Current issue: 53(3)

Under compilation: 53(4)

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Silva Fennica 1926-1997
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Acta Forestalia Fennica
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Articles containing the keyword 'supervised machine learning'.

Category: Research article

article id 10068, category Research article
Lari Melander, Risto Ritala, Markus Strandström. (2019). Classifying soil stoniness based on the excavator boom vibration data in mounding operations. Silva Fennica vol. 53 no. 2 article id 10068. https://doi.org/10.14214/sf.10068
Highlights: An excavator was equipped with an inertial measurement unit for taking automatic measurements of soil stoniness during mounding work; Supervised machine-learning classifiers were trained utilizing both the automatically measured data and manual stoniness measurements; The class prediction for the soil stoniness achieved an accuracy of 70% when assigned to constant grid cells.

The stoniness index of forest soil describes the stone content in the upper soil layer at depths of 20–30 centimeters. This index is not available in any existing map databases, and traditional measurements for the stoniness of the soil have always necessitated laborious soil-penetration methods. Knowledge of the stone content of a forest site could be of use in a variety of forestry operations. This paper presents a novel approach to obtaining automatic measurements of soil stoniness during an excavator-based mounding operation. The excavator was equipped with only a low-cost inertial measurement unit and a satellite navigation receiver. Using the data from these sensors and manually conducted soil stoniness measurements, supervised machine learning methods were utilized to build a model that is capable of predicting the stoniness class of a given mounding location. This study compares different classifiers and feature selection methods to find the most promising solution for this learning problem. The discussion includes a proposition for a meaningful measurement resolution of the soil’s stoniness, and a practical method for evaluating the variability of the stone content of the soil. The results indicate that it is possible to predict the soil stoniness class with 70% accuracy using only the inertial and location measurements.

  • Melander, Automation Technology and Mechanical Engineering, Tampere University, FI-33014 Tampere University, Finland ORCID ID: http://orcid.org/0000-0003-3662-5187 E-mail: lari.melander@tuni.fi (email)
  • Ritala, Automation Technology and Mechanical Engineering, Tampere University, FI-33014 Tampere University, Finland ORCID ID: http://orcid.org/0000-0003-0721-9948 E-mail: risto.ritala@tuni.fi
  • Strandström, Metsäteho Oy, Vernissakatu 1, FI-01300 Vantaa, Finland ORCID ID:E-mail: markus.strandstrom@metsateho.fi

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