The accurate calculation of statistics is a cornerstone of precision agriculture data analysis. GeoPard has added more detailed statistical calculations to the zones created on the platform to ensure that your maps and the analytic conclusions you draw from them are precise and reliable.
Advanced statistics are calculated per zone, containing the attributes: minimum and maximum values of the vegetation index (or other attributes), median, average, standard deviation, and the sum of all the values in the zone. The median is the middle value of a dataset that has been numerically ordered, as opposed to the average. This relates to the standard deviation, which reflects how the data is arranged around the average value. A low standard deviation suggests that the data in a given zone is grouped closely around the average, whereas a high standard deviation indicates that the zone data is spread out more widely around the average. The sum attribute is simply the total sum of all pixel values in that zone. Before any stats are calculated for your zones, all the outliers or anomalous data points are removed to prevent the creation of misleading statistics that do not accurately reflect your zone data.
After the manual amendments of zones through the Merge/Split tool, zone statistics are recalculated based on the new zone geometries. This allows for a refined and accurate understanding of data distribution inside and across zones.
As always, GeoPard values transparency throughout all aspects of the platform. During the aggregation of classified data into zones, all details are smoothed and hidden without metrics to show what happened, so the results of the data aggregation are provided through statistics. It is also always possible to backpedal and extract the original values from your zones to recheck them or to utilize them in your own models. You never need to worry about losing your original data in GeoPard. Statistics are of high value in determining map accuracy and are calculated for zones based on any data layer of your choice, including yield, ground sensors, satellite, topography, and multi-layer.
GeoPard presents zone statistics in a highly readable and straightforward manner, which can be seen in the example images below. At GeoPard we want to make it easy for you to be confident in the decisions you are making about your fields by providing you with the best and most comprehensive access to statistical calculations we can.
GeoPard is capable of processing and analyzing various types of ag spatial data. This is an example of working with high-density sensor datasets with a great spatial variability provided by Geoprospectors GmbH.
a depth to interface with information about compaction
electrical conductivity on 4 cumulative depth
Geopard lets you see points with raw values and continuous surface; compare different data layers; delineate soil zones for zonal soil sampling and VRA; combine TopsoilMapper data with data available in GeoPard such as historical, current vegetation, and elevation into one Zones Map.
Curious to know what low EC values represent on the map as a curve? This is an ancient riverbed, buried underground.
To visualize field data and make informed decisions it is often necessary to compare layers on multiple synchronized views.
In GeoPard, you can visually compare up to four layers of data on one screen. All layers work synchronously when you zoom in/out or move the map for your convenience.
How do I enter split-screen mode? Select a field and click the layer comparison icon in the upper right corner of the screen. Then select any snapshots, field control areas, or other data layers that you want to see on the same screen at the same time. Click Compare Layers.
The layer comparison feature synchronizes maps, cursors, zoom levels. Also you have the ability to add/remove data layers. Currently we support up to 4 data layers.
In precision farming, field data collection and data-driven decision making are absolutely essential. As the next stage in the development of multi-layers analytics and finding dependencies across layers we introduce new module Zones Operations.
There you can search for dependencies between different zones maps such as historical vegetation, topography including its derivatives, data from yield monitors, soil data, scanners, stability maps, and so on. This is a step forward in defining the most influenced areas and understanding the reasons for field heterogeneity.
How can you identify the areas? First of all select field maps, you want to cross-investigate. A layer comparison view is a good approach to define specific zones for analyzing. You may want to compare low yield potential and sloppy areas, most unstable zones and low vegetation, low electrical conductivity and yield, as applied fertilization map and current vegetation, and others. Secondly, mark specific zones on every map you want to compare in the Zones Operations module. And finally, obtain a zone of interest. Note that it is possible to use more than two maps in analyses.
How can you apply this knowledge? In addition to finding relationships that can help explain yield, it is possible to set yield goals for defined zones; to scout interesting areas; to reduce investments in such localized zones or build the plan of mitigating limiting factors and pull up underperforming areas knowing the underlying causes; to build agronomy plan using VRA practices.
There are several examples of field insights on screenshots. Note that every field is unique and below-mentioned cases do not guarantee 100% same result for your field but it is a good way to start the investigation from.
You are very welcome to share your agronomic practices by commenting on this post, contact the GeoPard Agriculture team directly. We are open to the feedback because we build the solution for you for a better understanding of field variability and managing it.
Almost all management zones are adjusted before becoming a Variable Rate Application map. This can be merging some zones together, manual corrections in well-known spots, the addition of extra buffer areas, ag equipment compatibility, etc.
We, in the GeoPard team, understand that accurate natural management zones with valid polygons will save a lot of time during zone verification and correction processes.
The GeoPard engine does the following:
automatically removes noise,
automatically merges small polygons into the closest bigger zone,
keeps only the necessary minimum amount of points in every zone polygon,
makes VRA maps compatible with any agricultural equipment and machinery.
Nobody knows his field better than a farmer or agronomist who works with the field for many years. That’s why any algorithm-based analytics often needs to be validated and adjusted by a professional using his deep knowledge of the field.
Merge and split zones feature allows a professional to make a few important things:
Assign a polygon or a complete zone to another class
These adjustments can be applied for any data layer and it is a very useful feature to prepare YOUR perfect zones for precision ag operations like VR seeding, fertilizing or spraying.
It’s no secret that we constantly enrich the GeoPard Agriculture solution and increase its value to users. Just look at the “coming soon” section of our website https://geopard.tech to get an idea of some features on the way.
Their prioritization can be challenging. Here, feedback and product demos come to the rescue. Thus, presenting our solution to many attendants of the World Agri-Tech Summit in London, we were able to adjust the delivery plan and release a new layers comparison feature just within a few days.
What is it about? You can visually compare field analytics side by side in a split view. It is possible to select any type of layers for comparison: imagery with natural or infrared colors, imagery with vegetation views, in-season or historical management zones. Two layers behave synchronously when you zoom-in / zoom-out or move a map for your conveniences.
How to enter the split view mode? Select your field and click compare layers icon in the top menu. On the split view screen, select the analytics layer using a searchable drop-down list located on the top.
Historical (multi-year) management zones provide insights about every spot in the field.
What does it mean? Historical (multi-year) management zones are built based on 30+ years archive of satellite imagery. Images with peak vegetation during every season are automatically selected as inputs for analytics. Otherwise, every such image represents a potential yield file for the related year.
How can you use it? The field crop development pattern helps to know the agricultural area better and to apply the right decision with the right input rates in the right spots. Historical management zones could be used as a blueprint for prescription (Rx) files for seeding, fertilization, zones based soil sampling.
We support all regions by request.
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