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

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.

Application of Optimization and Modeling for the Composting Process Enhancement

“…The obtained result allowed to adjust the air flow rate to the value required to keep the carbon monoxide concentration below the maximum recommended concentration (<100 ppm). A very interesting approach to the analysis and monitoring of the composting process was presented by Wojcieszak et al [123], where image analysis of compost samples was applied in conjunction with modeling ANN. The developed ANN models were able to determine the dry matter and dry organic matter of compost with high accuracy.…”

Identification of Factors Affecting Environmental Contamination Represented by Post-Hatching Eggshells of a Common Colonial Waterbird with Usage of Artificial Neural Networks

“…The generated ANN topologies are, among others, a universal and convenient instrument increasingly used in scientific research. They are often used as predictive instruments [ 21 , 22 ]. In contrast to other analytical methods, such as linear models, ANNs are not sensitive to multi-collinearity or non-normal distribution (collinearity).…”

Influence of Gypsum-Containing Waste on Ammonia Binding in Animal Waste Composting

“…In the course of biothermal processes, compounds that are easily assimilated by plants are formed in the compost. Compost additives reduce organic matter and nitrogen losses [6][7][8][9][10].…”