Hardware/Software Co-Design of a Vision System for Automatic Classification of Date Fruits

Khriji, Lazhar; Ammari, Ahmed Chiheb; Awadalla, Medhat

doi:10.4018/ijertcs.2020100102

Cited by 7 publications

(1 citation statement)

References 23 publications

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“…Image processing combined with Artificial Neural Networks (ANN) was used to estimate anthocyanin content ( R = .98) and antioxidant activity ( R = .93) of sweet cherries at different ripening stages in an economical and faster way than by common laboratory methods (Taghadomi‐Saberi, Omid, Emam‐Djomeh, & Ahmadi, 2014). Khriji, Ammari, and Awadalla (2020) conducted a study for automatic classification of the Khalas, Khunaizi, Fardh, Qash, Naghal, and Maan date fruits in Oman. For this, they used three machine learning methods, ANN, Support Vector Machine (SVM), and K‐Nearest Neighbor (K‐NN).…”

Section: Introductionmentioning

confidence: 99%

Cultivar identification of sweet cherries based on texture parameters determined using image analysis

Ropelewska

Popińska

Sabancı

et al. 2021

J Food Process Engineering

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The aim of this study was to develop discriminative models for distinguishing different cultivars of whole sweet cherries based on the texture parameters determined using image analysis. The whole fruit images of “Büttner's Red,” “Kordia,” and “Lapins” were acquired using a digital camera. The discriminative models were built for textures selected from individual color channels R, G, B, L, a, b, X, Y, and Z and color spaces CIE RGB (R—red, B—blue, G—green), CIE Lab (L*—lightness from black to white, a*—green and red, b*—blue and yellow), CIE XYZ (Y—lightness, X and Z—components of color information). The models were developed for texture sets without division and subsets of textures with division into those calculated based on the histogram, co‐occurrence matrix, run‐length matrix, autoregressive model, gradient map. The total accuracy reached 100% for the models built based on sets of textures without division selected from color channels R and X and color spaces RGB, Lab, and XYZ. In the case of divided sets of textures, the correctness of 100% was obtained for textures selected from histogram and co‐occurrence matrix for color space Lab. For color channels, the highest accuracy was equal to 97% for the model built based on the selected textures calculated based on the histogram for color channel L. The significance of this study is great for practical applications. The correct identification of sweet cherry cultivar using the discriminative models can be important for the selection of fruits with the desired properties for consumption and processing. Practical applications The research involved the use of a procedure that allows the classification of sweet cherries in a non‐destructive, objective, and inexpensive manner. The obtained results were very satisfactory and allowed for the complete discrimination of sweet cherry cultivars based on texture parameters of the whole fruit. The developed models may be used in practice for cultivar identification of sweet cherries and detection of falsification or confirmation of authenticity. It will avoid the mixing of different cultivars with different properties intended for use and processing.

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Section: Introductionmentioning

confidence: 99%