Neural classifier in the estimation process of maturity of selected varieties of apples

Boniecki, P.; Piekarska-Boniecka, H.; Koszela, Krzysztof; Zaborowicz, Maciej; Przybył, Krzysztof; Wojcieszak, Dawid; Zbytek, Z.; Ludwiczak, Agnieszka; Przybylak, Andrzej; Lewicki, Andrzej

doi:10.1117/12.2197029

Cited by 10 publications

(11 citation statements)

References 6 publications

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“…In recent years, the artificial neural networks have been used in agricultural engineering [11][12], eco-energetics [13][14] and environmental engineering [15][16]. The obtained rewarding results in terms of classification and prediction were used to construct the expert systems working in real-time, effectively supporting the decision-making processes in many sectors of agriculture, food processing and biofuel production [17][18]. This even resulted in development of the first agricultural equipment and machinery based on the methods of artificial intelligence, and therefore, without constant supervision and manual.…”

Section: Introductionmentioning

confidence: 99%

Energy and economic potential of maize straw used for biofuels production

Zbytek

Dach

Pawłowski

et al. 2016

MATEC Web of Conferences

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Abstract. The paper presents the energy and economic comparison of two technologies of maize straw utilization: solid biofuel production (briquettes) and methane fermentation. The research experiments have shown that maize straw is the material which can be efficiently implemented in both technologies. Maize straw usage as briquettes can generate more energy (10.956 GJ Mg -1 ) than methane fermentation (9.74). In Europe, biogas is used in co-generation units for production of electric and heat energy. Due to higher price of electricity, economic profitability of maize straw usage for biogas production is over twice higher (182 USD) than in case of briquettes production (96 USD).

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Section: Introductionmentioning

confidence: 99%

Energy and economic potential of maize straw used for biofuels production

Zbytek

Dach

Pawłowski

et al. 2016

MATEC Web of Conferences

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“…High quality requirements for producers make it necessary to select the product at the time of its harvest, preparation for packaging, and storage. Therefore, non-invasive methods for identifying lesions are investigated [8].…”

Section: Introductionmentioning

confidence: 99%

Quality Evaluation of Potato Tubers Using Neural Image Analysis Method

et al. 2020

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This paper describes the research aimed at developing an effective quality assessment method for potato tubers using neural image analysis techniques. Nowadays, the methods used to identify damage and diseases are time-consuming, require specialized knowledge, and often rely on subjective judgment. This study showed the use of the developed neural model as a tool supporting the evaluation of potato tubers during the sorting process in the storage room.

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“…In the face of diversity of neural networks resulting from using numerous architectures, a really important stage is the selection of those, which, in an ambiguous way, are able to solve a given problem that is under research. Classification is understood as finding such a classifier, which allows dividing the set of elements into groups, called classes [25,26,27]. Elements belonging to one group are called objects.…”

Section: Introductionmentioning

confidence: 99%

Neural Image Analysis and Electron Microscopy to Detect and Describe Selected Quality Factors of Fruit and Vegetable Spray-Dried Powders—Case Study: Chokeberry Powder

Przybył

Gawałek

Koszela

et al. 2019

Sensors

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The study concentrates on researching possibilities of using computer image analysis and neural modeling in order to assess selected quality discriminants of spray-dried chokeberry powder. The aim of the paper is the quality identification of chokeberry powders on account of their highest dying power, the highest bioactivity, as well as technologically satisfying looseness of the powder. The article presents neural models with vision techniques backed up by devices such as digital cameras, as well as an electron microscope. The reduction in size of input variables with PCA has an influence on improving the processes of learning data sets, thus increasing the effectiveness of identifying chokeberry fruit powders included in digital pictures, which is shown in the results of the conducted research. The effectiveness of image recognition is presented by classifying abilities, as well as low Root Mean Square Error (RMSE), for which the best results are achieved with a typology of network type Multi-Layer Perceptron (MLP). The selected networks type MLP are characterized by the highest degree of classification at 0.99 and RMSE at 0.11 at most at the same time.

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Neural classifier in the estimation process of maturity of selected varieties of apples

Cited by 10 publications

References 6 publications

Energy and economic potential of maize straw used for biofuels production

Energy and economic potential of maize straw used for biofuels production

Quality Evaluation of Potato Tubers Using Neural Image Analysis Method

Neural Image Analysis and Electron Microscopy to Detect and Describe Selected Quality Factors of Fruit and Vegetable Spray-Dried Powders—Case Study: Chokeberry Powder

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