CA2280364A1 - Grading system for particulate materials especially cereal grains - Google Patents

Description

This invention relates to a system and process and apparatus for the classification and sorting of wheat and other cereal grains using machine imaging techniques.
Inspection of wheat or other cereal grains is a subjective process. A current system of wheat and cereal grain grading is based upon Kernel Visualization Distinguishability or Single Kernel Characterization (Psotka, J. 1999) as determined on site by the local grain inspector or agent. As such, the process is subject to the vagaries of continual human intervention, e.g. personal biases, fatigue, poor training, etc. Furthermore, the standards for the system are set on a yearly basis and, in many cases, are reset during harvest if the weather conditions become extreme, i.e. if there is too much rainfall or if an early frost has occurred. Hence, there may not be time to retrain all staff at all terminal locations or grain elevators adequately to provide a standard inspection system.
Inspection of wheat or other grains is a labour intensive process. At each wheat and cereal grain elevator and at each terminal an inspection of the wheat or grain must be carried out. Such inspection may include weighing, determination of dockage, determination of moister content, determination of protein content, detection of the presence and extent of fungal growth, detection of the presence and extent of the chemical residues, etc. Furthermore, inspection of a grain shipment performed at an elevator may be repeated at a terminal if an extended period of time has

- 2 -lapsed or if the shipment is selected for a foreign market or specialized market. Hence, multiple and detailed inspections of wheat and cereal grains are required in many cases to assure their quality.
There is no inexpensive objective system available to farmers for such inspections so that they may judge accurately the quality of grains from different areas of the farm. If such a system were available a farm could avoid mixing a batch of high quality grain with a batch of poor quality grain with the result that the quality -- and hence the financial recompense -- may be reduced for the mixed batches. Of course, a competent farmer can judge subjectively but, at least in marginal cases, subject judgement may lead to irretrievable mistakes in strategy.
Once differing grades of grain have been mixed, they can not be separated.
Optical scanning and machine imaging of material for quality control is a standard practice in industry.
Application of this technology to the classification of wheat and other cereal grains has been investigated using Fourier analysis of grain shape and relative position of the centre of gravity of the kernel (Segerlind and Weinberg 1972, Barker, Vouri and Myers 1992), pattern classification using recursive learning (Brogan and Edison 1974), statistical pattern recognition based upon morphological features (Zayas, Pomeranz and Lai 1985, Zayas, Lai and Pomeranz 1986, Neuman et al. 1987, Symon and Fulcher 1988a,

- 3 -1988b), statistical pattern recognition applied to three-dimensional images (Thomson and Pomeranz 1992), statistical pattern recognition applied to colour images (Neuman et al.
1989a, 1989b) and pattern recognition using neural networks as applied to colour images (Egelberg, Mansson and Peterson 1994, Lou 1997). The usefulness of these algorithms in solving classification problems related to wheat and other cereal grains in real-time applications have been demonstrated in the determination of corn kernel size distribution (McDonald and Chen 1990), in particular, and morphological classification problems in general (Haralick, Sternberg, and Zhuang 1987, McCubbery and Lougheed 1985, Kimmel et al. 1985).
Given the demands of wheat and cereal grain classification mentioned above, it would be desirable to couple computer algorithms and high-speed computers which can implement these algorithms in real-time with a real-time image processing system to the classification of wheat and other cereal grains and also other particulate material.
Accordingly the invention provides a system for classification of particulate material e.g. grains such as cereal grain in comparison with a set standard, the system comprising imaging means for providing images of grains in a sample of said grains, computer means programmed to grade the sample in comparison with said set standard from data obtained from the images.

- 4 -The sample of said grains or other particulate material may be presented to the imaging means scattered on a transparent sheet to display at least some individual grains, which transparent sheet conveys the grains past said imaging means. The transparent sheet may by Mylar (trademark) .
Indeed, for any system for display of individual grains, it is advantageous to convey the grains past a display point on a transparent conveyor. Any stationary display surface would accumulate dust, dirt and debris which would eventually lead to inaccurate grading.
The transparent sheet may be conveyed from a roller over, for example, a colour linear photo diode array of a flatbed scanner by means of a winding powered roller which winds used dusty or dirty Mylar.
The set standard refers to qualities of different characteristics of the grains, for example, colour, size, roundness, roughness -- to mention a few.
The standard may be altered from time to time. For example, in Canada for wheat, the standard may be set by the Canadian Grain Commission. If the quality of wheat consistently rises for several years due possibly to improved environmental conditions and improved strains of wheat, it may be desirable to reset the standard upwardly.
In other countries, standards may be set differently.

_ 5 _ Whatever the standards, it is probably that similar characteristics may be measured.
Any chosen standard may be made universal by the publication on the Internet which may be downloaded by interested parties, e.g. farmers, elevator operators, etc.
The computer program for the grading system may then be updated using suitable simple software.
The sample should be of a size such as to be representative of the batch of granular material to be graded. Indeed, there is different samples from different regions of the batch may be taken. However, the sample size presents some difficulties in obtaining fast results. It is preferable that the system should incorporate statistical analysis software including an automatic learning network (ALN). This allows for the setting up of a linear equation so that statistical clusters may be evaluated in the grading procedure. Of course, all final adjustments of parameters must remain in the hands of the standard maker and these parameters are programmed into the computer before operation of the system.
Such a system for grading a small sample may be used as a farm gate machine for the convenience of the farmer. It may also be configured as intermediate size apparatus for use , for example, at small grain elevators. It may alternatively be utilized as a stage in large scale apparatus for grading. Such apparatus may be utilized at grain elevators, grain terminals and the like. In such apparatus the volume and weight of the grain are determined, and the grain is measured for dockage. Any presence of a chemical residue is determined, possibly using a gas mass spectrometer. The protein and moisture content are measured utilizing a NIR (near infrared) scanner. Fungal growth is detected using an ultraviolet excited fluorescent scanner.
The W excited fluorescent scanner may be a sophisticated apparatus component capable of not only showing fluorescence when fungal growth is present but also indicating its extent. The grain may then pass to a singulator for division into discreet samples each of which may be inspected automatically utilizing the system described above. It may be more convenient to utilize a camera, for example a linear array digital or analogue camera, in place of the flat bed optical scanner previously referred to.
Also, in such large scale apparatus it may be possible to provide some separation of the differently graded grains downstream of the imaging means, e.g. the camera. Such separation of the grains may be, for example, by pneumatic means such as air sorter. It is possible that other means such as knife-edge separator may be used. If it desired to isolate any particular group of grains such as wild oats which are difficult to detect in conventional dockage measurement, a proximity detector may be utilized to indicate the region of these grains and the sorting means may be applied in that region.

The invention includes a method of grading particulate material utilizing the system of the invention.
Embodiments of the invention will now be described with reference to the drawings in which:
Figure 1 shows a schematic view of a small scale system for grading a manually loaded sample;
Figure 2 is a flow-chart showing the operations of the system illustrated in Figure 1;
Figure 3 is a schematic view of a grading system for individual grains as part of a large system for bulk grading; and Figure 4 is a flow chart showing the operations of the system of Figure 3.
Figure 1 shows a simple system which may be provided quite inexpensively and which is simple to operate. A
farmer may use it on his own premises to give information concerning batches of grain, for example, wheat, from different regions of his farm. In this case a small sample, for example one cup full, of grain is utilized.
A sample of particulate material 14 is scattered in a single layer onto the surface of a transparent Mylar film 16. Scattering may be automatically controlled from a sample container 12 which may be utilised to ensure samples of uniform volume. However, scattering may equally be manual.
The Mylar film 16 is conveyed above a colour linear _ g _ photodiode array 18 from a free roller 20 onto a driven roller 22. Tension may be maintained on the Mylar sheet using a braking system 23 of any suitable type on roller 20.
The driven roller 22 may be driven by motor 24.
The sample 12 scatters indiscriminately on the Mylar sheet carrying with it any dockage such as dust, dirt or other debris scooped up with the sample. It should be noted, however, that it is entirely possible to utilize the system as part of a large scale grading operation. In this latter case, it is probable that the grain supplied onto the Mylar sheet will be single line or multiple line feed of individual grains.
The Mylar sheet between rollers 20, 22 may be supported on a transparent platen 26 between the Mylar sheet 16 and the photodiode array 18.
A vacuum hold down device 28 is utilized to remove air from between the Mylar sheet 16 and the platen 26.
The photodiode array 18 may be any convenient array sensitive to coloured light and calibrated using a standard RGB palette. Each photodiode is typically 1024 pixels mounted transversely to the direction of motion, each pixel being 50~m wide (e. g. an EG&G Reticon K-series wide aperture linear photodiode array and necessary colour filters). A
source of light 30 illuminates the sample 12, the reflected light from the sample being received by the photodiode array 18.
More than one photodiode array may be mounted parallel to each other and transversely to the sample flow, each photodiode array possibly having its own source of light.
Alternatively, the photodiode arrays may be arranged in longitudinal, staggered rows each array extended transversely to the direction of motion.
While this photodiode array system is described with reference to a randomly scattered sample on the Mylar sheet 16 it is worth noting that when the system is part of larger grading system and allowing ten line feeds of grain from a singulator to cross a 1024 pixel photodiode array, then 200 grains of the sample may be analysed and classified per second.
The photodiode array 18 sends details to computer 32 utilizing an automated learning network 33(ALN).
Individually scanned grains may be allocated a performance number for each characteristic. The performance numbers are allocated in accordance with the command program of the computer which itself is conformed to the standards set by the standards maker. Statistical analysis software, including the ALN, sets up a linear equation so that the computer may recognize statistical clusters of certain characteristics. Each characteristic has a vector and similar vectors are grouped together. The vectors are the detected values composed of parameters like length, width, aspect ratio, convexity, first and higher order moments of pixel coordinates, averaged IGB colour coordinates, and morphological parameters like roughness of perimeter ,etc.
Thus there is little possibility, in view of the statistical grouping, that, for example, some shrivelled grain would be confused with frost bitten grain.
The computer system itself may comprise a central microprocessor of any suitable type for example a 586 (Pentium) microprocessor may be used. The computer system also comprises all necessary peripherals for the storage of data, reproduction of data and/or analysis performed on the data, connections to the various components of the machine and any external connections deemed desirable for the efficacious operation of the machine.
Figure 2 shows a flow chart showing the sequence of operations described with reference to Figure 1.
Figure 3 shows a schematic view of another embodiment of the invention in which a system such as that described with reference to Figure 1 is included as part of a large scale grain grading operation. It is to be noted that the system previously described with reference to Figure 1 is itself a different embodiment when described with reference to Figure 2. The photodiode array 18 has been replaced by a camera system with resulting minor changes in mechanical structure. The camera system is described by way of a mere alternative imaging means but it is possible that the camera system may be more suited to the case where the sample is presented as a single line feed.
A sample of wheat or other cereal grains is selected randomly or by whatever means is the usual procedure for the inspector and passes through a standard sampler device 42 (see Figure 4) (e.g. a Winchester bushel), in order to determine the weight and volume of the sample. The sample is then passed to device 44 (see Figure 4) for measuring the dockage (e. g. a Seedburo Star docket tester or a Carter-Dockage tester). It is placed on a conveyor 46 for example a vibratory chute, and is passed through an NIR scanner 48 (e. g., a standard ultraviolet "black-light" and NIRS Systems Model 6500 scanning monochromator operating in reflectance mode between 400 and 6500 nm) scanning for fungal damage, moisture content and protein content. Next the sample may pass onto an aspirator e.g. a Bates Laboratory Aspirator.
It also passes gas mass spectrometer 52 used to detect any residues of insecticides, herbicides, etc., and grain determinants, e.g. bin burn. It should be noted that in this embodiment any of devices 42, 44, 46, 48, and 52 may be interchanged in position or wholly removed or any combination thereof depending upon the desired application, cost, etc. The standard maker is likely to use a large scale machine which allows for direct verification of accuracy. The standard can then be applied to small and intermediate scale machines and to other large scale machines.

The sample 40 now passes onto device 54, a singulator (e.g. Seedburo Count-a-Pak Seed Counter) which produces a single line or multiple lines of wheat or other cereal grains. Upon exiting from device 54 the singulated sample 40a falls under the force of gravity onto belt conveyor 58 and is scanned using a series of two or more line scan cameras 60 (e. g. LC 1911 Reticon Modular Line Scan Cameras which may operate with 256, 512, 1024 or 2048 elements and have pixels of l3,um or 26,um and may operate up to 35000 scans per second). Cameras 60 are equipped with all of the necessary optics and filters to be colour sensitive and are calibrated using a standard RGB palette. More cameras and channels from the singulator(s) may be used to increase the speed with which a sample may be analysed. The height through which the sample falls may also be varied as desired to meet any space requirements, etc. After passing cameras 60 the sample is separated by air sorter 62 or some other suitable device which allows some of the sample to pass and fall to a suitable receptacles 64, and the remainder of the sample to continue on to the next conveyor system.
The singulated sample 40b then passes onto another conveyor belt 66 or other transport system, and is transported past a plurality of air sorters 68 each associated with a proximity detector, which allows for an unambiguous determination of the location and sorting of the individual wheat or other cereal grain kernels. The air sorters 68 remove the individual wheat or cereal grain kernels and place them into final containers 70.

The cameras 60 are linked with a computer32 and ALN 33 similar to that described with reference to Figures 1 and 2 in a similar manner.

Claims