Classification of dried vegetables using computer image analysis and artificial neural networks

Koszela, Krzysztof; Łukomski, Mateusz; Mueller, W.; Górna, Karolina; Okoń, P.; Boniecki, P.; Zaborowicz, Maciej; Wojcieszak, Dawid

doi:10.1117/12.2281718

Cited by 26 publications

(8 citation statements)

References 7 publications

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“…With these developments, CVS has been enticing much R&D attention from the food processing industry. It has so many possibilities in itself that it can replace human vision for classification, evaluation of a different aspects of food products according to their visual appearance, and quality inspection (Koszela et al, 2017;Teena et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Study of color kinetics of banana (Musa cavendish) under microwave drying by application of image analysis

Nagvanshi

Venkata

Goswami

2020

Food sci. technol. int.

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Microwave drying works on the volumetric heating concept promoted by electromagnetic radiation at 0.915 or 2.450 GHz. In this study, banana ( Musa Cavendish) was taken as the sample and treated under microwave drying. The effect of two process variables, namely slice thickness (2, 3.5, and 5 mm) and microwave power (180 W, 360 W, and 540 W), were studied on drying kinetics and color kinetics. It was observed that the inverse variation relationship exists between drying time and microwave power level while drying time and slice thickness exhibited a direct variation relationship. A Computer Vision System (CVS) was developed to measure the color values of banana in CIELab space using an algorithm written in MATLAB software. Once the color parameters were obtained, they were fitted in First and Zero-order kinetic models. Both models were found to describe the color values adequately. This study concludes that microwave drying is a promising dehydration technique for banana drying that reduces the significant time of drying. Application of CVS is an excellent approach to measure the surface color of banana.

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Section: Introductionmentioning

confidence: 99%

Study of color kinetics of banana (Musa cavendish) under microwave drying by application of image analysis

Nagvanshi

Venkata

Goswami

2020

Food sci. technol. int.

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“…The training set consisted of 1800 randomly selected data, and it was divided proportionally 2:1:1 into In order to create the classification neural models, a neural network simulator implemented in Statistica v. 10 package was used [1,[18][19][20]. The most important stage of the ANN generation was the preparation of the training files containing encoded selected representative properties, constituting the empirical basis for the classification [20][21][22][23]. For this purpose, four numerical input variables and one nominal output variable were determined, which resulted from the nature of the formulated scientific problem and represented the characteristic parameters of the process examined.…”

Section: Methodsmentioning

confidence: 99%

Neural Visual Detection of Grain Weevil (Sitophilus granarius L.)

et al. 2020

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A significant part of cereal production is intended for agri-food processing, which implies a necessity to search for and implement modern storage systems for this product. Stored grain is exposed to many unfavorable factors, particularly caryopsis macro-damage caused mainly by grain weevil (Sitophilus granarius L.). This triggers a substantial decrease in the value of the stored material, thus resulting in serious economic losses. Due to this fact, it is necessary to take steps to effectively detect this pest’s presence when grain is delivered to storage facilities. The purpose of this work was to identify the representative physical characteristics of wheat caryopsis affected by grain weevil. An automated visual system was developed to ease the detection of damaged kernels and adult weevils. In order to obtain the empirical data, a decision was made to take advance of SKCS 4100 (the Perten Single Kernel Characterization System). The measurements obtained were used to build the training sets necessary in the process of ANN (artificial neural network) learning with digital neural classifiers. Next, a set of identifying neural models was created and verified, and then the optimal topology was selected. The utilitarian goal of the research was to support the decision-making process taking place during grain storage.

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“…An alternative to the classical description and an analysis of empirical systems was found in modern methods, such as artificial neural modeling [14,15]. Artificial neural networks (ANN) are a branch of science that has developed intensively [16], and the main advantage of the neural models has been their ability to generalize the knowledge acquired through training.…”

Section: Storage Phase Of the Rock Bed Thermal Storagementioning

confidence: 99%

Neural Identification of a Temperature Field in the Storing Phase of Thermal Energy in Rock Bed Thermal Storage

2021

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Thermal energy stored within a rock bed thermal storage system, which is mostly used in agriculture, can be identified during the storage phase using mathematical models based on heat transfer, which concerns batteries running in a vertical setting. However, this requires the conversion of differential equations into algebraic equations, as well as knowledge about the initial and boundary conditions. Furthermore, a lack of information or incomplete information about the initial conditions makes it difficult or impossible to evaluate the volume of stored energy, or can cause significant errors during evaluation. Such situations occur in systems equipped with a rock battery, in which solar collectors act as source of energy. Considering the above, as well as the lack of a model for batteries in a vertical setting, we identified the need for research into the storage phase of rock bed thermal storage systems, working in a horizontal setting, and generating MLP-type neural models. Among these models, MLP 4-7-1 turned out to be the best both in terms of the values of regression statistics and possibilities of generalization. According to the authors, artificial neural models depicting temperature changeability in storage phase will be helpful in the development of a new methodology that can predict the heat volume in rock bed thermal storage systems.

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Classification of dried vegetables using computer image analysis and artificial neural networks

Cited by 26 publications

References 7 publications

Study of color kinetics of banana (Musa cavendish) under microwave drying by application of image analysis

Study of color kinetics of banana (Musa cavendish) under microwave drying by application of image analysis

Neural Visual Detection of Grain Weevil (Sitophilus granarius L.)

Neural Identification of a Temperature Field in the Storing Phase of Thermal Energy in Rock Bed Thermal Storage

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