Identification of malting barley varieties using computer image analysis and artificial neural networks

Nowakowski, Krzysztof; Boniecki, P.; Tomczak, Robert Jacek; Kujawa, Sebastian; Raba, Barbara

doi:10.1117/12.954155

Cited by 21 publications

(12 citation statements)

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“…High influence on the development of artificial neural networks had a technical evolution in the field of neuroprocessors construction, which are analog hardware applications. 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].…”

Section: Introductionmentioning

confidence: 99%

Energy and economic potential of maize straw used for biofuels production

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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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“…Artificial neural networks were responsible for the identifying defects on the basis of the variables obtained during the process of image analysis of research material -barley caryopsis [6] [7]. The data obtained were let in accurate way to find whether a kernel has been damaged.…”

Section: Methodsmentioning

confidence: 99%

Identification of physical parameters of cereal grain using computer image analysis and neural models

et al. 2013

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The subject of the project was the selection of neural models for the identification of physical parameters of grain quality regarding to malting barley. Help in its implementation was the original computer system, "Hordeum v 2.0", in which graphic data was gained from digital images of kernels obtained by acquisition. The principal aim was to verify whether the artificial neural networks in combination with computer image analysis can become a practical tool used in farming, and whether the proposed technology can be applied in analysing the quality of cereal grains.

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“…The combination of both tools, which is also called the neural image analysis, is applied for the assessment of damage to cereal grains (Nowakowski et al, 2009(Nowakowski et al, , 2011 and fruit (Guyer and Yang, 2000). Apart from that it is also used for the identification of cereal grain varieties (Chen et al, 2010;Nowakowski et al, 2012;Pourreza et al, 2012;Zapotoczny, 2011). In view of the broad classification potential offered by the neural image analysis the authors decided to use it for research on the determination of compost maturity.…”

Section: Introductionmentioning

confidence: 99%