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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[…]
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 spatialized data. The processing functions inherent in QGIS in addition to all those of the associated geographic information systems (SAGA, GRASS…) via the geoprocessing module allow Read more about Link R and QGIS: Integrate your own R algorithms in QGIS[…]
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 them as they are, without really asking ourselves too many questions! However, Read more about Uncertainty and Sensitivity[…]
All the data acquisition systems positioned in and around agricultural fields generate a very large amount of information on the functioning of production systems. However, this raw data from the sensors alone is of little interest. This data must be placed in a particular production context and processed with tailor-made algorithms in order to be Read more about GeoFIS : an open source platform to process Precision Agriculture data[…]
The simulation of fields with varying spatial structures is an interesting strategy when it comes to testing or evaluating a specific processing method. The main advantage of simulations is that one is able to control the data distribution within the field so that the context under which the processing method is applied is well-known. For instance, one might Read more about Simulating spatial datasets with known spatial variability[…]
Computing an experimental variogram The usefulness of variograms in Precision Agriculture studies have been largely detailed in a previous post. This is effectively a valuable tool to study the spatial structure of agronomic and environmental spatial datasets. This post will make use of a dataset that was created following the methodology of the post : “Simulating spatial Read more about Implementing variograms in R[…]
One common task in Precision Agriculture studies is to predict the values of a specific variable. More than often, this variable is likely to be costly or time-consuming to acquire and one tries to develop a more or less complex model to infer the values of this variable. For instance, it is well-known that soil Read more about How to validate a predictive model ?[…]
Management zones or management classes ? Site-specific management is a common practice in Precision Agriculture. The objective is to delineate management zones within the fields that will be the place of variable rate applications. Most common strategies of differentiate management include fertilization, irrigation and harvest. To produce variable rate application maps, two approaches are generally adopted. Read more about Delineation of management classes and management zones in Precision Agriculture[…]
When entering the field of geostatistics, one is confronted almost instantaneously to the variogram tool. The definition and existence of the variogram relies on fundamental assumptions that are often presented from a theoretical point of view. These assumptions are almost always left apart because they are relatively difficult to understand. I sincerely admit that the mathematical Read more about Fundamental assumptions of the variogram : Second-order stationarity, intrinsic stationarity…. What is this all about ?[…]
Spatial autocorrelation of agronomic and environmental variables Fig. 1 NDVI spatial pattern within a field In the fields of agronomy and environment, spatial observations generally exhibit some sort of spatial correlation, to a greater or lesser extent. Spatial data effectively share more similar characteristics with neighbouring observations than with other far apart. This concept has been Read more about Spatial autocorrelation and the violation of the assumption of independency between observations in traditional statistical tests[…]