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Machine Learning Methods for Predicting Site-Specific Profitability from Sensor-Based Nitrogen Applications
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.orgCitation: 2021 ASABE Annual International Virtual Meeting 2101082.(doi:10.13031/aim.202101082)
Authors: Chunxia Wu, Joe Luck, Laura Thompson, Laila Puntel, Samantha Teten
Keywords: machine learning, Marginal Net Return (MNR), precision agriculture, SENSE profitability, sensor-based nitrogen application
We first partitioned study strips into 20 feet long paired (Grower vs. SENSE) study units. We then created a 200-feet long aggregation block to move along each study strip to integrate N application data, yield data, and site condition data. Within each block, we compared yields and marginal net returns (MNR) from Grower and SENSE treatments and quantified the profitability of the sensor-based N application as the difference between SENSE MNR and Grower MNR. Three types of features were constructed to characterize site conditions within each aggregation block: 1) features derived from DEM, TWI, 2) features constructed to quantify the complexity of soil combination, and 3) features extracted from the SSURGO database on soil properties. The data processing generated 12000+ samples with 70+ features.
Multiple supervised methods were applied to model the relationship between the site features and the profitability of the sensor-based N application (target outputs are -1 for nonprofitable, 1 for profitable). We compared the ML models and found that the Random Forest classification model performed the best with a close to 90% test accuracy.(Download PDF) (Export to EndNotes)