Dr. D Mulla

CFANS Soil, Water & Climate
College of Food, Ag & Nat Res Sci
Twin Cities
Project Title: 
Machine Learning Applications in Agriculture

In the context of a rapid increase in phosphorus (P) fertilizer prices, new techniques are needed for geospatial predictions of soil P for improved P fertilizer management, while increasing farmer profitability and reducing environmental concerns. One of the biggest issues in site-specific phosphorus management is the substantial spatial variability in plant available P across fields. This leads to an expensive and laborious process for accurate mapping soil P using a traditional soil sampling and laboratory analysis approach. To overcome this barrier, emerging sensing and data interpretation technologies should be employed to accurately assess spatial heterogeneity of P within fields, and to help farmers optimize mineral P fertilization recommendations. This study will use machine learning algorithms and novel data fusion concepts to analyze integrated high-density spatial data layers related to the potential P availability to plants.

  • Machine learning algorithms will be used to evaluate the relative importance of different auxiliary soil properties at predicting plant-available P. High-density data mining techniques and various sensor data fusion algorithms and optimization techniques will be used to predict P spatial distribution and identify site-specific management zones.
  • Auxiliary data to predict available P include satellite imagery across years and dates, high-density apparent soil electrical conductivity (ECa), gamma-ray spectrometry, high-resolution topography as well as soil test ground truth data from several agricultural fields. Spatial maps will be prepared for auxilliary data, including elevation data based on a Real-Time Kinematic (RTK) global navigation satellite systems (GNSS) receiver, DUALEM-21S sensing, and a gamma-ray (SoilOptix) spectrometer. 
  • The machine learning models will be used to predict P and generate high resolution maps showing P status zones and recommendation maps at the local level. The results of machine learning predictions will be used to develop a robust decision tool for phosphorus precision nutrient management and variable rate technology. 
  • Environmental sensor covariate fusion combined with spatial machine learning algorithms have potential to be a useful tool to help farmers save P fertilizer and preserve environmental resources through understanding the available P spatial variability across agricultural fields. 

Project Investigators

Abdelkrim Lachgar
Dr. D Mulla
Are you a member of this group? Log in to see more information.