Visible imaging to convolutionally discern and authenticate varieties of rice and their derived flours. (April 2020)

• Record Type: Journal Article
• Authors: Izquierdo, Manuel; Lastra-Mejías, Miguel; González-Flores, Ester; Pradana-López, Sandra; Cancilla, John C.; Torrecilla, José S.
• Is Part Of: Food control. Volume 110(2020)
• Publication Date: 2020-04
• Subjects: Images -- Rice characterization -- Convolutional neural networks -- Food quality; Food -- Quality -- Periodicals; Food -- Analysis -- Periodicals; Food handling -- Periodicals; Food industry and trade -- Quality control -- Periodicals; Aliments -- Industrie et commerce -- Qualité -- Contrôle -- Périodiques; Aliments -- Qualité -- Périodiques; Aliments -- Analyse -- Périodiques; Hygiène alimentaire -- Périodiques; Food -- Analysis; Food handling; Food -- Quality; Periodicals; Electronic journals; 664.07
• DOI: 10.1016/j.foodcont.2019.106971
• Languages: English
• ISSNs: 0956-7135

Abstract

Abstract: In this research, more than 27, 000 images (samples) of five different types of rice ( Oryza sativa L.) have been used to design and validate a deep learning-based system to carry out their classification. A typical photographic camera was used to obtain images from five different varieties of rice, which will be used for their classification after proper treatment. The resulting photographs were processed by convolutional neural networks (CNNs), which have been trained and optimized using these images to identify different types of rice. Finally, the model was successfully validated using images which were initially isolated from the training database. The result was an algorithm capable of detecting and classifying all five rice types accurately. Therefore, CNNs have shown to be a compelling tool for the evaluation of this type of cereal, including for quality purposes, thanks to traits which include high sensitivity, speed, and not requiring highly specialized personnel to implement the optimized version of the algorithm. Highlights: Five Spanish rice varieties classified via convolutional neural networks. Deep learning aids in rice authentication only using images of rice and/or flour. Accurate models distinguish rice grains and flours of a range of particle sizes. Cost-effective and intelligent approach to fight fraud in rice sector.