NO, Precision Agriculture is not just for the rich!
YES, one can enormously improve one's practices with sober and frugal digital technologies!
NO, digital technology is not necessarily evil incarnate!
YES, I too am fed up with solely hearing about Artificial Intelligence, Big Data, and Block Chain.
NO, Precision Agriculture is not necessarily economically profitable (well, not until we put a price on the resources used and give sufficient financial incentives for environmental services rendered).
YES, the digital must serve agronomy and field expertise and must not and cannot replace them.
Are you interested in the research projects I have been working on ?
All of our sciences and fields of work abound with norms, standards, and exchange formats. Agriculture is no exception to this (you will find in the appendix a list of organizations and institutions closely or remotely involved in standardization in agriculture). We need to exchange, collaborate, share data and information, so we need to speak Read more about Standards and data exchange in agriculture[…]
In Precision Agriculture, the delimitation of within-field zones has become a fairly classic step in the processing chains of the services offered on the market (Figure 1). The creation of zones is mainly used to meet operational demands. These zones already simplify the reading of a Precision Agriculture map because it allows one to take Read more about More and more zoning : Classical zoning, Fuzzy Zoning, Constraint Zoning[…]
We propose, in Beta version, a set of R codes (more than forty) and a QGIS plugin to manipulate and process data acquired in the framework of Precision Agriculture. The R codes and the QGIS plugin can be retrieved from the Aspexit GitHub account. Remember to read the tutorials to learn how to retrieve the Read more about R codes to be used in QGIS and a QGIS plugin to work in Precision Agriculture[…]
There is no such thing as a perfectly homogenous agricultural field ! And this is simply due to the fact that we work with living organisms and that we are confronted with phenomena that are all more complex than each other (soil, climate, plants, agricultural practices…), and which also have the unfortunate tendency to interact Read more about Quantifying the within-field heterogeneity or variability in agriculture[…]
Reflecting on the carbon footprint of digital technologies in the AgTech and Precision Agriculture sectors
The International Society for Precision Agriculture (ISPA), proposed a definition of Precision Agriculture in 2019 following exchanges between 45 scientists from around the world: “Precision Agriculture is a management strategy that collects, processes and analyzes spatial, temporal and individual data, and combines them with other information to guide adaptive management decisions related to the plant Read more about Reflecting on the carbon footprint of digital technologies in the AgTech and Precision Agriculture sectors[…]
As you are no doubt aware, geo-positioning has been a particularly powerful lever for many agricultural applications and crop itineraries. But are you clear on the extent of geo-positioning systems, tools and methods? If you’re French, you should be crazy about abbreviations – so hang on : GPS, GNSS, EGNOS, DOP, TTF, SBAS, LBAS, WAAS, Read more about Geo-positioning in agriculture[…]
We will work here on a dataset that is fairly well known in spatial analysis – the “meuse” dataset – in order to reintroduce a number of concepts to those who would like to engage in linear modelling of spatialized data. This case study will be treated under the R language. One of the big Read more about Linear modelling of spatial data in R[…]
If there is one subject that is neglected when working with spatialized data, it is the reference coordinate system. In general, we try to spend as little time as possible on the subject – often because it’s not very clear – and assume that we have collected our data in a super coordinate system or Read more about Coordinate reference systems[…]
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[…]