Application of Low-cost Color Sensor Technology in Soil Data Collection and Soil Science Education

Sensor technologies provide opportunities to increase the quality and quantity of soils data while introducing new techniques and tools for classrooms. Linear regression models were developed for organic carbon prediction using color data gathered with the Nix Pro™ for dry (R2 = 0.7978, MSPE = 0.0819), and moist soils (R2 = 0.7254, MSPE = 0.1536). A mobile application, the Soil Scanner app, was created to allow for a more soil science dedicated interface that would allow users to create their own database consisting of GPS location and soil color data gathered using the Nix Pro™. The final application produced results in multiple color systems, including Munsell, recorded GPS location, sample depth, moisture conditions, in-field or laboratory settings, and a photograph of the soil sample. All data could then be uploaded to an online database. The GPS location allows for easy integration of data into GIS mapping software for the spatial manipulation of soils data. The application was tested by generating GIS maps showing the gradient of soil color across two field surfaces. The Nix Pro™ color sensor functions as a successful teaching tool and, coupled with the Soil Scanner app, offers a new means of gathering and storing reliable soils data. There is added benefit to having a soil science application that can be updated to include further analysis methods, resulting in an ever growing soils database. A laboratory exercise was developed that introduced students in an entry level soils course to the importance of soil color and the methods used to determine soil color. Students were then asked to determine the color of three soil samples using the Nix Pro™ and the standard Munsell Color Chart before conducting simple statistical analysis and responding to a questionnaire. Responses indicate that the Nix Pro™ was the preferred method of color analysis and students felt the sensor to be a more reliable method than traditional color books

Predicting Soil Organic Carbon and Total Nitrogen at the Farm Scale Using Quantitative Color Sensor Measurements

Sensor technology can be a reliable and inexpensive means of gathering soils data for soil health assessment at the farm scale. This study demonstrates the use of color system readings from the Nix ProTM color sensor (Nix Sensor Ltd., Hamilton, ON, Canada) to predict soil organic carbon (SOC) as well as total nitrogen (TN) in variable, glacial till soils at the 147 ha Cornell University Willsboro Research Farm, located in Upstate New York, USA. Regression analysis was conducted using the natural log of SOC (lnSOC) and the natural log of TN (lnTN) as dependent variables, and sample depth and color data were used as predictors for 155 air dried soil samples. Analysis was conducted for combined samples, Alfisols, and Entisols as separate sample sets and separate models were developed using depth and color variables, and color variables only. Depth and L* were significant predictors of lnSOC and lnTN for all sample sets. The color variable b* was not a significant predictor of lnSOC for any soil sample set, but it was for lnTN for all sample sets. The lnSOC prediction model for Alfisols, which included depth, had the highest R2 value (0.81, p-value < 0.001). The lnSOC model for Entisols, which contained only color variables, had the lowest R2 (0.62, p-value < 0.001). The results suggest that the Nix ProTM color sensor is an effective tool for the rapid assessment of SOC and TN content for these soils. With the accuracy and low cost of this sensor technology, it will be possible to greatly increase the spatial and temporal density of SOC and TN estimates, which is critical for soil management