• 专利附录
  • 专利说明
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    • 专利摘要:
    Procedure for the automatic estimation of the porosity of the vineyard by means of artificial vision (1), which comprises the following stages: A) capture an original rgb image (13) of a strain (10) with a digital camera (11) in the field; B) select in the original rgb image (13) the region of the image that corresponds to the production zone of the strain (10) to obtain an image with the selection of the productive region of the strain (14); C) segment the image with the productive region of the strain (14) by selecting seeds to obtain an image with the selection of the segmented strain (15) with the pixels that make up the strain (10) and with those corresponding to the background (12); D) analyze the image with the selection of the segmented strain (15) to identify the gaps present in it to obtain a classified binary image (16); E) calculate the percentage of holes in the strain (10). (Machine-translation by Google Translate, not legally binding)
    公开号:ES2550903A1
    申请号:ES201500551
    申请日:2015-07-15
    公开日:2015-11-12
    发明作者:Manuel Javier TARDÁGUILA LASO；Borja MILLÁN PRIOR；María Paz DIAGO SANTAMARÍA
    申请人:Universidad de La Rioja；
    IPC主号:
    专利说明:

    Procedure for the automatic estimation of the porosity of the vineyard through artificial vision 5.
    Object of the invention
    The present invention relates to a non-invasive procedure that allows estimating of
    10 automatically, and without the use of a manual witness, the porosity of the vineyard through artificial vision.
    The present invention is of great interest for the wine sector in general, and especially for crop management and grape quality improvement. 15 General and closest prior state of the art
    The exposed leaf surface of the vine is one of the most important parameters to control in the production of grapes of the highest quality. The optimal management of the
    2 O exposed leaf surface seeks to find the balance between maximum solar radiation capture to optimize photosynthesis and the existence of holes that allow air flow and adequate exposure of the fruits.
    The number of leaf layers determines the efficiency in the radiation uptake of the
    25 plant, since each leaf absorbs 94% of the photosynthetically active incident radiation [1] so that successive leaf layers will receive a very small proportion of it, being this lower than 1% in the third layer or later. This reduction in the radiation received causes that these leaves are not photosynthetically active, so the plant has to spend resources on maintaining them, which
    3 Or they will not be used in the ripening of the grapes.
    The optimization of the leaf surface of the vine can be carried out with various structures that divide the plant wall by means of conduction systems [1], regulating irrigation so that the growth of the branches is controlled [2] or by means of the
    35 manual or mechanized leaf removal [3,4]. The ideal foliar configuration is that which has between 1 and 1.5 leaf layers and a percentage of holes located between 20% and 40% [5], which guarantees the adequate capture of solar radiation while reducing shadows.
    4 O The presence of gaps in the vegetable wall of the vineyard is important to favor aeration of the fruit, since poor aeration favors fungal infections [6, 7]. In red varieties, exposure to solar radiation induces the synthesis of anthocyanins [4,8], key compounds in high quality wine. However, excessive exposure of the clusters can cause burns and quality reduction
    45 in the color of the grape [9, 10]. Optimizing the capture of solar radiation by the leaves and at the same time the exposure of the clusters is one of the challenges of viticulture worldwide; The diversity of climates with different rainfall and temperature regimes requires different strategies to maximize quality.
    5 The most commonly used method to determine the porosity of the leaf surface is the "Point Quadrat Analysis" (PQ A) [1]. This technique is based on the use of a test stick that is inserted at regular intervals in the wall or vegetative canopy of the vineyard. Counting the number of times and parts of the vine with which the probe comes into contact (leaves, clusters, branches or hollows) you get the proportion of the different
    10 items The porosity of the vineyard can be quantified as the division of the number of holes divided by the total of the witness insertions. A minimum of 50 passes is recommended to properly identify porosity [1]. The PQA requires a large amount of labor and time to be carried out in a limited number of strains, so its use in the wine industry is reduced. The realization of the test
    15 complete with 50 insertions requires ten to fifteen minutes to complete, so that the number of strains evaluated per unit of time is limited. To achieve greater implantation among the wine growers and the industry it is necessary to find a method that allows the estimation of the porosity of the vegetative wall in a faster way.
    20 Although due to the conditions described above, it is not widely used, PQA is the standard in viticulture worldwide. A faster and simpler method to evaluate the porosity of the vegetative wall of the strain would allow its widespread use and with it the improvement in the management of the vegetative surface resulting in a greater
    25 fruit quality.
    There are no methods available on the evaluation of the porosity of the vineyard using image analysis.
    3 O The technical advantage of the present invention is that of a non-invasive procedure, which allows the porosity of a strain to be determined automatically by artificial vision.
    Bibliographic references 35
    [1] Smart RE, Influence of light on composition and quality of grapes. Hort Act. 206: 37-47 (1987).
    [2] Intrigliolo DS and Castel JR, Response of grapevine cv. 'Tempranillo'to timing and
    4 O amount ofirrigation: water relations, vine growth, yield and berry and wine composition. Irrig Sci 28: 113-125 (2010).
    [3] Tardaguila J, of Toda FM, Pony S and Diago MP, Impact of early leaf removal on
    yield and fruit and wine composition of Vitis vinifera L. Graciano and Carignan. Am J 45 Enol Vitic 61: 372-381 (2010).
    [4] Tardaguila J, Blanco J, Pony S and Diago M, Mechanical yield regulation in winegrapes: comparison ofearly defoliation and crop thinning. Aust J Grape Wine Res 18: 344-352 (2012).
    [5] Smart R and Robinson M, Sunlight Into Wine; A Handbook for Winegrape Canopy Management. Winetitles, Adelaide, pp. 88 (1991).
    [6] English J, Thomas C, Marois J and Gubler W, Microc1imates of grapevine canopies associated with leaf removal and control of Botrytis bunch rot. Phytopathology 79: 395401 (1989).
    [7] Diago MP, Vilanova M and Tardaguila J, Effects oftiming ofmanual and mechanical early defoliation on the aroma of Vitis vinífera L. Tempranillo wine. Am J Enol Vitic 61: 382-391 (2010).
    [8] Diago MP, Ayestarán B, Guadalupe Z, Garrido Á and Tardaguila J, Phenolic composition of Tempranillo wines following early defoliation of the vines. J Sci Food Agric 92: 925-934 (2012).
    [9] K1iewer WM, Influence of temperature, solar radiation and nitro gen on coloration and composition of Emperor grapes. Am J Enol Vitic 28: 96-103 (1977).
    [10] Mori K, Goto-Yamamoto N, Kitayama M and Hashizume K, Loss ofanthocyanins in red-wine grape under high temperature. J Exp Bot 58: 1935-1945 (2007).
    [11] McLachlan G, Mahalanobis distance. Resonance 4: 20-26 (1999).
    Brief description of the figures
    Glossary of references
    (ID Procedure for obtaining porosity manually.
    (!) Procedure for the automatic estimation of the porosity of the vineyard
    by artificial vision.
    (10) Strain
    (eleven) Digital camera.
    (12) Background.
    (13) Original RGB image
    (14) Image with the selection of the productive region of the strain.
    (fifteen) Image with the selection of the segmented strain in its constitutive classes
    (leaves, clusters, wood and holes).
    (16) Binary image classified.
    Figure 1 (Fig.l) .- shows a schematic configuration of the location of a digital camera (11) to capture an original RGB image (13) of a strain (10), under field conditions.
    Figure 2 (Fig. 2) .- shows a block diagram with the movement flows according to the state of the art (O) and according to the present invention (1). The current state of the art is reflected by a dashed line, while the flow according to the present invention is shown by a solid line.
    Figure 3 (Fig. 3) .- shows a set of images (14, 15, 16) obtained by applying the procedure recommended by the invention (1) to an original RGB image (13).
    An original RGB image (13) can be seen in Fig.3A.In Fig.3B you can see an image with the selection of the productive region of the
    15 strain (14). In Fig.3C an image can be seen with the selection of the segmented strain (15) in its constituent classes (leaves, clusters, branches or hollows). A classified binary image (16) can be seen in Fig.3D.
    2 O Figure 4 (Fig. 4) .- shows a graph, which is presented by way of example, where different values of "% gaps measured by strain" can be seen against their corresponding values of "% gaps estimated by strain", as well as the linear correlation between these values.
    2 5 Detailed description of the invention and presentation of a preferred embodiment of the invention
    The present invention allows to determine the porosity of the vegetative wall or canopy of the vineyard. That is, it allows the percentage of holes in the leaf area of a 3 O strain (10) to be assessed quantitatively by means of a rapid, precise and objective solution. Strain
    (10) is of a vine of both red and white varieties. The method is based on the image analysis of a vine photograph taken in the field
    The method for the automatic estimation of the porosity of the vineyard by artificial vision (1) according to the present invention comprises the following steps or steps:
    Stage "a". Capture an original RGB image (13) of a strain (10) with a digital camera (11) in the field. 40 An original RGB image (13) is captured for each strain (10).
    A preferred configuration of the digital camera (11) is as follows: place the digital camera (11) at the same height as the strain (10) in order to obtain an original RGB 45 image (13) of elevation or profile of the strain (10). In order to avoid interference from the rows behind the photographed, and that would be visible through the gaps of the same, a background (12) of uniform color that contrasts with the strain (10) is used. This background will be located behind the strain to be photographed, allowing the holes to be identified by their different color. Alternatively, interference from the rear row 5 can also be avoided by taking pictures at night, with the use of a calibrated light to illuminate only the row in front and not the rear ones. Alternatively, photographic optics configured in such a way that they focus on the target row will be used, blurring the rear rows, allowing subsequent filtering. A model and preferred focal length of the digital camera (11) is the
    Next 10: a Nikon 5300D camera with Nikon 16-85 lens (Nikon Corp., Japan) with the appropriate focal length to focus the strain.
    A preferred configuration of the digital camera capture parameters (11) for a typical brightness is: shutter speed 200 ms, ISO 800 sensitivity,
    15 manual focus and white balance 'Sunlight', and a resolution of the original images 24 mpix.
    Stage "b". Select in the original RGB image (13) the region of the image that corresponds to the production area of the strain (10) to obtain an image with the selection of the productive region of the strain (14).
    The image usually contains more area than the one under study, so it must be selected or errors could be induced in the analysis. The image area is selected by an operator marking the vertices of the area to be analyzed.
    25 manual.
    Stage "c". Segment the image with the productive region of the strain (14) by selecting seeds to obtain an image with the selection of the segmented strain (15) with the pixels that make up the strain (10) and with which
    3 O correspond to the fund (12).
    The process of identifying the holes in the leaf area of the strain is carried out based on the color analysis of the different elements present in the image. To differentiate the elements present in the image from their color, segmentation has been used
    35 by distance from Mahalanobis [11], which allows you to group the pixels of the image in volume to a value, selected as the definition of that set. The value used to generate the groups is called seed and is obtained by direct selection on the image. This selection is only necessary once for the total images to be analyzed, captured under similar lighting conditions. Stage "d". Analyze the image with the selection of the segmented strain (15) to identify the gaps present in it to obtain a classified binary image (16).
    5 Using the seeds selected in step "b", the classification of the pixels present in the region to be analyzed is selected selected in step "c". The values of the ROB color components are used to classify each pixel based on their proximity to those of the selected seeds. Several groups are used: wood (trunk / branch), leaves, clusters (if any) and holes / bottom. Stage "el !. Calculate the percentage of holes in the strain (10).
    The percentage of gaps is calculated by the quotient between the number of pixels labeled as background, divided by the number of pixels corresponding to the
    15 region of interest.
    The linear correlation between the percentage of gaps obtained by the manual field invasive method is determined, and the percentage of gaps estimated in the image, obtaining y = Ax + B, and its R2; where A and B are the coefficients of the regression line
    2 or linear. By way of a non-limiting example of the invention, in Fig. 4, a regression graph is shown with y = O, 979'x-l, 115, R2 = O, 932. Technical advantage provided by the invention
    25 A new method, based on image analysis, has been developed to estimate the percentage of holes in the leaf surface of a strain. This new method has been analyzed in several wine and country trials, demonstrating that it is a robust and precise method. The relatively low cost of the method, its accuracy, its reliability and the relative speed compared to the current manual measurement methods
    3 Or make it a great alternative to traditional methods. That is, this method can be a new tool to estimate the percentage of vineyard voids quickly, reliably and accurately.
    The technical advantage of the present invention is that of a non-invasive procedure, which allows the porosity of a strain to be determined or estimated automatically by artificial vision.
    权利要求:
    Claims (1)
    [1]
    one. Processfor theestimateautomatic ofthe porosity ofvineyard
    by artificial vision (1), characterized in that it comprises the stages
    5 following:
    a) Capture an original RGB image (13) of a strain (10) with a camera
    digital (11) in the field;
    10 b) Select in the original RGB image (13) the region of the image that
    correspond to the production area of the strain (10) to obtain an image
    with the selection of the productive region of the strain (14);
    c) Segment the image with the productive region of the strain (14) by
    fifteen seed selection to obtain an image with strain selection
    segmented (15) with the pixels that make up the strain (10) and with which
    correspond to the fund (12);
    d) Analyze the image withthe selection of the segmented strain (15) for
    2 o identify the gaps present in it to obtain a binary image
    classified (16);
    e) Calculate the percentage of holes in the strain (10).
    25 2.Method according to claim 1, characterized in that the strain (10) is
    of a vine of both red and white varieties.
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