Pattern Recognition in Agricultural Soils Using Principal Component Analysis and Interdigitated Microwave Sensors

Pattern recognition in agricultural soils using interdigitated microwave sensors combined with principal component analysis offers a novel approach to soil characterization. In this study, soil samples were collected at the “El Potrillo” ranch, Chihuahua, Mexico, following extraction and preparation protocols. The results of the PCA of the soils revealed that the first two principal components (PC1 and PC2) explain 99.99% of the variability, with the first principal component accounting for 99.73% of the total variability, allowing for effective discrimination of the samples. A high correlation was observed between the behavior patterns of the deeper samples in the soil and the reference solutions with a lower glyphosate concentration. On the other hand, the samples from the soil surface showed greater similarity to deionized and distilled water. Furthermore, when evaluating interdigitated sensor configurations, it was determined that the 3F sensor is redundant and can therefore be excluded. These findings highlight the effectiveness of the combined use of microwave sensors and PCA to identify patterns in agricultural soils.