Abstract:
A method for prescribing a field operation by generating an optimized prescription with a weighted prescription subprocess, executing the field operation prescribed, and then updating the weighted prescription subprocess using a learning subprocess. The weighted prescription subprocess calculates and sums weighted output from two or more site-specific models to generate the optimized prescription. The learning subprocess determines new model weights as a function of relative model error calculated by comparing model output against actual and desired results of the executed field operation.

Description:
FIELD OF THE INVENTION  
       [0001]     The present invention relates to the practice of precision farming, and more specifically, to the generation of optimized field operation prescriptions.  
       BACKGROUND OF THE INVENTION  
       [0002]     Manual ground truthing of remotely sensed information has been a common practice for decades in many enterprises. Use of ground-truthed aerial information to generate site-specific application prescriptions has been practiced for over a decade in agricultural crop production. However, the impact of remotely sensed images has been limited in crop production because of the time and money required to do ground truthing of the image. What is needed in the art is a method of better utilizing remotely sensed images without frequent manual ground truthing.  
       SUMMARY OF THE INVENTION  
       [0003]     This invention improves the usefulness of remote images by learning site-specific rate weighting factors for a given field over time. This invention shows how prescriptions from heterogeneous sources, including aerial images, can be improved over time using a site-specific weighting system that learns based on past performance. Automated crop data collection combined with crop models and learned correction factors may also be used to improve the effectiveness of site-specific crop management and reduce its cost. 
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0004]      FIG. 1  illustrates a method for prescribing a variable-rate field operation employing two or more models and a learning subprocess. 
     
    
     DETAILED DESCRIPTION  
       [0005]     This invention description focuses on variable rate application of the chemical PIX to cotton. One skilled in the art will see how the invention applies to variable rate application of other chemicals (pesticides, fungicides, fertilizers, etc) to other crops, as well as for other field operations such as tillage, seeding, and harvesting. This invention shows how prescriptions from heterogeneous sources, including aerial images, can be improved over time using a site-specific weighting system that learns based on past performance. Automated crop data collection combined with crop models and learned correction factors may also be used to improve the effectiveness of site-specific crop management and reduce its cost.  
         [0006]     The general prescription method  4  for each contemplated field operation/chemical application is as follows: Step 1: obtain aerial images  10  of a crop in a desired field. These may be obtained from just above the crop using a ground based device, aircraft, or satellites, and may be timed by crop and weather prediction models. Step 2: perform standard processing  12  of the aerial images. This includes, but is not limited to, geo-rectification, patching, reflectance correction, color correction, cloud corrections, etc. The company GeoVantage currently provides this service on a commercial basis. Step 3 (optionally): perform ground truthing activity  14  for the aerial images. Step 4: generate an optimized variable rate chemical application prescription  16  based on aerial field images and other data with two or more model subprocesses  18  per a weighted prescription subprocess  20 . Step 5: execute the prescribed variable rate operation over the field  22 . Step 6: update site-specific model weightings  24  based on in-situ crop information, such as height in the case of cotton, per a learning subprocess  26 . Step 7: repeat the prescription method  4  for each field operation by starting at step one  10 .  
         [0007]     Embodiments for the weighted prescription subprocess  20  and the learning subprocess  26  are illustrated below. Machine learning is a diverse and growing field, so other embodiments will be apparent to one skilled in the art. For example, the algorithm described for the weighted prescription subprocess  20  could be replaced with one based on neural networks, particle filters, Kalman filters, etc. The present embodiment uses rasters as a means of representing aggregated site-specific data, but polygons, quadtrees and other representations are also useable.  
         [0008]     The general method for the weighted prescription subprocess  20  is as follows: Step A1: for each model subprocess  18 , execute a given model  28  to generate output  30 , a field sub-area element (raster, polygon, etc), with a recommended application rate or other field operation parameter. Models  28  could include, but are not limited to aerial images, in situ field data, one or more crop models, soil moisture models, and soil productivity indices. Step A2: for each element of model output  30 , calculate a weighted output  32  based on model weights  34  assigned for each model  28 . The sum of the weights  34  for all models  28  used should equal 1.0 or 100%. Thus, for example, an element may give 50% weight to the prescription based on recent aerial images, 25% to a prescription based on a first crop model, 12.5% based on a second crop model, and 12.5% weight to a prescription based on a governmental soil productivity index. The first time this process is used, a weight of 1.0 may be given to a specific source such as a recent aerial image. Alternately, all prescription models  28  could be given equal weighting. Step A3: generate an optimized field operation prescription value  36  for each field sub-area by summing the weighted output  38  from all model subprocesses  18  employed.  
         [0009]     The general method for the learning subprocess  26  is as follows: Step B1: at some time after the field operation  22 , in-situ crop information is collected  40  for actual results  42  on how the prescription method  4  performed. In the case of variable rate PIX application, the post-process data would include plant height and/or height variability changes. For each field node in the prescription grid, the amount was either correct, low, or high by some amount. Step B2: an estimated “correct” amount to get desired results  48  is calculated for each field node element and compared with the output  30  from each model  28  to determine model error  44 . Step B3: from the determination of model error  44 , new weights  34  are calculated  46  for each model  28 . Models  28  having output  30  closer to the correct value have their weights  34  increased, those further away have their weights  34  decreased. In general, the updated formula for the (x,y) element of the weighting matrix of the ith source is:
 
Weight( i,x,y )= k*f  (prescription error ( i,x,y ))+(1 −k )*g(past weight(s) ( i,x,y ))
 
         [0010]     Where k is for weighting current and past performance in coming up with a new weight. The function g may actually consider more than just the most recent weight. In that regard, the new weight can be thought of as a filtered value. An example of re-weighting is provided below and is not necessarily the best scheme: four prescription models with equal weighting of 0.25 provide prescriptions of 3.50, 3.65, 4.00, and 4.25 for a given field raster element. The weighted prescription is
 
(3.50*0.25)+(3.65*0.25)+(4.00*0.25)+(4.25*0.25)=3.85
 
         [0011]     After observing the actual results  42  of the chemical application  22 , it is estimated that the rate should have been 3.95. For example, the applied PIX rate did not inhibit cotton growth as much as desired and a higher rate should have been used. The four prescription sources have errors of magnitude 0.45, 0.30, 0.05, and 0.30 and magnitude percentages of 11.4%, 7.6%, 1.2%, and 7.6% with an average of 6.95%. Function f (prescription error (i,x,y)) will multiply the base weights by the average error / source error and then renormalize (=&gt;indicates “dividing by 2.06=0.15+0.23+1.45+0.23” to renormalize):
 
0.25*(6.95%/11.4%)=0.15=&gt;0.073
 
0.25*(6.95%/7.60%)=0.23=&gt;0.121
 
0.25*(6.95%/1.20%)=1.45=&gt;0.725
 
0.25*(6.95%/7.60%)=0.23=&gt;0.121
 
         [0012]     Next in this example, a value of k=0.3 (and 1−k=0.7) is selected to demonstrate a preference for past weightings over most recent weightings in adjusting the weights  34  to be used next time:
 
= k *f ( )+(1 −k )*g( )
 
0.190=0.3*0.073+0.7*0.25
 
0.205=0.3*0.121+0.7*0.25
 
0.400=0.3*0.725+0.7*0.25
 
0.205=0.3*0.121+0.7*0.25
 
         [0013]     As mentioned earlier, other learning algorithms and reweighing schemes may be used here, including but not limited to neural networks and particle filters. 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.