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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[…]
Ah, Precision Agriculture! We hear about it all the time right now. Agriculture 4.0 (I don’t even know what number we’re at anymore), super precise machines, connected sensors….. Digital technologies and innovations create a buzz in agriculture (Note: Sprinkle it all with a little bit of Big Data, Deep Learning or Artificial Intelligence to shine Read more about Precision Agriculture in all intimacy[…]
The complex architecture that we have detailed in detail in the previous sections is a multi-layer perceptron (MLP). This is the classic architecture of the neural network. Nevertheless, depending on the type of data used to input neural models (images, voice signal, etc.), more specific architectures have been implemented. To work with images, for example, Read more about Neural network – Let’s try to demystify all this a little bit (3) – Application to images[…]
With everything that has been presented in Part 1, I hope that you will have understood how a neural network works in general, with the two main steps of forward and back propagation. And that’s not bad enough, it’s a lot of concepts to mature! In this part, without going into too much detail either, Read more about Neural network – Let’s try to demystify all this a little bit (2) – To go a little further[…]
Unless you have emerged from a period of cryogenics or have been locked in a bunker for several years, it is unlikely that you have never heard of a neural network. Having heard about it is one thing. Understanding what can be used is another. Knowing how it works is a whole different matter. If Read more about Neural network – Let’s try to demystify all this a little bit (1) – Neural architecture[…]
The concept of fuzzy logic was proposed in the 1960s by Lotfi Zadeh, an Iranian mathematician and computer scientist, to tackle the limits of good old classical logic. Which limits are we talking about? Let’s take a first very simple example on the temperature of the water that flows when you take a shower. If Read more about Fuzzy logic or the extension of classical logic[…]
Visualizing your data is THE most important part during your project management (we often talk about “Data Visualization” or Dataviz”). Whether upstream to understand a little bit about how the data are arranged or downstream so that a decision-maker can make an informed decision, there is always a time when we will have to think Read more about Data Visualisation with R and Shiny[…]
Few will tell you that their data is all pretty and clean and can be used as is in decision models… That’s a fact. When a dataset is collected, no one is immune to the risk of biased or outliers coming up and disrupting the quality of the data. And there are plenty of sources Read more about Outliers, abnormal data, Let’s take a look at the situation[…]