Virtual Soil Survey from Sensors and Machine Learning
High-resolution soil surveys are usually scarce and nationwide surveys tend to have small scales and do not provide much insight on field soil characterization. Therefore, the use of sensors, especially proximal soil sensors (PSS), has become an important tool for soil characterization and delineation of different zones within a field. Thus, we analyzed and compared the capability of PSSs platforms for the delineation of differences in soils within a field. The results presented that similar zones to the ones determined by a high-resolution soil survey could be achieved using PSSs.
CEU Credits: 0.5
Bio: Felippe Karp, Olds College
Felippe Karp got his bachelor’s degree in Agronomic Engineering from the School of Agriculture “Luiz de Queiroz” (ESALQ), University of Sao Paulo – Brazil, and his Master’s at Science in Plant, Environmental Management and Soil Sciences from the School of Plant Environmental and Soil Sciences, Louisiana State University – USA. Currently, he is a Ph.D. Candidate at the Bioresource Engineering Department from McGill University, and is involved in a partnership with Olds College under the HyperLayer Data Project. Felippe is passionate about the concept of Precision Agriculture and enjoys working with agricultural data to improve the current agricultural practices toward more sustainable, lucrative, and productive agriculture.