About 30 hours of workload
This first part of the MLOOSC is aiming at (re)discovering different software and interpreted programming language useful for the data analysis in agriculture. The basics of QGIS will be developed in this chapter thanks to the videos created by the teachers specialists of that domain, from UTAD (Portugal) and will be used in the chapter 3 about geographical information system and remote sensing. For R and Matlab most of the works will be done in the online platform dedicated to master those software and programming language directly through their official website (respectively RStudio cloud and Mathworks Matlab Onramp). Interactive exercises are given through those online platforms and ease the way to learn those languages. Finally for Octave, credentials are given to Dr. Nicolas Neuman who contributed to the videos concerning this software. R, Octave and Matlab will be used in some exercises in the chapter 4 about data analysis and modelling and in the chapter 5 about automation. At the end of the chapter, each participant will have the basics of those interesting software: installation, user interface,project or script edition, basics of syntax and data visualization (graphics) for R, Octave and Matlab.
For the external contents
Materials coming directly from R Studio and Matlab Onramp
Dr Nicolas Neuman for tutorials of Octave
For the members of the Smart-farming project consortium who contributed to the content
About 25 hours of workload
The objective of sensors technologies chapter is to give an overview of different crop, soil and climate sensor techniques that can be used in precision agriculture. The chapter will give a brief theoretical background of the different techniques for soil, crop and weather monitoring, and their link to precision agriculture applications including some examples of how the techniques can be used in the form of case studies. Case studies are concerning variable nitrogen fertilization, variable weeding, variable liming. Finally an additional part is focusing on the calibration and validation of sensors.
About 20 hours of workload
The objective of the chapter is firstly to give an overview of what is a remote sensing, and its basic principles including wavelengths and vegetation reflection as well as the process to follow when using a drone for capturing images on field from flight preparation to images processing. Then an overview of what is geographical information (GIS) are seen and it will be directly accompanied by self-conducted exercises in QGIS based on images captured with a drone.
QGIS is necessary for the practical exercises in GIS.
About 30 hours of workload
The general framework of precision farming starts from the development of methodological approaches for soil survey followed by soil fertility mapping using geostatistics. Then the areas are delineated according to soil maps and yield patterns and modelling allows to improve the fertilizer recommendation for each zone in that area. The objective of this chapter is firstly to learn basics theory about data analysis in general, and about soil map, geostatistics and interpolation in particular inducing the notion of spatial and temporal variability. Then the second step will be to learn crop modelling and its role for nitrogen optimization with practical exercises with detailed instructions about that topic.
R and Octave/Matlab are necessary for the practical exercises.
About 20 hours of workload
The objective of this chapter is, in the first part, to know how to regulate a process in automation systems and the components necessary for this regulation. After viewing the role of automation in agriculture, the lessons will start from the control strategy and specifications, and the dynamics of the systems in automation (with exercises in Octave/Matlab included). Then actuators are needed to make the changes or control something in the system and will be viewed in the second part of the chapter. Finally, the chapter aims also at learning the basics of image analysis from the definition of an image to the detection of some specific traits in an image, through exercises in Matlab.
Octave or Matlab is necessary for the practical exercises.
About 4 hours of workload
The objective of this chapter is firstly to understand the challenges and opportunities of robotics in agriculture, secondly to know how robots work and how do they perceive their environment and finally to estimate their state through the position, velocity and acceleration variables. Examples of robots from Saga Robotics AS are given in the following lectures.
Structure of automation and robotics chapters were built jointly between the two teams responsible of those two topics.
