Classifications

• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T7/00—Image analysis
  • G06T7/60—Analysis of geometric attributes
  • G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
• • A—HUMAN NECESSITIES
  • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
  • A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
  • A01G17/00—Cultivation of hops, vines, fruit trees, or like trees
  • A01G17/02—Cultivation of hops or vines
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
  • G06F17/10—Complex mathematical operations
  • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T7/00—Image analysis
  • G06T7/0002—Inspection of images, e.g. flaw detection
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V20/00—Scenes; Scene-specific elements
  • G06V20/35—Categorising the entire scene, e.g. birthday party or wedding scene
  • G06V20/38—Outdoor scenes
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
  • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
  • G06Q50/02—Agriculture; Fishing; Forestry; Mining
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2207/00—Indexing scheme for image analysis or image enhancement
  • G06T2207/30—Subject of image; Context of image processing
  • G06T2207/30108—Industrial image inspection
  • G06T2207/30128—Food products
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2207/00—Indexing scheme for image analysis or image enhancement
  • G06T2207/30—Subject of image; Context of image processing
  • G06T2207/30181—Earth observation
  • G06T2207/30188—Vegetation; Agriculture
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2207/00—Indexing scheme for image analysis or image enhancement
  • G06T2207/30—Subject of image; Context of image processing
  • G06T2207/30242—Counting objects in image
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V20/00—Scenes; Scene-specific elements
  • G06V20/60—Type of objects
  • G06V20/68—Food, e.g. fruit or vegetables

Definitions

• the invention relates to the field of wine farming. More precisely, it concerns an automatic tool for helping to estimate a harvest volume in a vineyard.
• a winemaker In order to better manage a vineyard, a winemaker must have an estimate of the harvest volume of grape berries. This estimation can in particular enable him to anticipate on the human and material means to put in place to actually carry out the harvest, to optimize the organization of the harvest.
• This estimate can be determined by plot of a vineyard, to the extent that the conditions of terroir, micro-climates, cutting strategy and culture, etc. can influence the evolution of the vine in a very local way.
• this estimate can be used as a parameter to determine the best time to achieve this harvest. It can also detect abnormalities in the development of grape berries.
• Harvest performance is estimated based on the number of bays counted automatically by the vehicle and comparing this number with data from the previous year.
• the object of the present invention is to provide a system and method at least partially overcoming the aforementioned drawbacks.
• the present invention provides a method for estimating a harvest volume within a vineyard organized in a row of vines, comprising steps of:
• the invention comprises one or more of the following features which can be used separately or in partial combination with one another or in total combination with one another: the detection of bays comprises the determination of circular shapes within said image photographic;
• the determination of a number of berries on a cluster includes the use of an experimental model linking a number of berries visible on a photographic image at said number of berries on a cluster;
• the number of clusters on a vine is determined by an abacus depending on the grape variety of the vine and vine holding technique implemented by the operator of said vineyard;
• the harvest volume is dependent on a geographical location.
• a second object of the invention relates to a system for estimating a harvest volume within a vineyard organized in rows of vines, comprising:
• calculation means for detecting bays within said photographic image, determining a number of bays on a cluster, determining a number of bunches on a vine of said set and determining an indicator of said volume as a function of said number of bays on a cluster , said number of clusters on a vine and the diameter of said bays determined from said photographic image and said distance measurement.
• the system according to the invention comprises one or more of the following characteristics which can be used separately or in partial combination with one another or in total combination with one another:
• the calculation means are provided for detecting the berries by the determination of circular shapes within said photographic image; - The calculation means are provided for determining a number of berries on a cluster by the use of an experimental model linking a number of berries visible on a photographic image to said number of berries on a cluster;
• the calculation means are provided for determining the number of bunches on a vine according to an abacus depend on the variety of said vine and vine held by the operator of said vineyard.
• the system further comprises a location device to make said harvest volume dependent on a geographical location.
• Another aspect of the invention relates to a vehicle comprising a system as previously defined.
• FIG. 1 represents schematically an example of implementation of the invention
• Figure 2 shows schematically an example mapping a number of berries visible on a photographic image with a number of berries actually counted on a cluster.
• Figure 3 schematically shows an embodiment of the invention by means of a straddle.
• a winery is organized in rows of vines, so as to allow the operator to move between each row, including using a vehicle.
• the system S according to the invention is intended to flow between two rows RI, R2 of the vineyard.
• This system can be carried by a member of the farm staff. Preferably, it can be made integral with a vehicle that can be towed by a staff member or be a motor vehicle. According to one embodiment, this motor vehicle can be a robot.
• the vehicle can take the form of a tractor, a quad, a straddle, etc.
• Figure 3 illustrates the particular case of a straddle vehicle V.
• This figure shows a vine in a cross section. Three rows RI, R2, R3 are shown.
• Stradler is a vehicle with two legs, Jl, J2 ended by wheels and adapted to roll in the grooves formed by two successive rows. Each leg can roll in a groove distinct from the other, so that the body of the straddle can be located above the vines. This body may include a passenger compartment in which takes place a driver.
• the photographic device and the distance measuring means which will be described below, can be located on one of the legs of the straddle, at the height of the bunches of grapes of the vine.
• the system S has a DP photographic device arranged so as to capture photographic images of the vines.
• This photographic device is suitable for capturing photographic images. It can therefore be a digital camera, but also a video camera, or any other appropriate device.
• it can be arranged so that the axis of view is perpendicular to the axis of rows of vines and, therefore, the axis of movement of the system.
• This photographic device DP is arranged in the system S so that its elevation relative to the ground corresponds substantially to that of the bunches of grapes.
• This elevation can therefore be an adjustable parameter depending on the type of vine (grape variety) and the way in which the farmer prunes and exploits his vineyard.
• the DP photographic device can capture photographic images corresponding to an entire row.
• Each photographic image corresponds to a set E, depending on the distance d between the vines and the photographic device DP as well as the opening angle thereof.
• This set E corresponds to a set of vines, which can possibly be reduced to a single vine stock.
• the system S can thus be provided to capture series of photographic images covering each of the adjoining sets, so that all the photographic images for a row cover all the vines of a row.
• the system S captures photographic images according to a determined rhythm, generally periodic, which may be dependent on the speed of movement of the system. It can also have computing means MC designed to determine overlaps between photographic images to avoid taking into account several times the same photographed object (cluster, vine, bay ).
• a sampling may be implemented in order to capture photographic images only of part of a row.
• This sampling can be controlled by a random generator in order to avoid any bias.
• this sampling is performed on a significant fraction of the row.
• the system S has a lighting device, in particular to allow the capture of photographic images regardless of natural lighting conditions, but also to standardize these conditions of an image photographic to another.
• the lighting device may in particular be chosen from various possible lighting such as a halogen lamp, LED lighting, a flash, strobe or continuous lamp, etc.
• the photographic device can be adapted to capture photographic images of different natures.
• the photographic images captured can be monospectral, multispectral, hyperspectral ...
• Spectro-imaging also called “hyperspectral” imaging as opposed to “multispectral” or “superspectral” imaging, is a technology allowing the representation of a scene following a large number of spectral bands (generally more than one hundred), narrow ( ⁇ 10 nm) and contiguous.
• the photographic device can operate in different wavelength ranges. It can work in visible light, but also in ultraviolet or infra-red.
• the photographic device can operate in the visible range and in 3 bands: red, green, blue, in order to generate a color photographic image.
• the system can have two photographic devices in order to capture in parallel photographic images of the two opposite rows.
• a first DP photographic device may be located to the left of the system S (in FIG. 1) and capture photographic images of the row RI, while a second device, not shown in the figure, is located on the right and captures photographic images of row R2.
• the photographic images provided by the DP photographic device (s) are digital images allowing their processing by the calculation means MC, also embedded in the system S.
• the system S may further comprise means Dd for measuring a distance d between said set of vine stocks and the photographic device DP.
• These means can be a rangefinder for example. As will be seen later, this distance allows scaling of the captured image and transforming a measure into a number of pixels into a size in centimeters.
• the distance measuring means Dd can be integrated within the DP photographic devices. The detection of grape berries in a digital image can be carried out in different ways.
• the computing means MC determine the circular shapes within the digital image by detecting arcs of the circle. Indeed, a bay may be partially obscured by another bay located in a plane closer to the camera, so that only an arc is detectable. From the arcs of circles detected, it is possible to deduce the visible berries.
• the calculation means MC can then estimate the number of these bays (visible totally or partially) in the photographic image, as well as the diameter of the bays. An average diameter can be determined at this stage, or the diameters of each bay can be stored to allow more complex statistical calculations.
• the calculation means MC are then provided to determine a number of berries on a cluster.
• a photographic image only provides a two-dimensional view of a set of clusters.
• a cluster is a three-dimensional object, part of which is masked because it is opposed to the photographic device.
• One possible implementation is to use an experimental model linking a number of berries visible on a photographic image to said number of berries on a cluster.
• This model can be constructed by matching the results provided by the previous step on the number of berries visible for a cluster to a manual measurement of the total number of berries on that same cluster.
• Figure 2 illustrates such mapping.
• the x-axis indicates the actual number of bays per cluster (ie manually counted).
• the ordinate axis indicates the number of visible bays automatically determined by the computing means MC from a digital image representing the same cluster.
• Each cluster is represented by a point (corresponding to the legend "series 1").
• a next step of the method according to the invention, implemented by the calculation means MC, consists in estimating a number of clusters for a vine stock.
• This step is based on counting the number of clusters visible on the processed photographic image, for a given vine stock. Then, an abacus can be used to deduce the actual number of clusters on this vine. The abacus is also determined experimentally and allows to take into account clusters masked by the vine leaves. This abacus can depend on the type of vine variety but also on vine keeping techniques: depending on how the farmer prunes his vineyard, a different abacus can be used. From the number of berries on a cluster, the number of clusters on a vine and the diameter of the bays, the calculation means MC can estimate the harvest volume.
• the diameter of the berries is an important parameter since it allows to directly supply a volume of the berry and therefore a weight, and, consequently, a quantity of grape juice that can be extracted.
• the measurement of this diameter makes it possible to obtain an indication of the degree of maturity of a vine and to better determine both the right moment for a harvest and also the possible anomalies in the development of the vine.
• the harvest volume estimated by the steps of the invention makes it possible to have a good estimate of the yield of a vine and possibly to determine a moment optimizing this yield.
• the system S comprises a DL geolocation device.
• This device can be a simple GPS device ("Global Positioning System") or GNSS ("Global Navigation Satellite System”). It allows to associate each photographic image to a geographical position. Thus, it is possible to determine location-dependent volume indicators. For example, a function linking a crop volume indicator to the location can be determined.
• This geolocation of the indicator makes it possible to refine the knowledge that the operator can have of his wine exploitation. In particular, it makes it possible to adapt the response to be made more accurately and to detect local anomalies. If an anomaly is detected, the geolocation also makes it possible to intervene on the corresponding site.
• the calculation means can make it possible to distinguish berries in the pea stage from the determination of the average diameter of the berries. This allows an earlier estimate.
• the entire device is protected by a sealed housing fixed for example to the front of a vehicle.
• the device is robust to vibrations, dust, certain shocks and projections of liquids or sludge.
• the presence of the housing can also provide the device with protection against temperature variations.
• control of the system is based on a prior registration of the contour of the parcels to be visited.
• the DP photographic device can start taking pictures. The acquisition becomes completely automatic. It is not necessary to trigger shooting manually at the beginning and end of a row.
• the system comprises transmission means that send the images to a display means. This makes it possible to follow in real time the information obtained after the processing of the images and to check the quality of the images.
• the processing and analysis of the images can be done directly by the computing means MC integrated in the system.
• the processing and the analysis of the images are carried out by the computing means MC integrated in a computer located remote from the system and accessible via a telecommunications network.

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• 2015
  • 2015-05-29 FR FR1554862A patent/FR3036830B1/fr not_active Expired - Fee Related
• 2016
  • 2016-05-30 EP EP16733646.0A patent/EP3304416A1/de not_active Withdrawn
  • 2016-05-30 WO PCT/FR2016/051279 patent/WO2016193602A1/fr active Application Filing
  • 2016-05-30 US US15/578,062 patent/US10672138B2/en not_active Expired - Fee Related

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