First Implementation of ML Pipeline to Solve Africa Soil Chemical-Physical Properties Prediction (Kaggle)
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Africa.ipynb
Ca-2014-09-25 07:49:17.561349.txt
P-2014-09-25 08:11:43.773154.txt
README.md
SOC-2014-09-25 08:48:56.484713.txt
Sand-2014-09-25 09:07:23.374776.txt
pH-2014-09-25 08:32:25.626052.txt
ridge-pH-2014-09-25 08:48:25.247992.pickle
svr-Ca-2014-09-25 08:11:09.934405.pickle
svr-P-2014-09-25 08:31:53.611168.pickle
svr-SOC-2014-09-25 09:06:46.651682.pickle
svr-Sand-2014-09-25 09:23:22.520904.pickle
training.csv

README.md

Africa Soil Property Prediction Challenge (Kaggle)

The challenge of this Kaggle competition is to predict predict physical and chemical properties of soil using spectral measurements

Advances in rapid, low cost analysis of soil samples using infrared spectroscopy, georeferencing of soil samples, and greater availability of earth remote sensing data provide new opportunities for predicting soil functional properties at unsampled locations. Soil functional properties are those properties related to a soil’s capacity to support essential ecosystem services such as primary productivity, nutrient and water retention, and resistance to soil erosion. Digital mapping of soil functional properties, especially in data sparse regions such as Africa, is important for planning sustainable agricultural intensification and natural resources management. Diffuse reflectance infrared spectroscopy has shown potential in numerous studies to provide a highly repeatable, rapid and low cost measurement of many soil functional properties. The amount of light absorbed by a soil sample is measured, with minimal sample preparation, at hundreds of specific wavebands across a range of wavelengths to provide an infrared spectrum. The measurement can be typically performed in about 30 seconds, in contrast to conventional reference tests, which are slow and expensive and use chemicals.

Conventional reference soil tests are calibrated to the infrared spectra on a subset of samples selected to span the diversity in soils in a given target geographical area. The calibration models are then used to predict the soil test values for the whole sample set. The predicted soil test values from georeferenced soil samples can in turn be calibrated to remote sensing covariates, which are recorded for every pixel at a fixed spatial resolution in an area, and the calibration model is then used to predict the soil test values for each pixel. The result is a digital map of the soil properties.

This competition asks to predict 5 target soil functional properties from diffuse reflectance infrared spectroscopy measurements.

  • SOC: Soil organic carbon
  • pH: pH values
  • Ca: Mehlich-3 extractable Calcium
  • P: Mehlich-3 extractable Phosphorus
  • Sand: Sand content