SOFM-type artificial neural network for the non-parametric quality-based classification of potatoes

Boniecki, P.; Przybył, J.; Zaborowicz, Maciej; Górna, Karolina; Dach, Jacek; Okoń, P.; Przybył, Krzysztof; Mioduszewska, N.; Idziaszek, P.

doi:10.1117/12.2243907

Cited by 6 publications

(7 citation statements)

References 11 publications

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“…As a result of the progress made in the research area concentrating on classification issues [ 26 , 30 , 40 ] with Artificial Neural Networks (ANN, quality changes were observed in relation to farm and food products, which are dictated by consumer expectations [ 26 , 34 ]. This method is based on neural models supported by computer analysis of images, which is faster and more efficient in comparison to the traditional calculating methods.…”

Section: Resultsmentioning

confidence: 99%

“…This method is based on neural models supported by computer analysis of images, which is faster and more efficient in comparison to the traditional calculating methods. Artificial Neural Networks (ANN) allows us to make more effective simulations of complex processes, among other things, via the process of learning parallel with information processing [ 29 , 34 ]. On the basis of the research results, it was found that the image analysis is an effective technique of recognizing contamination of triticale seeds.…”

Section: Resultsmentioning

confidence: 99%

“…Classification means finding a classifier that will enable the division of a set of elements into groups called classes. Elements belonging to one group are called objects [ 26 , 34 ]. They may differ from each other, but not in the properties due to which they are assigned to a particular class.…”

Section: Introductionmentioning

confidence: 99%

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The Use of Image Analysis to Detect Seed Contamination—A Case Study of Triticale

Gierz

Przybył

Koszela

et al. 2020

Sensors

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Samples of triticale seeds of various qualities were assessed in the study. The seeds were obtained during experiments, reflecting the actual sowing conditions. The experiments were conducted on an original test facility designed by the authors of this study. The speed of the air (15, 20, 25 m/s) transporting seeds in the pneumatic conduit was adjusted to sowing. The resulting graphic database enabled the distinction of six classes of seeds according to their quality and sowing speed. The database was prepared to build training, validation and test sets. The neural model generation process was based on multi-layer perceptron networks (MLPN) and statistical (machine training). When the MLPN was used to identify contaminants in seeds sown at a speed of 15 m/s, the lowest RMS error of 0.052 was noted, whereas the classification correctness coefficient amounted to 0.99.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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The Use of Image Analysis to Detect Seed Contamination—A Case Study of Triticale

Gierz

Przybył

Koszela

et al. 2020

Sensors

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“…Using them in intelligent information systems to perform analysis and recognition with sound or images simplifies production processes, the systematic monitoring of processes and the control of production process as well as the quality evaluation of finished goods. The research work that was done is one the examples of using ANN [2,34,35] which aims, among other things, at automating the process of controlling the quality condition of dried fruits with acoustic signals.…”

Section: Preparation Of Artificial Neural Networkmentioning

confidence: 99%

Classification of Dried Strawberry by the Analysis of the Acoustic Sound with Artificial Neural Networks

Przybył

Duda

Koszela

et al. 2020

Sensors

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In this paper, the authors used an acoustic wave acting as a disturbance (acoustic vibration), which travelled in all directions on the whole surface of a dried strawberry fruit in its specified area. The area of space in which the acoustic wave occurs is defined as the acoustic field. When the vibrating surface—for example, the surface of the belt—becomes the source, then one can observe the travelling of surface waves. For any shape of the surface of the dried strawberry fruit, the signal of travelling waves takes the form that is imposed by this irregular surface. The aim of this work was to research the effectiveness of recognizing the two trials in the process of convection drying on the basis of the acoustic signal backed up by neural networks. The input variables determined descriptors such as frequency (Hz) and the level of luminosity (dB). During the research, the degree of crispiness relative to the degree of maturity was compared. The results showed that the optimal neural model in respect of the lowest value of the root mean square turned out to be the Multi-Layer Perceptron network with the technique of dropping single fruits into water (data included in the learning data set Z2). The results confirm that the choice of method can have an influence on the effectives of recognizing dried strawberry fruits, and also this can be a basis for creating an effective and fast analysis tool which is capable of analyzing the degree of ripeness of fruits including their crispness in the industrial process of drying fruits.

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“…An effective identification of the effects of grain weevil feeding on stored grain entailed the designing, manufacturing, and verification of the new, original classification model [7][8][9][10][11]. The commonly recognized separation properties, such as those represented by artificial neural networks, imply the legitimacy of using them in the process of identifying grain damage caused by grain weevil [12].…”

Section: Introductionmentioning

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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SOFM-type artificial neural network for the non-parametric quality-based classification of potatoes

Cited by 6 publications

References 11 publications

The Use of Image Analysis to Detect Seed Contamination—A Case Study of Triticale

The Use of Image Analysis to Detect Seed Contamination—A Case Study of Triticale

Classification of Dried Strawberry by the Analysis of the Acoustic Sound with Artificial Neural Networks

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

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