Generally speaking, when we want to evaluate the robustness and/or generality of an algorithm, we need to test it on a large number of data, with quite varied characteristics, to ensure that the algorithm will give conclusive results in the vast majority of cases. If we had the means to have real data or field Read more about How to generate spatially correlated data?[…]
Precision Agriculture is a data-based discipline; data that is collected to measure, describe, quantify, understand, or analyze agrosystems. A wide variety of measurement systems have been developed to measure agronomic parameters of interest, from plant vegetation status to crop yield, including weed detection and soil physico-chemical parameters. These increasingly sophisticated systems make it possible to Read more about Working with high-resolution data in precision agriculture[…]
In spite of the emulation around precision agriculture and digital agriculture in France, it is clear that these tools and solutions are not yet widespread in the field. Some recent statistics from the French observatory of digital uses in agriculture can testify to this: less than 10% of farmers used variable-rate tools in 2018, as few Read more about Producing profitability maps from yield maps?[…]
In the previous post, a fairly general picture of yield sensors and associated data was drawn. Here we return to a recurring question related to yield data: How can we ensure that yield data are reliable enough to be used properly? We will therefore review the main sources of uncertainty in these data sets; and Read more about Filtering – Cleaning Yield Maps[…]
This post should have been published a long time ago, especially since it was the subject of my thesis. It’s now done… First of all, one might ask why a thesis work was proposed on yield data when yield sensors have been around since the 1990s? First, it is clear that yield information is in Read more about Yield maps in Precision Agriculture[…]