EP0658262B1 - Procede et dispositif d'evaluation automatique de cereales et autres produits granulaires - Google Patents

Procede et dispositif d'evaluation automatique de cereales et autres produits granulaires Download PDF

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Publication number
EP0658262B1
EP0658262B1 EP93919788A EP93919788A EP0658262B1 EP 0658262 B1 EP0658262 B1 EP 0658262B1 EP 93919788 A EP93919788 A EP 93919788A EP 93919788 A EP93919788 A EP 93919788A EP 0658262 B1 EP0658262 B1 EP 0658262B1
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Prior art keywords
kernels
kernel
belt
picture elements
area
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EP93919788A
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German (de)
English (en)
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EP0658262A1 (fr
Inventor
Rickard ÖSTE
Peter Egelberg
Carsten Peterson
Patrik Söderlund
Lennart Sjöstedt
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Agrovision AB
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Agrovision AB
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
    • B07C5/34—Sorting according to other particular properties
    • B07C5/342—Sorting according to other particular properties according to optical properties, e.g. colour
    • B07C5/3425—Sorting according to other particular properties according to optical properties, e.g. colour of granular material, e.g. ore particles, grain

Definitions

  • the present invention relates to a method and a device for automatic evaluation of cereal kernels or grains and similar granular products, e.g. beans, rice and seeds, which are handled in bulk.
  • Each shipment of cereals may contain a certain amount of kernels of some other kind of cereal than the desired one, for example rye and wild oats in shipments of wheat, and of kernels which per se are of the desired kind but which are of unsatisfactory quality, for example broken-off kernels, kernels chewed by animals, green kernels and burnt kernels. Also stones and other objects are to be found among the kernels.
  • the size of the kernels can be evaluated. This is now carried out by letting the kernels pass a number of sieves having a gradually diminishing width of mesh. It is desirable that the size evaluation can be carried out in a more rational manner.
  • Scientific literature comprises examples of experiments being made to evaluate cereals by means of computerised image analysis.
  • an image analysing program could identify kernels with an accuracy of more than 97%.
  • German patent publication DE 34 43 476 describes a method and a device for testing and sorting granular material, in particular for testing and sorting unpeeled or. damaged grains from peeled undamaged grains.
  • the device is equipped with a camera for imaging grains on a conveyor belt.
  • the damaged grains are identified by means of a colour comparison and are blown away by means of pressurised air.
  • This method and device cannot be used for evaluating the amounts of different grains in a sample handled in bulk as it results in a binary classification, i.e. the grains are either classified as undamaged or damaged.
  • GB 2,012,948 discloses a method of determining the distribution of sizes for samples of, inter alia, cereal kernels.
  • the kernels are caused to fall between a screen which is illuminated by a stroboscope, and a video camera by means of which images of the kernels are produced.
  • the video images are digitised and the kernels are identified in the images.
  • the distribution of sizes of the kernels in the sample is determined.
  • WO 91/17525 discloses a method for automatically classifying an object into predetermined classes.
  • a video camera takes time-domain images of objects which are carried one by one on a conveyor belt past the camera.
  • the time-domain images are transformed by Fourier analysis into frequency-domain signals which form input signals to a neural network effecting the actual classification.
  • the object of the present invention is to provide a method and a device for automatic evaluation of granular products handled in bulk, especially cereal kernels, which method and device can replace the human inspection and evaluation.
  • a method and device for automatic evaluation of granular products handled in bulk, especially cereal kernels, which method and device can replace the human inspection and evaluation.
  • it must be possible to analyse a sample in about the same time it takes today to analyse it manually. More precisely, this means that it must be possible to classify and determine the weight of a sample, of about 1500 cereal kernels, in about 5 min.
  • the accuracy in the classifying procedure must be high. For example, it must be possible to determine the percentage weight distribution of the different components in a wheat sample with an accuracy of about 0.2% of the weight of the entire sample. Since a sample of cereals may contain stones and other foreign objects, it is also required that such objects are identifiable in the evaluation.
  • the method and the device according to the invention bring the advantage that a sample of cereal kernels can be analysed at least as quickly as if the analysis were carried out manually.
  • This is rendered possible in that a plurality of kernels at a time are presented to a device which produces digital images of the kernels, each image containing a plurality of kernels, but each kernel occurring in one image only.
  • the kernels are preferably oriented in one direction. Since the kernels are presented in this manner, they can quickly and reliably be identified in the digital images.
  • the classification of the kernels is carried out by means of a neural network whose input signals are based on the picture element values of a plurality of picture elements representing the kernel.
  • picture element value is here meant a value which is used to represent the picture element; for example the intensity in monochrome images; red, green and blue intensity in RGB representation in colour images; hue, saturation and intensity in HSI representation in colour images.
  • the input signals to the neural network by providing a weighted addition of the picture element values for a plurality of picture elements, thereby compressing the information contents of the picture elements representing a kernel.
  • kernels classified into one or more definite classes can be physically separated after the classification procedure, whereupon the separated kernels are weighed separately as are the non-separated kernels, thereby determining the weight of the different fractions.
  • the extent of each coherent area of picture elements representing a kernel is determined perpendicular to the longitudinal axis of the area, and it is investigated whether the extent has a minimum (or a plurality of minimums) in some other place than at the ends of the area. If this is the case, the image is estimated to contain two (or more) kernels and is divided at the minimum(s).
  • the morphological properties of the kernels can be determined by means of the picture elements representing the kernel.
  • Fig. 1 illustrates an embodiment of a device according to the invention, the feeding device being shown in longitudinal section and the image processing device as a block diagram
  • Fig. 2 is a schematic side view of a separation device which may supplement the device in Fig. 1
  • Fig. 3 is an end view of the separating device in Fig. 2, and a scale.
  • the invention essentially comprises a feeding device 1, a video camera 40 and an image processing device 2.
  • the feeding device 1 comprises a first belt conveyor 3 arranged in a casing 4 and having a first wheel 5 driven by a motor (not shown), a second wheel 6, and an endless belt 7 running over the wheels 5, 6.
  • the belt 7 is formed with grooves 8 in which the cereal kernels are portioned out.
  • the belt may have indentations designed in some other manner.
  • the casing 4 contains a store 9 which tapers off to the belt 7 and which is filled with samples of cereal kernels.
  • the store 9 comprises two plates 10, 11 which are inclined towards one another. The lower end of the plate 10 is spaced from the belt 7, and a scraper 12 is attached to this end to take down the cereal kernels into the grooves 8.
  • a second belt conveyor 15 is arranged vertically and horizontally offset relative to the first belt conveyor 3.
  • the second belt conveyor 15 comprises a first wheel 16 driven by a motor (not shown), a second wheel 17 and an endless belt 18 running over the first and second wheels 16, 17.
  • the belt 18 is formed with grooves 14 in which the kernels are conveyed.
  • the grooves 14 in the second conveyor are closer to each other than those in the first conveyor, and their width is adjusted to kernels in a given size interval such that the kernels orient themselves in the longitudinal direction of the grooves.
  • the colour of the belt is selected to provide a strong contrast to the background.
  • the first wheel 16 of the second belt conveyor 15 is arranged below the second wheel 6 of the first belt conveyor 3 such that cereal kernels can fall down from the first conveyor 3 onto the second conveyor 15.
  • Two plates 20, 21 are arranged between the first belt conveyor 3 and the second belt conveyor 15. When the kernels fall from the first conveyor, they bounce first against the plate 20 and then against the plate 21, the kernels thereby spreading.
  • At the sides of the second belt conveyor there are arranged, adjacent its first wheel 16, limiting means 22 serving to locate the kernels from the beginning at a certain distance from the edges of the belt 18.
  • the front end of the limiting means 22 in the belt direction is provided with a curtain 23 which is arranged to pass down the kernels into the grooves of the endless belt 18 and ensure that the kernels form one layer and that they do not overlap each other.
  • a vibrator 25 Between the first wheel 16 and the third wheel 19, and between the upper and lower reach of the belt 18, there is arranged a vibrator 25.
  • the vibrator comprises a shaft 26 to which one end of a metal sheet 27 is attached. Its other end is arranged between a roller 28 driven by a motor (not shown), and the lower side of the belt 18.
  • the end surface of the roller 28 is fitted with three washers 29, mounted with play by means of screws.
  • the amplitude of the vibrations is determined by the position of the roller 28 and the play of the washers.
  • the amplitude should be the same, independently of the rigidness of the belt.
  • a tooth detecting unit 31 Adjacent the third wheel 19, there is arranged a tooth detecting unit 31. This is mounted on one side of the circumference of the third wheel 19 and comprises a light emitter in the form of a light diode 32 and a light receiver in the form of a photocell 33.
  • the tooth detecting unit 31 is connected (not shown) to a computer 42. When the third wheel 19 rotates, the tooth detecting unit 31 emits a pulse-shaped signal to the computer 42.
  • the third wheel 19 also serves to damp vibrations in the belt 18 in the area between the third wheel 19 and the second wheel 17.
  • a video camera 40 in such a manner that images of the belt 18 in the vicinity of the second wheel 17 can be taken.
  • an annular lamp 41 between the camera and the belt is arranged.
  • the camera 40 is connected to the image processing device 2 whose design and function will be described in more detail below.
  • a sample of cereal kernels is poured on to the first belt conveyor 3 through the store 9.
  • the kernels then form a heap on the belt, but when the belt moves, they will, owing to the upward inclination of the belt and through the scraper 12, be spread portionwise in the grooves 8 of the belt.
  • the limiting means 22 preventing the kernels from landing on the edges of this belt. Owing to the vibrations of the second belt 18, the advancing kernels will move sideways in the grooves towards the edges of the belt. The kernels positioned on the ridges between the grooves will fall down into the grooves.
  • the kernels When the kernels reach the area under the video camera 40, they will therefore be separated in the longitudinal direction of the belt, be oriented in essentially the same direction and be positioned in essentially one layer on the belt.
  • the kernels will thus overlap each other but to a very small extent.
  • the kernels may, however, lie close together in the grooves in the longitudinal direction thereof.
  • a stop signal is emitted, and the computer 42 stops all driving motors. Then the first and the second belt stop, and the vibrations are discontinued. After a short wait, the computer 42 emits a signal to the video camera 40 which takes an image of the kernels on the belt 18. Subsequently, the motors are started again, and the feeding of the kernels continues as described above until a stop signal is again emitted.
  • the reason why the system waits after the belt conveyor has stopped is that any movements of the kernels should be damped such that the kernels lie still.
  • the third wheel 19 contributes, as mentioned above, to the reduction of the amplitude of the vibrations in the area under the camera 40 such that the waiting time can be kept short.
  • the predetermined number of teeth after which the stop signal is emitted is selected such that the video camera will take images of the belt which cover the belt without interspaces, but without overlappings. In other words, each kernel passing the video camera will occur in exactly one image, and each image will include a plurality of kernels.
  • the belt can be moved continuously and the lamp 41 can be replaced by a stroboscope which together with the camera 40 is controlled such that images are taken of the belt without interspaces and without overlappings.
  • the image processing unit 2 fundamentally comprises a computer 42 connected to the video camera 40, and a user terminal 43 on whose display device the result of the analysis is presented.
  • the computer 42 there are programs for classification and other evaluation of the cereal kernels based on the images produced by the video camera 40. These programs comprise a conversion of the video signals from the camera 40 into suitable input signals to a neural network program which effects the actual classification.
  • the digitised image produced consists of e.g. 512 x 512 picture elements.
  • the picture elements are represented by RGB representation, i.e. by a value of the intensity of red colour, a value of the intensity of green colour and a value of the intensity of blue colour. Alternatively, a grey scale or some other colour representation may be used.
  • the program locates the kernels in the digitised image.
  • a threshold value of the colour in each picture element In order to simplify the processing in this step, it is advantageous to pass from RGB representation to HSI representation (Hue, Saturation and Intensity).
  • RGB representation Hue, Saturation and Intensity
  • the program examines the image point by point, line by line. When it finds a picture element representing a kernel, it examines all neighbouring picture elements. For those picture elements of the neighbouring picture elements which are considered to represent a kernel, the procedure is repeated until all picture elements connected with the first picture element have been identified.
  • the longitudinal axis of the connected picture elements is determined to represent a kernel. If the direction of the longitudinal axis deviates by more than a predetermined value from the y axis of the image, the coherent kernel area is rotated until its longitudinal axis is parallel with the y axis of the image.
  • the coherent kernel area identified in the image may thus represent more than one kernel.
  • the number of picture elements in x direction which represent a kernel is summed up for each y value in the coherent kernel area.
  • the program thus makes a histogram of the number of kernel picture elements in x direction. Then an envelope curve of the histogram is determined, and it is investigated whether there is a minimum between the envelope curve terminal points in y direction. A sufficiently marked minimum indicates that the coherent kernel picture element area actually corresponds to two kernels.
  • the program makes a cut in parallel with the x axis at the minimum of the envelope curve. Subsequently, each part of the coherent kernel picture element area is stored as an image of a kernel. If there are a plurality of minimums, a cut is made at each minimum. If a separation of a kernel picture element area has been carried out, the longitudinal axis of each kernel is determined, and the kernel is rotated, if the deviation from the y axis of the image is greater than the predetermined value.
  • the size of each kernel can be determined by counting the number of picture elements in the coherent picture element area representing the kernel. Also the shape and colour of each kernel can be determined by studying the picture elements.
  • the size determination can also be used to avoid that the image processing device perceives stones and other foreign objects that may join the kernels, as kernels. If the size of a coherent picture element area is not within a certain interval, it is considered to represent a foreign object and is registered as such.
  • the RGB values of the picture elements are converted into HSI values. This conversion is not necessary, but it has appeared that the classification of cereal kernels will be more correct if HSI representation is used instead of RGB representation.
  • the H values are summed up separately, the I values are also summed up separately, as well as the S values, along rows and columns in the image of a kernel.
  • the values of the H component of all x coordinates are thus summed up.
  • the corresponding addition for the I value and the S value is carried out.
  • a weighted addition is carried out for each x coordinate for the H value of all y coordinates, whereupon the weighted addition is repeated for the S and I values.
  • the program thus produces one histogram in x direction and one in y direction for each picture element component. This results in a large number of sums.
  • the standardised sums constitute input signals to a neural network.
  • a neural network is a program consisting of a number of input nodes, in this case one for each sum, and a number of output nodes which in this case represent each of the possible classes into which the kernels can be classified. Between the input nodes and output nodes, there are hidden nodes. By feeding input signals representing known kernels to the neural network and telling it into which class the kernel should be classified, the neural network can be trained to classify kernels correctly. When the neural network has learnt to classify the different interesting kernels, it can be used to classify previously unseen kernels.
  • the hidden nodes are sigmoid functions, which makes it possible to adapt input data to a substantially arbitrary (linear/non-linear) function. If the classes are linearly dependent on the input nodes, the network is trained to effect a linear discriminant adaptation.
  • the neural network method thus comprises linear discriminant adaptation as a special case.
  • Each output node is represented by a value between 0 and 1.
  • a kernel is evaluated as belonging to the class whose corresponding output node has the greatest value.
  • random samples are taken before the classification, it is determined which kind of cereal is predominant, and this is reported to the neural network. If the highest output node value goes below a predetermined value, and the output node which is favoured has the second greatest value, then the kernel is not classified into the class whose output node has the greatest value, but into the class whose output node has the second greatest value.
  • Foreign objects are defined by the value of all output units being lower than a given threshold value.
  • the result of the classification is presented on the display device of the user terminal 43, for example in the form of a histogram with a bar for each kind of cereal, one for wild oats, one for burnt kernels and one for damaged kernels.
  • the result can be presented in % by weight of the sample. It has in fact proved to be possible to determine the weight of the kernel by means of the size of its image, since there is a connection between these parameters, which can be determined empirically. In the evaluation, thus the number of picture elements which represent the kernel involved is counted. Based on this number, the size and weight of each kernel can be determined and, consequently, the weight and size distribution of the entire sample.
  • the shape and colour distribution of the sample can also be determined based on the input signals to the neural network.
  • the Table below shows an example of ten analyses of a 50 g cereal sample which has been analysed by means of a device according to the invention.
  • the sample consisted of 5.00% rye; 5.00% oats; 5.00% barley; 5.00% burnt wheat kernels; 0.00% wild oats; 5.00% damaged wheat kernels and 75.00% wheat.
  • x is the average and s(x) is the standard deviation. All values are % by weight of the weight of the sample.
  • the weight of the different fractions can be determined by means of the arrangement schematically shown in Figs 2 and 3, by which the device in Fig. 1 may be supplemented.
  • the arrangement is mounted at the end of the second belt 18 after the position in which the camera 40 takes an image of the kernels on the belt.
  • the arrangement comprises a third belt 51 which constitutes a cover over the second belt 18 and which is driven synchronously therewith by means of a toothed belt 60 connecting the wheel 17 of the second belt 18 to a toothed shaft 61 of the third belt.
  • the third belt 51 comprises alternating grooves 51a and ridges 51b which are aligned with grooves 18a and ridges 18b in the second belt 18, thereby forming a plurality of channels 62 between the sides of the second and third belt facing each other.
  • the arrangement in Figs 2 and 3 further comprises a separating means for each channel formed by the belt and the cover.
  • the separating means comprises a compressed-air source 52 and a pipe 53 connecting the compressed-air source with the mouth of the corresponding channel, when the cover 51 is lowered onto the belt.
  • On the other side of the belt there is a container 54 directly opposite the mouths of the channels.
  • Below the end of the belt 18 there is arranged a further container 55 on a scale 56.
  • the first container 54 can be connected to the second container 56 via a duct 57.
  • a wheat sample with an admixture of rye in which the weights of the wheat fraction and the rye fraction, respectively, are to be determined.
  • the computer 42 in a first moment, identifies one or more rye kernels in an image taken by the camera 40.
  • the belt 18 has advanced one step for the camera to take the next image, then the surface of the belt, on which the kernels in the preceding moment were analysed, will be covered by the third belt 51, and the identified rye kernels will be positioned in one or more of the channels 62.
  • the computer 42 activates the compressed-air source(s) 52 in whose corresponding channels a rye kernel has been identified.
  • the rye kernel and wheat kernels, if any, which are positioned in the same channel, are blown into the container 54, whereupon the belt 18 can be advanced when the next image should be taken.
  • the kernels remaining on the belt 18, which thus are wheat kernels, fall down into the container 55 as the belt advances.
  • the wheat kernels are weighed in the container 55 by means of the scale 56. Subsequently, the wheat kernels are emptied, and the rye kernels and the wheat kernels, if any, in the container 54 are let down into the container 55 and weighed.
  • the sample besides rye, contains an admixture of barley, the barley kernels can be blown into a special container and weighed separately.
  • Figs 2 and 3 can also be used to blow away objects which the computer cannot identify.
  • a signal is suitably emitted to an operator to request a manual check.

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  • Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)

Claims (14)

  1. Procédé d'évaluation automatique de grains de céréales ou produits granulaires analogues qui sont manipulés en vrac, comprenant des étapes de répartition des grains et de production d'images numériques des grains, telles que chaque image contient plusieurs grains, caractérisé en ce que chaque grain se trouve dans une image seulement, et en ce que le procédé comprend les étapes suivantes :
    la localisation des grains dans les images numériques par localisation de zones cohérentes d'éléments d'image ayant une intensité ou couleur qui dépasse une valeur prédéterminée,
    la détermination de l'étendue de chaque zone cohérente perpendiculairement à l'axe longitudinal de la zone,
    la division de la zone à chaque minimum, dans l'étendue ayant au moins une dimension prédéterminée et à distance des extrémités de la zone dans la direction longitudinale, chaque partie ainsi cloisonnée étant traitée comme si elle représentait un grain,
    la production d'un ensemble de signaux d'entrée d'un réseau de neurones (42) pour chaque grain à l'aide de valeurs d'éléments d'image pour plusieurs éléments d'image représentant le grain, et
    la détermination par le réseau de neurones (42) de celle de plusieurs classes à laquelle appartient chaque grain.
  2. Procédé selon la revendication 1, caractérisé en ce que les grains sont orientés essentiellement en direction prédéterminée avant la production des images numériques des grains.
  3. Procédé selon la revendication 1 ou 2, caractérisé en ce que la production des signaux d'entrée du réseau de neurones (42) comprend une addition pondérée des valeurs d'éléments d'image pour plusieurs éléments d'image représentant le grain.
  4. Procédé selon la revendication 3, caractérisé en ce que l'addition pondérée des valeurs d'éléments d'image est réalisée par composante pour chaque composante d'élément d'image parmi plusieurs éléments d'image représentant le grain.
  5. Procédé selon l'une quelconque des revendications 1 à 4, caractérisé en ce que les éléments d'image de l'image numérique sont représentés par les composantes d'intensité rouge, d'intensité verte et d'intensité bleue (représentation RGB), et les composantes d'éléments d'image sont transformées en une composante de teinte, une composante de saturation et une composante d'intensité (représentation HSI).
  6. Procédé selon la revendication 5, caractérisé en ce que la dimension et/ou la forme et/ou la couleur de chaque grain sont déterminées par les composantes de la représentation RGB dans l'image numérique.
  7. Procédé selon l'une des revendications 1 à 6, caractérisé en ce que le poids de chaque grain est déterminé d'après la dimension de la zone des éléments d'image représentant le grain.
  8. Procédé selon l'une quelconque des revendications 1 à 6, caractérisé en ce que, après le classement, les grains classés dans une première classe au moins sont séparés, puis les grains séparés sont pesés séparément et les grains non séparés sont aussi pesés séparément.
  9. Dispositif d'évaluation automatique de grains de céréales ou de produits granulaires analogues manipulés en vrac, comprenant une caméra (40) destinée à produire des images numériques des grains et un dispositif de présentation (14-19, 25-28) destiné à présenter plusieurs grains à la fois dans la couverture de l'objectif de la caméra (40), caractérisé en ce qu'il comporte un réseau de neurones (42) destiné à classer les grains, en ce que le dispositif est disposé afin que chaque grain se trouve dans une image seulement, et en ce qu'il est en outre destiné
    à localiser les grains dans les images numériques par localisation de zones cohérentes d'éléments d'image ayant une intensité ou une couleur dépassant une valeur prédéterminée,
    à déterminer l'étendue de chaque zone cohérente perpendiculairement à l'axe longitudinal de la zone,
    à diviser la zone à chaque minimum dans l'étendue de dimensions déterminées au moins et à distance des extrémités de la zone dans la direction longitudinale,
    à traiter chaque partie cloisonnée comme si elle représentait un grain, et à produire un ensemble de signaux d'entrée du réseau de neurones (42) pour chaque grain à l'aide des valeurs d'élément d'image pour plusieurs éléments d'image représentant le grain.
  10. Dispositif selon la revendication 9, caractérisé en ce que le dispositif de présentation (14-19, 25-28) comporte une courroie transporteuse (15), la courroie étant formée avec des évidements (14), la forme des évidements étant ajustée afin qu'elle corresponde à celle des grains et étant orientée dans une direction commune.
  11. Dispositif selon la revendication 10, caractérisé en ce que le dispositif de présentation (14-19, 25-28) comporte un dispositif de vibration (25-28) destiné à faire vibrer le transporteur (15) pour orienter les grains sur celui-ci.
  12. Dispositif selon la revendication 11, caractérisé en ce que le dispositif de vibration (25, 28) comporte une feuille métallique (27) dont une première extrémité est placée entre la courroie (18) et un élément vibrant qui comporte un rouleau (28) et plusieurs rondelles (29) qui sont montées sur le rouleau avec du jeu, si bien que les rondelles (29) frappent la feuille métallique (27) lorsque le rouleau (28) tourne.
  13. Dispositif selon l'une quelconque des revendications 9 à 12, caractérisé par un dispositif (51-53) de séparation de grains prédéterminés des grains restants après le classement.
  14. Dispositif selon la revendication 13, caractérisé en ce que le dispositif (51-53) de séparation comporte un dispositif (52) destiné à chasser par soufflage des grains prédéterminés du dispositif de présentation (14-19, 25-28).
EP93919788A 1992-09-07 1993-09-06 Procede et dispositif d'evaluation automatique de cereales et autres produits granulaires Expired - Lifetime EP0658262B1 (fr)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
SE9202584A SE470465B (sv) 1992-09-07 1992-09-07 Sätt och anordning för automatisk bedömning av spannmålskärnor och andra kornformiga produkter
SE9202584 1992-09-07
PCT/SE1993/000723 WO1994006092A1 (fr) 1992-09-07 1993-09-06 Procede et dispositif d'evaluation automatique de cereales et autres produits granulaires

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Publication Number Publication Date
EP0658262A1 EP0658262A1 (fr) 1995-06-21
EP0658262B1 true EP0658262B1 (fr) 2002-03-06

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EP (1) EP0658262B1 (fr)
JP (1) JP3588108B2 (fr)
AU (1) AU4990793A (fr)
DE (1) DE69331662T2 (fr)
DK (1) DK0658262T3 (fr)
ES (1) ES2170072T3 (fr)
SE (1) SE470465B (fr)
WO (1) WO1994006092A1 (fr)

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CN108764023A (zh) * 2018-04-04 2018-11-06 浙江大学 一种基于深度学习的传送带上物料检测系统

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ES2170072T3 (es) 2002-08-01
DE69331662D1 (de) 2002-04-11
SE9202584D0 (sv) 1992-09-07
SE9202584L (sv) 1994-03-08
JP3588108B2 (ja) 2004-11-10
SE470465B (sv) 1994-04-18
AU4990793A (en) 1994-03-29
EP0658262A1 (fr) 1995-06-21
WO1994006092A1 (fr) 1994-03-17
DK0658262T3 (da) 2002-05-21
DE69331662T2 (de) 2002-08-08

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