SBIR Phase I: Creating high-quality, lower-cost soil maps using machine learning algorithms

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The broader impact/ commercial potential of this SBIR Phase I project will be to produce high-quality, accurate, high-resolution soil maps for agronomists and farmers. Accurate soil information is a fundamental driver of better, more efficient crop/soil management. The new technology would significantly increase farm profitability, lower food costs, and improve environmental protection and sustainability. Making higher quality soil fertility mapping readily available and usable is the goal of this project. This technology is expected to result in increased crop yield while allowing for decreased input costs, leading to higher profits in an industry that chronically suffers from low profit margins. The expected benefits include more environmentally responsible farm management and better manure-management planning, nutrient-management planning, precision farming, land use planning, planting decisions, evaluating stressors on plants, field conditioning, crop rotation, and prediction/interpretation of yields. These will result in increased farm profitability, more efficient application of nitrogen fertilizers, and increased soil health and fertility for plants. This project advances an innovative technology has three key components to produce maps of essential soil nutrients in training fields and beyond — maps that currently require extensive sampling while producing inadequate data. The first is a digital hill-slope position to select optimal sampling locations to represent the soil variability across the landscape, eliminating the need to take unnecessary soil samples. The second element leverages advanced machine-learning algorithms insensitive to the quantity of sample size. The third element is its ability to select suitable remotely sensed information (terrain derivatives and satellite imagery). The technology will select appropriate analysis scales of terrain derivatives to capture all potential soil variability. It will then select and use proper bands of satellite imagery, based on spatial, temporal, and spectral resolution, to decrease the risks of overfitting and computation time. Unlike currently available methods, this technology can predict the soil nutrients inside the training fields and beyond — i.e., this technology has the potential to predict soil properties in neighboring fields — using the soil information obtained from training fields—without the need for additional samples.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.