Determination of dry matter content in composted material based on digital images of compost taken under mixed visible and UV-A light

Zaborowicz, Maciej; Wojcieszak, Dawid; Górna, Karolina; Kujawa, Sebastian; Kozłowski, R. J.; Przybył, Krzysztof; Mioduszewska, N.; Idziaszek, P.; Boniecki, P.

doi:10.1117/12.2243985

Cited by 18 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors have not found any other studies in the literature aimed at an objective comparison of differences in the appearance of the material composted under different conditions in terms of the size and thermal insulation of the composting chambers. There are a few studies in which the methods of computer image analysis combined with neural modelling have been used to assess certain compost features, such as maturity degree [17,45] or dry matter content [46]. However, their objectives and methods are very far from the studies presented in this manuscript.…”

Section: Resultsmentioning

confidence: 99%

Image Analysis of Sewage Sludge and Barley Straw as Biological Materials Composted under Different Conditions

2019

View full text Add to dashboard Cite

Composting is one of the most important methods of sewage sludge management. This paper describes the methods of computer image analysis used for objective comparison of the appearance of composted materials under diverse conditions in terms of size and thermal insulation of the composting chambers. The research material was a mixture of sewage sludge and barley straw. The composting process was performed under strictly controlled laboratory conditions, using 10 composting chambers with five different volumes. In half of them additional thermal insulation was used, while in the others no insulation was applied. A proper composting process run was observed only in the three chambers with the largest volume and with additional thermal insulation. The images of the materials were subjected to a wide analysis, wherein the values of 17 parameters regarding color and texture were estimated. Significant differences were observed in the appearances between materials obtained during the properly running composting processes and those obtained in the chambers of insufficient size and thermal insulation. The values of the considered parameters determined for images of the composted material under normal and abnormal conditions were significantly different from each other. Thus, these parameters may be used as indicators of a correctly conducted composting process. In the cases of 15 parameters, the values of these differences exceeded 10%, and in the cases of 10 parameters 50%, while in the cases of three parameters as much as 100%.

show abstract

Section: Resultsmentioning

confidence: 99%

Image Analysis of Sewage Sludge and Barley Straw as Biological Materials Composted under Different Conditions

2019

View full text Add to dashboard Cite

show abstract

“…For many years researchers from the Poznań University of Life Science have been developing computer software which generates an FEM mesh from a series of photographs of seed cross-sections made with a microtome (Wereset al, 2009). The geometric properties of crop seeds can be determined by means of neuronal image analysis, which is commonly used to determine the parameters and properties of agricultural products , Zaborowicz et al, 2016.…”

Section: Measurement Of Geometric Propertiesmentioning

confidence: 99%

A method and a station for measurement of the aerodynamic properties of crop seeds

Gierz¹,

Wojcieszak²,

Szymenderski³

et al. 2018

Engineering Mechanics

View full text Add to dashboard Cite

In order to construct sowing and transporting elements in modern pneumatic seed drills it is necessary to know the aerodynamic properties of crop seeds. They are characterised by three indicators: terminal velocity, drag coefficient and fineness ratio. The aim of the study was to present a method and a research station for measurement of the aerodynamic properties of crop seeds in vertical and horizontal planes. The method assumes measurement of geometric properties of seeds by neuron image analysis and aerodynamic measurements in a specially designed station.

show abstract

“…Neural image analysis is commonly used to solve scientific problems of biosystems engineering [29]. The method has been applied to assess the quality of foodstuffs such as fruit and vegetables [17,[30][31][32], cereal grains [33][34][35], and meat [36,37]. The method can also be used to analyse composting processes [23,31].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Determination of dry matter content in composted material based on digital images of compost taken under mixed visible and UV-A light

Cited by 18 publications

References 6 publications

Image Analysis of Sewage Sludge and Barley Straw as Biological Materials Composted under Different Conditions

Image Analysis of Sewage Sludge and Barley Straw as Biological Materials Composted under Different Conditions

A method and a station for measurement of the aerodynamic properties of crop seeds

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

Contact Info

Product

Resources

About