RESEARCH PAPER
Development of a machine vision system for the determination of some of the physical properties of very irregular small biomaterials
| 1 | Department of Biosystems Engineering, Sari Agricultural Sciences and Natural Resources University, Sari, 9th km of Farah Abad Road, 4818168984, Iran |
| 2 | Department of Petroleum Engineering, College of Engineering, knowledge University, 44001 Erbil, Iraq |
| 3 | Department of Agricultural, Forest and Transport Machinery, University of Life Sciences in Lublin, Głęboka 28, 20-612 Lublin, Poland |
| 4 | Department of Biosystems Engineering, Gorgan Agricultural and Natural Resources University, Iran |
| 5 | Department of Civil Engineering, Cihan University-Erbil, Kurdistan Region, Erbil 44001, Iraq |
CORRESPONDING AUTHOR
Mariusz Szymanek
Department of Agricultural, Forest and Transport Machinery, University of Life Sciences in Lublin, Głęboka 28, 20-612, Lublin, Poland
Department of Agricultural, Forest and Transport Machinery, University of Life Sciences in Lublin, Głęboka 28, 20-612, Lublin, Poland
Final revision date: 2022-01-01
Acceptance date: 2022-01-18
Publication date: 2022-02-18
Int. Agrophys. 2022, 36(1): 27–35
HIGHLIGHTS
- Application of the image processing technique is presented for volume estimation of very irregular small biomaterials (wheat and rice-paddy grains)
- The capability of the image processing technique in estimating the volume of very irregular small biomaterials
- A new parameter called “cylindercity”, which can be used for some cylindrical crops, such as wheat and rice.
KEYWORDS
TOPICS
ABSTRACT
The application of an image processing technique is presented for the volume estimation of very irregular small biomaterials (wheat and rice-paddy grains). Two common cylindrical small biomaterials, the Alvand variety of wheat grain and the Neda variety of paddy grain were considered for examination. The captured images were exported to be processed by an image processing software (ImageJ) and the edge-extracted image was used in SolidWorks for the 3D reconstruction of the model. The revolved images in the SolidWork were used to estimate the volume of the examined grains. The estimated volume was then compared with the conventional mathematical expression and also with the real volume measurement using the fluid displacement method. Volume estimation using machine vision and image processing techniques has a considerably lower mean error (9.5%) in comparison to the mathematical error (14.7%). The average value of cylindricity for Alvand wheat was found to be equal to 82.34% at a moisture content of 11.83%. The new cylindricity factor had a significantly smaller standard deviation in comparison to the standard deviation of the sphericity factor for the examined cylindrical crops (61.5% for the wheat grains and 59.6% for the paddy grains). The new cylindricity factor can be used for the heat and mass transfer modelling of cylindrical crops.
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