Assessment of Soil Fertility Using Induced Fluorescence and Machine Learning

Sensors (Basel). 2022 Jun 20;22(12):4644. doi: 10.3390/s22124644.

Abstract

Techniques such as proximal soil sampling are investigated to increase the sampling density and hence the resolution at which nutrient prescription maps are developed. With the advent of a commercial mobile fluorescence sensor, this study assessed the potential of fluorescence to estimate soil chemical properties and fertilizer recommendations. This experiment was conducted over two years at nine sites on 168 soil samples and used random forest regression to estimate soil properties, fertility classes, and recommended N rates for maize production based on induced fluorescence of air-dried soil samples. Results showed that important soil properties such as soil organic matter, pH, and CEC can be estimated with a correlation of 0.74, 0.75, and 0.75, respectively. When attempting to predict fertility classes, this approach yielded an overall accuracy of 0.54, 0.78, and 0.69 for NO3-N, SOM, and Zn, respectively. The N rate recommendation for maize can be directly estimated by fluorescence readings of the soil with an overall accuracy of 0.78. These results suggest that induced fluorescence is a viable approach for assessing soil fertility. More research is required to transpose these laboratory-acquired soil analysis results to in situ readings successfully.

Keywords: induced fluorescence; precision agriculture; proximal soil sensing; soil fertility.

MeSH terms

  • Agriculture* / methods
  • Fertilizers
  • Fluorescence
  • Machine Learning
  • Soil* / chemistry
  • Zea mays

Substances

  • Fertilizers
  • Soil

Grants and funding

This research received support from the Colorado State University Agricultural Experiment Station and the Cornell University Agricultural Experiment Station. In-kind support was provided by Force-A, Orsay, France.