Author(s)

Reginald S. Fletcher

Crop Production Systems Research Unit, Agricultural Research Service, United States Department of Agriculture, Stoneville, USA.

Open-source and free tools are readily available to the public to process data and assist producers in making management decisions related to agricultural landscapes. On-the-go soil sensors are being used as a proxy to develop digital soil maps because of the data they can collect and their ability to cover a large area quickly. Machine learning, a subcomponent of artificial intelligence, makes predictions from data. Intermixing open-source tools, on-the-go sensor technologies, and machine learning may improve Mississippi soil mapping and crop production. This study aimed to evaluate machine learning for mapping apparent soil electrical conductivity (EC_a) collected with an on-the-go sensor system at two sites (i.e., MF2, MF9) on a research farm in Mississippi. Machine learning tools (support vector machine) incorporated in Smart-Map, an open-source application, were used to evaluate the sites and derive the apparent electrical conductivity maps. Autocorrelation of the shallow (EC_as) and deep (EC_ad) readings was statistically significant at both locations (Moran’s I, p 0.001); however, the spatial correlation was greater at MF2. According to the leave-one-out cross-validation results, the best models were developed for EC_as versus EC_ad. Spatial patterns were observed for the EC_as and EC_ad readings in both fields. The patterns observed for the EC_ad readings were more distinct than the EC_as measurements. The research results indicated that machine learning was valuable for deriving apparent electrical conductivity maps in two Mississippi fields. Location and depth played a role in the machine learner’s ability to develop maps.

Spatial Variability, Machine Learning, Electrical Conductivity, Mapping, Data Mining

Fletcher, R. (2023) Machine Learning Mapping of Soil Apparent Electrical Conductivity on a Research Farm in Mississippi. Agricultural Sciences, 14, 915-924. doi: 10.4236/as.2023.147061.