In the realm of modern agriculture, precision is paramount for maximising crop productivity. Farmers and agronomists are continually seeking innovative methods to optimise resource allocation and make data-driven decisions. One groundbreaking tool that has revolutionised field management is satellite imagery.
Satellite imagery provides a valuable overview of agricultural landscapes, unlocking insights into field variability. By leveraging satellite images, farmers can gain valuable information about soil quality, plant health, and overall field performance. In this article, we will explore how you can use satellite imagery to measure field variability for fertiliser and seed input, and create your own variable rate applications and prescription maps.
This article will provide you with a high level overview of the process to build the solution. To complement it, you can find a detailed Jupyter notebook ready to be run.
We randomly selected a field in the UK, which is growing some crops between March and July 2022. The images clearly show the growth and maturation of the crop, followed by the harvest.
In our comparison, we want to look at several vegetation indices commonly used in Agriculture: NDVI (Normalized Difference Vegetation Index), LAI (Leaf Area Index) and NDRE (Normalized Difference Red Edge). If we take a date early in the season, we can compare the value of these indices for the same field.
We can now start using the SpaceSense clustering function, to identify zones. This function enables you to cluster any satellite image with several parameters:
We are going to run it for several experiments:
1. A single day, with a single index
We will use the 21st of April for our single date, 6 zones, and NDVI for our index.
As shown in the result below, the vertical bands observed within the NDVI image are still clearly visible in the clustering result, and seem to cluster the zones efficiently.
2. A single day, with several indices
We keep the 21st of April for our single date, 6 zones, but we add LAI and NDRE to the NDVI.
As shown in the result below, we still see the vertical bands from the NDVI, but we also see an area that appeared in the bottom, which is visible in the LAI band. This clearly indicates that both indices were used in the result. The NDRE being pretty neutral, had little impact on the resulting cluster.
3. Several days, with several indices
We took all the images from the season, 6 zones, and all the indices (LAI, NDRE and NDVI).
As shown in the result below, when taking the whole season, we see significantly less noise than with a single date, and it shows the long term differences in the field, and reveals structural variations within the field
4. A single day, with several indices and a custom file
We will use the 21st of April for our single date, 6 zones, all the indices (LAI, NDRE and NDVI) and a custom file, represented below
As you can see, this is a randomly generated file, just designed for an example. The image looks like static because each pixel value was generated randomly. This is designed to highlight the visual impact of that file on the clustering.
And sure enough, the result is clearly impacted by the added file. If replaced with a relevant file with actual field information, it will provide precious information
And that is it! You now have all the elements to start building your own variable rate applications using your agronomic knowledge, to design something fully adapted for your crop, and your region. Have fun!