Abstract:
A method of processing vegetation data including the steps of identifying data relating to an agricultural field, segregating areas of predetermined development patterns and prescribing application rates of an agricultural compound. The identifying step includes identifying data relating to an agricultural field representative of areas of predetermined development patterns of vegetation in the field. The segregating step includes segregating the areas of the predetermined development patterns thereby defining segregated areas, other areas in the field being non-segregated areas. The prescribing step including prescribing application rates of an agricultural compound to the non-segregated areas dependent on at least one attribute determined from the data.

Description:
FIELD OF THE INVENTION 
       [0001]    The present invention relates to a method for analyzing image information of a field, and, more particularly to a method of analyzing image pixel information of soil or crop parameters in a field. 
       BACKGROUND OF THE INVENTION 
       [0002]    Current approaches to characterize agricultural field variability involve two basic processes. First is the creation of layers that describe variability of soil or crop parameters and then secondly to divide this characterized layer into classes. For example, aerial images can be used characterize parameters describing crop or soil variability. This is in addition to in-field sensors, crop color sensors, soil EM readings or other methods. Current methods for characterizing aerial images involve converting the original multi-spectral data to vegetation indices such as Normalized Difference Vegetation Index (NDVI). This NDVI layer is then divided into classes using an approach such as an equal area or equal increment approach. When divided using equal area, all fields have an equivalent distribution of high and low zones, independent of the level of actual variability, sometimes resulting in prescriptions that may be poorly related to actual crop conditions. When divided on the basis of equal increments (e.g. 31-40, 41-50 . . . ), some classes have little or no representation in some fields. In both cases, it is difficult to compare fields based upon the resultant classification. This problem is exacerbated when images, or other data, are acquired by sensors that may provide different outputs for the same crop situation depending upon conditions such as sensor settings, light level, angles of the images relative to the crops, direction of the planted rows, or differences in crop architecture, such as upright or droopy leaves. 
         [0003]    It is known that the normalization of an image layer or sensor values yields a somewhat more consistent relationship to parameters of interest, such as yield and Soil and Plant Analysis Development (SPAD) chlorophyll. A normalized SPAD, based upon the mean pixel value from a high nitrogen reference strip, is a better indicator of nitrogen stress, or yield, than SPAD values alone. The use of relative SPAD and relative yield, based upon the highest or highest mean yield of the field, allows a more consistent relationship when more than one field is used in the analysis. Similar results have been shown for images or sensor results in addition to SPAD values. One of the problems in the prior art is the use of high nitrogen reference strips in that SPAD/pixel/sensor readings often show considerable variability within and between high nitrogen reference strips within the whole field. This makes it difficult to select a value to be used for normalization. A further problem with this approach is that growers are not eager to create high nitrogen reference strips. 
         [0004]    What is needed in the art is the ability to select a target or best representative pixel value without the need to establish a high nitrogen reference strip. 
       SUMMARY OF THE INVENTION 
       [0005]    The present invention provides a method for normalizing images or other data layers and for identification of low vegetation zones and weedy patches. 
         [0006]    The invention in one form is directed to a method of processing vegetation data including the steps of identifying data relating to an agricultural field, segregating areas of predetermined development patterns and prescribing application rates of an agricultural compound. The identifying step includes identifying data relating to an agricultural field representative of areas of predetermined development patterns of vegetation in the field. The segregating step includes segregating the areas of the predetermined development patterns thereby defining segregated areas, other areas in the field being non-segregated areas. The prescribing step including prescribing application rates of an agricultural compound to the non-segregated areas dependent on at least one attribute determined from the data. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0007]      FIG. 1  schematically represents pixel data of an agricultural field that is used to illustrate the methods of the present invention; 
           [0008]      FIG. 2  is a block diagram of one embodiment of a method of processing vegetation data of the present invention; and 
           [0009]      FIG. 3  is another block diagram of another embodiment of a method of processing vegetation pixel data of the present invention. 
       
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       [0010]    Referring now to the drawings, and, more particularly to  FIGS. 1 and 2 , there is shown a method  10  that includes an approach to normalizing images or other layers based upon the best portion of a field  50  and to identify areas of field  50  with limited vegetation or excessive vegetation such as weedy patches. A tool can be used to optimize the prescriptions or other operational decisions that result in application rates of an agricultural compound that is then applied to the selected field. At step  12 , areas are identified in the field having unusual development patterns, which might require special treatments thereto. 
         [0011]    Often decisions are based upon the assumption that the level of input from an image can be directly related to the extent of crop canopy/canopy color. The term canopy is also used herein to indicate the extent of vegetation. These attributes may indicate more nitrogen is present and that reduced canopy areas may require additional nitrogen. This relationship often results in the incorrect application of compounds, such as nitrogen to areas of the field that have little canopy or that have excessive canopy. Excessive canopy can occur when the crop is double planted or there is simply a weedy patch. To eliminate this incorrect application a graphical user interface is utilized where the layer describing the vegetation status, such as NDVI is overlaid with a color infrared (CIR) layer. This provides for a more visually interpretable indication of canopy differences. The tool allows a user to progressively remove pixels from lower vegetation areas, such as an area  60 , causing area  60  to be segregated, at step  14 . Pixels can also be segregated or removed from higher vegetation areas that correspond to weedy areas  58  or double planted areas  56 . Segregation step  14  allows for the designation of these extreme type areas to be identified and segregated for special consideration. This allows for a particular application rate to be selectively designated at step  16  for the extreme pixel areas that are segregated. 
         [0012]    The remaining areas  52  and  54  of the field, the non-segregated portions, receive prescriptions that are directly related to the extent of crop canopy/color, such as NDVI. The progressive removal of pixels at step  14  is monitored by the user using the overlying CIR layer, an RGB layer or other layer that may satisfy the objective, while incrementally selecting higher and/or lower pixels based upon the values of the underlying layer, such as NDVI. The CIR layer not only provides the more easily interpretable layer, but also provides a spatial context to select pixels meeting certain criteria. For example, high density pixels with consistent patterns near field borders may be attributed to double planted areas  56 , while weedy patches  58  often have a geometrical pattern or spatial distribution that is easily distinguished. Neither of these could be confidently selected based upon an NDVI value alone. The process can be used independently of a normalization process or can be used prior to, or following, the normalization process. 
         [0013]    After certain areas have been segregated in step  16  the pixels in the non-segregated areas  52  and  54  are reviewed and are then related to a selected best area in the field, at step  18 . This is a normalization process that removes variability due to differences in the camera, sensor, instrument calibration, light availability and other factors that can cause variability in the pixel data relating to the crop. It also removes variability due to crop architecture, such as droopy or upright leaves, or the stage of growth of the crop. For example, in the case of nitrogen stress there is an assumption that some portion of the field has sufficient nitrogen, and the remainder of the field is related to this best area, also known as the best pixel value or best pixel data. This best pixel value is considered to be a 100% value, which may be below the threshold value that defines a segregated area containing excessive vegetation. Also, the best pixel value can be based upon a statistical derivative of the non-segregated area, such as a 90 th  percentile value. Following the normalization process at step  18  it then becomes possible to compare fields with each other based upon the magnitude and extent of stressed areas within each field. 
         [0014]    Fields can then be prioritized based upon the distribution of stressed areas. The application of agricultural compounds can then be applied to the fields based upon the prioritization assigned to each field or areas of the fields. Field ranking is utilized to provide for optimal use and timing of needed applications of agricultural compounds. 
         [0015]    The foregoing includes a user selection of all pixels that are not representative of the crop, such as double planted areas  56 , weedy areas  58 , etc., that gives rise to a non-segregated normalized layer. The pixels that are in the excessive vegetation segregated area typically have values greater than the best value pixel in the non-segregated normal areas. The highest pixel value in the non-segregated area is identified at step  18  and that pixel value is then used as the denominator for application to all pixels in the non-segregated area, and the conversion to a percentage by multiplying by 100. This results in a layer where all representative pixels are related to the best pixel value selected from the non-segregated areas of the field. This is expressed by the equation: Normalized Value=Current Pixel Value/Best Pixel Value×100. Based upon the normalized values assigned to each pixel, application rates are prescribed for the non-segregated areas based on the relative pixel values determined at step  20 . The agricultural input or compound, such as a nitrogen fertilizer, is then selectively applied at step  22  based upon the assigned application rate that is dependent upon the normalized pixel values described above. The application device is geo-located and applies the prescribed application rates to areas in the field that correspond to the pixel locations. The application rates, which are assigned at step  16  to the segregated areas, may include the non-application of nitrogen or other inputs to such areas as drowned out areas or areas having excessive canopy. Additionally, a selective weed control compound may be utilized in certain segregated areas that are identified as weedy patches. 
         [0016]    Now, additionally referring to  FIG. 3 , process  100  illustrates a masking of selected areas at step  102  in which a portion of the pixels may be selected to mask out areas with different crop varieties to thereby allow the relative characterization of one variety of crop at a time. In addition, masking step  102  can be carried out on all non-crop pixels, such as grass covered areas within vineyards or orchard areas, before the normalization step. Masking also allows comparisons to other fields to become more effective, particularly if things such as grass waterways  60  are masked out prior to the normalization process. At step  104 , application rates can be designated for the masked areas, such as not allowing any application of the agricultural compound in the masked off areas. This masking step can often be accomplished directly using the same selection process as described in  FIG. 2 , but can also be completed using alternative methods before this process. 
         [0017]    The normalization process is described at step  106  in which the non-masked areas are related to a selected area having the best pixel value as described in method  10 . At step  108  application rates are prescribed to the non-masked areas at step  108 . The agricultural compound is selectively applied at step  110  to the field area. The combination of method  100  and method  10  can be utilized to mask selected areas prior to the segregation described in method  10 . 
         [0018]    The normalization process of the present invention allows for the contrasting of fields with one another thereby allowing the identification of fields with varying degrees of stress. For example, a field A can have twenty percent of the area of field A being below 85% of the best pixel value, while field B may have only two percent of the field area below 85% of the best pixel value, thereby indicating that field A has a higher level of stress. This analysis then allows a decision maker to identify fields with problems, the extent of the observed problems and to make informed decisions about which fields warrant treatment or warrant priority treatment before other fields. This also allows for the utilization of risk assessment to identify fields that aren&#39;t developing according to predefined criteria. 
         [0019]    It should be understood that the description utilizing the CIR and NDVI layers is only one example, and other image or vegetation index layers can be used instead of, or in addition to, the CIR and NDVI layers for particular applications. For example, other indices that are more closely related to leaf area index (LAI) or canopy differences can be used instead of NDVI. It is also possible to use different attributes such as the selection of the best chlorophyll pixel and the best canopy density pixel for the same field and to use the combination of these two normalization layers that result, to enable differentiation of areas impacted by such things, as moisture stress, from those areas impacted by nitrogen stress. 
         [0020]    Having described the preferred embodiment, it will become apparent that various modifications can be made without departing from the scope of the invention as defined in the accompanying claims.