Abstract
Abstract The geometric and color features of agricultural material along with related physical properties are critical to characterize and express its physical quality. The experiments were conducted to classify the physical characteristics like size, shape, color and texture and then workout the relationship between manual observations and using image processing techniques for weight and volume of the four wheat refractions i.e. sound, damaged, shriveled and broken grains of wheat variety PBW 725. A flatbed scanner was used to acquire the images and digital image processing method was used to process the images and output of image analysis was compared with the actual measurements data using digital vernier caliper. A linear relationship was observed between the axial dimensions of refractions between manual measurement and image processing method with R 2 in the range of 0.798–0.947. The individual kernel weight and thousand grain weight of the refractions were observed to be in the range of 0.021–0.045 and 12.56–46.32 g respectively. Another linear relationship was found between individual kernel weight and projected area estimated using image processing methodology with R 2 in the range of 0.841–0.920. The sphericity of the refractions varied in the range of 0.52–0.71. Analyses of the captured images suggest ellipsoid shape with convex geometry while the same observation was recorded by physical measurements also. A linear relationship was observed between the volume of refractions derived from measured dimensions and calculated from image with R 2 in the range of 0.845–0.945. Various color and grey level co-variance matrix texture features were extracted from acquired images using the open-source Python programming language and OpenCV library which can exploit different machine and deep learning algorithms to properly classify these refractions.
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Claims 7
Claim · model-written summary
Manual and image-based axial dimensions had R² of 0.798–0.947.
Not reportedTaxon
Verbatim
e0“A linear relationship was observed between the axial dimensions of refractions between manual measurement and image processing method with R 2 in the range of 0.798–0.947”
Claim · model-written summary
Individual kernel weight ranged from 0.021 to 0.045.
Not reportedTaxon
Verbatim
e0“individual kernel weight and thousand grain weight of the refractions were observed to be in the range of 0.021–0.045 and 12.56–46.32 g respectively”
Claim · model-written summary
Thousand grain weight ranged from 12.56 to 46.32 g.
Not reportedTaxon
Verbatim
e0“thousand grain weight of the refractions were observed to be in the range of 0.021–0.045 and 12.56–46.32 g respectively”
Claim · model-written summary
Kernel weight and image-estimated projected area had R² of 0.841–0.920.
Not reportedTaxon
Verbatim
e0“Another linear relationship was found between individual kernel weight and projected area estimated using image processing methodology with R 2 in the range of 0.841–0.920”
Claim · model-written summary
Wheat refraction sphericity ranged from 0.52 to 0.71.
Not reportedTaxon
Verbatim
e0“The sphericity of the refractions varied in the range of 0.52–0.71”
Claim · model-written summary
Image and physical measurements both indicated an ellipsoid convex shape.
Not reportedTaxon
Verbatim
e0“Analyses of the captured images suggest ellipsoid shape with convex geometry while the same observation was recorded by physical measurements also”
Claim · model-written summary
Measured-dimension and image-derived volumes had R² of 0.845–0.945.
Not reportedTaxon
Verbatim
e0“A linear relationship was observed between the volume of refractions derived from measured dimensions and calculated from image with R 2 in the range of 0.845–0.945”