: a comparative analysis

Estimation of cotyledon isoflavone abundance by a grey luminance-based model in variously hilum-coloured soybean varieties

Evaluation of wheat kernels infected by fungi of the genus Fusarium based on morphological features

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