Blog

Suivez les dernières actualités postées ici !

Yield maps in Precision Agriculture

Table des matières Yield monitors: one of the pioneer sources of PAAcquisition  of  within-field  yield  data:  combine  harvesters  and  yield monitorsCharacteristics of within-field dataProvision and usagesAdvantages and limits of within-field yield dataHow to valorize yield maps?One last criticism for manufacturers. This post should have been published a long time ago, especially since it was the Read more about Yield maps in Precision Agriculture[…]

Precision Agriculture in all intimacy

Table des matières Challenges of the agricultural sectorWhat is Precision Agriculture?How can Precision Agriculture play a role in the agro-ecological transition?The adoption of Precision Agriculture is a bit slow….Precision Agriculture: Between approval and contradictionData quality, still a major loser…To go further…Micro plot experiments VS On-farm experimentsNew skills to work in Precision Agriculture?Again and again the Read more about Precision Agriculture in all intimacy[…]

Neural network – Let’s try to demystify all this a little bit (3) – Application to images

Table des matières Convolutional Neural NetworksImage DatabasesPackages and libraries to useKaggle Contest 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.), Read more about Neural network – Let’s try to demystify all this a little bit (3) – Application to images[…]

Neural network – Let’s try to demystify all this a little bit (2) – To go a little further

Table des matières Initializing weights and biasesThe cross-entropy functionRegularization and Drop OutThe bias-variance dilemmaA little feedback on the hyper-parameters, with some new ones in the listSupervised, unsupervised and reinforcement learning 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 Read more about Neural network – Let’s try to demystify all this a little bit (2) – To go a little further[…]

Neural network – Let’s try to demystify all this a little bit (1) – Neural architecture

Table des matières Neural networks, Deep learning and co.Architecture of neural networksForward propagation : How does the yield is estimated ?Backpropagation : How does the model learn?Extension to the multi-layers neurons Unless you have emerged from a period of cryogenics or have been locked in a bunker for several years, it is unlikely that you Read more about Neural network – Let’s try to demystify all this a little bit (1) – Neural architecture[…]

Fuzzy logic or the extension of classical logic

Table des matières Fuzzy inference systemsAn example in the agricultural worldFuzzificationDecision-making unitDefuzzificationAs a discussion and conclusion 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 Read more about Fuzzy logic or the extension of classical logic[…]

Data Visualisation with R and Shiny

Table des matières Data visualization with RThe coupling of R and Shiny 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 Read more about Data Visualisation with R and Shiny[…]

Outliers, abnormal data, Let’s take a look at the situation

Table des matières Outliers, introduce yourself !A brief overview of outlier detection methodsHow to judge the quality of a detection method?What to do once outliers are detected?Disadvantages and limitations of current methods: general application and automation Few will tell you that their data is all pretty and clean and can be used as is in Read more about Outliers, abnormal data, Let’s take a look at the situation[…]

Link R and QGIS: Integrate your own R algorithms in QGIS

Table des matières Parameter setting of QGIS and RThe treatment to be carried outSome rules of good conductImplementation of the R code in QGISLaunching the script Parameter setting of QGIS and R The presentation of QGIS is no longer necessary! This open-source platform is now widely used in many domains to visualize, exploit and process Read more about Link R and QGIS: Integrate your own R algorithms in QGIS[…]

Uncertainty and Sensitivity

Table des matières Different sources of uncertaintyUncertainty analysisA focus on sensitivity analysis Precision agriculture tools (pedestrian, static, tractor-mounted or airborne sensors, etc.) make it possible to acquire agronomic and environmental data sets at impressive spatial, temporal and attribute resolutions. Generally speaking, we tend to trust these captured data (sometimes too much), i.e., we often use Read more about Uncertainty and Sensitivity[…]