Abstract
The development of low-cost tools for rapid soil assessment has become a crucial field due to the increasing demands in food production and carbon storage. However, current methods for soil evaluation are costly and cannot provide enough information about the quality of samples. This work reports for the first time a low-cost 3D printed device that can be used for soil classification as well as the study of biological activity. The system incorporated multiple physical and gas sensors for the characterisation of sample types and profiling of soil volatilome. Sensing data were obtained from 31 variables, including 18 individual light wavelengths that could be used to determine seed germination rates of tomato plants. A machine learning algorithm was trained using the data obtained by characterising 75 different soil samples. The algorithm could predict seed germination rates with high accuracy (RSMLE = 0.01, and R(2) = 0.99), enabling an objective and non-invasive study of the impact of multiple environmental parameters in soil quality. To allow for a more complete profiling of soil biological activity, molecular imprinted-based fine particles were designed to quantify tryptophol, a quorum-sensing signalling molecule commonly used by fungal populations. This device could quantify the concentration of tryptophol down to 10 nM, offering the possibility of studying the interactions between fungi and bacterial populations. The final device could monitor the growth of microbial populations in soil, and offering an accurate assessment of quality at a low cost, impacting germination rates by incorporating hybrid data from the microsensors.