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

Dawid Wojcieszak, Maciej Zaborowicz, Jacek Przybył, Piotr Boniecki and Aleksander Jędruś
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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

Agriculture, 2021, vol. 11, issue 4, 1-12

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.

Keywords: neural modelling; neuron image analysis; dry matter and dry organic matter in compost; features of the composted material (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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