grain moisture contentPhysical appearance and kernel morphology significantly affect the grade of a harvested crop in addition to other factors such as test weight, percentage of foreign matter and constituent components. Moisture content of grain can potentially affect the physical appearance and kernel morphology, which in turn affects the grade of the harvested crop. In this project, we evaluated the effect of moisture content on the classification capability of color, morphology and textural features of imaged grains. Color images of individual kernels and bulk samples of wheat and barley were acquired using a high resolution color camera with an IEEE 1394 interface machine vision system. Algorithms developed by Grain Storage Research Laboratory were implemented to extract color, textural and morphological features from grain images. Image segmentation was applied to remove the background from the single kernel images. The extracted features were analyzed and classified for the effect of moisture content using statistical classifiers and a devised back propagation neural network model.

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DurumKnowledge of the structure and properties of microscopic surfaces of durum wheat starch granules is essential for understanding the functional and physico-chemical properties. The nanoscale surface undulations on the starch granules inside durum wheat macroscopically influence the milling properties. The objective of this study was to visualize the size and dispersion of the starch grains in vitreous and non-vitreous durum wheat kernels using atomic force microscopy. The distribution of starch granules in the vitreous and non-vitreous durum wheat starch samples were examined and compared. The results of our study confirm the ‘blocklet’ model of the ultra structure of the starch granule surface. Image contrast enhancement using UV/Ozone treatment of microtomed starch samples improved the characterization of growth rings on the starch samples. The observation of growth rings in the non-vitreous starch granule surfaces indicates that amylopectin is more common than amylose compared to the composition of vitreous starch.

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tomographyQuantitative characterization of pore topology inside grain bulks is necessary to predict the air traverse time and the cooling or fumigation pattern for the design of storage management strategies. In this study, inter-connected 3D array of void spaces was characterized by geometrical quantities such as specific surface area, pore throat size and nodal pore volume. These features were obtained from a 15 cm X 15 cm X 5 cm volume of wheat and pea grain bulks. The grain bulks were imaged using a high resolution X-ray computed tomography system at 200 micron resolution. The spatial distributions were computed based on 3D medial axis analysis of the void space in the images using 3DMA Rock software and a high performance Polaris computer. The other features calculated were medial axis tortuosity, throat surface area and porosity from the 3D images. Characterization of pore throat network provides reliable observation for facilitating realistic prediction of permeability and the nature of air and gas distribution inside grain bulks.

3d grainThe knowledge of distribution of pore space inside grain bulks is essential for determining the airflow resistance of grains. In this study, the internal pore structure and the 3D-distribution of air paths inside grain bulks were studied using X-ray computed tomography images. Image analysis methods were applied to the binary 3D X-ray CT images on the spatial distribution of voids to generate the connected, individualized pore objects of different size and shapes. Morphometric parameters such as 3D air path volume distribution, structure separation factor, Euler number, fragmentation index, structure model index were calculated based on hexahedral marching cubes volume model and marching cubes 3D surface construction algorithm. The quantified numerical measures of spatial integrity of air path networks were analyzed and compared with the air flow resistance of grain bulks. The results showed that the connectivity of airspace and the non uniform distribution of air path network inside grain bulks were responsible for the difference in airflow resistance between horizontal and vertical directions to the airflow of grain bulks.

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wheatCanadian Grain Commission has stringent regulations on the cleanliness and uniformity for both domestic and export wheat. Machine vision has made a considerable headway in classification and grading of cereal grains. Quite robust machine vision algorithms have been developed to extract morphological, color and textural features and have been evaluated on wheat, barley, oats, rye, etc. Sensitivity analysis of these algorithms to foreign matter needs to be carried out to develop confidence before they can be applied to on-line monitoring of grain samples. The samples used in this study were known quantities (0.5 to 10%) of barley/chaff mixed in Canada Western Red Spring (CWRS) and Canada Western Amber Durum (CWAD) wheat. Machine Vision algorithms developed at Biosystems Engineering, University of Manitoba were used to analyze the sample images for image extraction and classification. Statistical classifiers were used to classify the foreign matter in the wheat samples. Algorithms were checked for predetermined mixtures of chaff/barley in wheat until they failed to identify a given percentage of foreign admixtures.

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