Evaluate Accuracy of Seeding Application
How to evaluate the accuracy of a Seeding Application by comparing the prescribed Target rates with the actual Applied rates on a spatial basis.
Last updated
How to evaluate the accuracy of a Seeding Application by comparing the prescribed Target rates with the actual Applied rates on a spatial basis.
Last updated
In modern agriculture, most agronomic operations are executed based on pre-prepared Variable Rate Application (VRA) maps. These maps typically contain multiple seeding rates tailored to different field zones, addressing the inherent heterogeneity of soil and crop conditions.
By following such VRA maps, farmers aim to optimize seed usage, ensure proper plant density, and maximize yields. In this case, we will focus on evaluating the accuracy of a seeding application by comparing the intended seeding rates (as indicated in the VRA map) to the actual seeding rates recorded by the machinery during the field operation.
After the seeding operation is completed, the machinery’s onboard systems record various parameters related to the task. Of particular interest here are two key metrics:
Target Rate: The intended seeding rate at any given location, as specified in the application map (VRA or Flat Rate Application).
Applied Rate: The actual seeding rate delivered at that same location.
By examining these two attributes in a geospatial context, we can evaluate how closely the actual seeding application followed the prescribed plan. The comparison involves visualizing both Target Rate and Applied Rate maps, analyzing their statistical distributions, and confirming that the required amount of seed was accurately delivered.
To assess the accuracy of the fertilizer application, we will utilize a pre-saved Equation named Spatial Correlation Analysis (Data Layers Similarity) that measures the similarity between Applied Rate and Target Rate values on a spatial basis.
The similarity values range from 0 to 1, where 0 indicates no match and 1 signifies a 100% value-spatial match.
In other words, the closer the similarity score is to 1, the more accurately the seeding application was executed. Achieving values consistently near 1 indicates precise adherence to the application plan (VRA or Flat Rate) and ensures that the field receives the intended seeding rate for optimal crop establishment.
