Lothar Ratschbacher

Chunlei Xu, Michael Pregesbauer, Naga Sravani Chilukuri et al.

Ground Penetrating Radar (GPR) has been widely studied as a tool for extracting soil parameters relevant to agriculture and horticulture. When combined with Machine Learning (ML) methods, air-coupled Stepped Frequency Continuous Wave Ground Penetrating Radar (SFCW GPR) measurements could offer a cost-effective way to obtain depth-resolved soil data. As a first step of our study in this direction, we conducted an extensive field survey using a tractor-mounted air-coupled SFCW GPR instrument. Leveraging ML-based data processing, we evaluate the GPR instrument's ability by predicting the apparent electrical conductivity (ECaR) measured by a co-recorded Electromagnetic Induction (EMI) instrument. The large-scale field measurement campaign with 3472 co-registered and geo-located GPR and EMI data samples distributed over approximately 6600 square meters was performed on a golf course. This terrain offers high surface homogeneity but also presents the challenge of subtle soil parameter variations. Based on the results, we discuss challenges in this multi-sensor regression setting and propose the use of the nugget-to-sill ratio as a performance metric for evaluating ML models in agricultural field survey applications.

Alexandre Fioravante de Siqueira, Wagner Massayuki Nakasuga, Sandro Guedes et al.

The major challenges of automatic track counting are distinguishing tracks and material defects, identifying small tracks and defects of similar size, and detecting overlapping tracks. Here we address the latter issue using WUSEM, an algorithm which combines the watershed transform, morphological erosions and labeling to separate regions in photomicrographs. WUSEM shows reliable results when used in photomicrographs presenting almost isotropic objects. We tested this method in two datasets of diallyl phthalate (DAP) photomicrographs and compared the results when counting manually and using the classic watershed. The mean automatic/manual efficiency ratio when using WUSEM in the test datasets is 0.97 +/- 0.11.