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Assessment of the Content of Dry Matter and Dry Organic Matter in Compost with Neural Modelling Methods

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  • Dawid Wojcieszak

    (Department of Biosystems Engineering, Poznan University of Life Sciences, ul. Wojska Polskiego 50, 60-627 Poznań, Poland)

  • Maciej Zaborowicz

    (Department of Biosystems Engineering, Poznan University of Life Sciences, ul. Wojska Polskiego 50, 60-627 Poznań, Poland)

  • Jacek Przybył

    (Department of Biosystems Engineering, Poznan University of Life Sciences, ul. Wojska Polskiego 50, 60-627 Poznań, Poland)

  • Piotr Boniecki

    (Department of Biosystems Engineering, Poznan University of Life Sciences, ul. Wojska Polskiego 50, 60-627 Poznań, Poland)

  • Aleksander Jędruś

    (Department of Biosystems Engineering, Poznan University of Life Sciences, ul. Wojska Polskiego 50, 60-627 Poznań, Poland)

Abstract

Neural image analysis is commonly used to solve scientific problems of biosystems and mechanical engineering. The method has been applied, for example, to assess the quality of foodstuffs such as fruit and vegetables, cereal grains, and meat. The method can also be used to analyse composting processes. The scientific problem lets us formulate the research hypothesis: it is possible to identify representative traits of the image of composted material that are necessary to create a neural model supporting the process of assessment of the content of dry matter and dry organic matter in composted material. The effect of the research is the identification of selected features of the composted material and the methods of neural image analysis resulted in a new original method enabling effective assessment of the content of dry matter and dry organic matter. The content of dry matter and dry organic matter can be analysed by means of parameters specifying the colour of compost. The best developed neural models for the assessment of the content of dry matter and dry organic matter in compost are: in visible light RBF 19:19-2-1:1 (test error 0.0922) and MLP 14:14-14-11-1:1 (test error 0.1722), in mixed light RBF 30:30-8-1:1 (test error 0.0764) and MLP 7:7-9-7-1:1 (test error 0.1795). The neural models generated for the compost images taken in mixed light had better qualitative characteristics.

Suggested Citation

  • Dawid Wojcieszak & Maciej Zaborowicz & Jacek Przybył & Piotr Boniecki & Aleksander Jędruś, 2021. "Assessment of the Content of Dry Matter and Dry Organic Matter in Compost with Neural Modelling Methods," Agriculture, MDPI, vol. 11(4), pages 1-12, April.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:4:p:307-:d:528722
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    References listed on IDEAS

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    1. Jingbo Zhao & Yunke Li & Guofang Yang & Kui Jiang & Haoran Lin & Harald Ade & Wei Ma & He Yan, 2016. "Efficient organic solar cells processed from hydrocarbon solvents," Nature Energy, Nature, vol. 1(2), pages 1-7, February.
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    Cited by:

    1. Valeriy Voropaev & Nataliya Alfimova & Ivan Nikulin & Tatyana Nikulicheva & Aleksej Titenko & Vitaly Nikulichev, 2021. "Influence of Gypsum-Containing Waste on Ammonia Binding in Animal Waste Composting," Agriculture, MDPI, vol. 11(11), pages 1-16, November.

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