Delineation of Bare Soil Field Areas from Unmanned Aircraft System Imagery with the Mean Shift Unsupervised Clustering and the Random Forest Supervised Classification
The use of aerial remote sensing platforms such as Unmanned Aircraft Systems (UAS) has been proven as a cost and time effective way to perform tasks related to precision agriculture and decision making. Two machine learning (ML) algorithms have been implemented on UAS blue and red band imagery to de...
| Published in: | Canadian Journal of Remote Sensing |
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| Main Authors: | , , , , , |
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2020-07-01
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| Online Access: | http://dx.doi.org/10.1080/07038992.2020.1763789 |
| Summary: | The use of aerial remote sensing platforms such as Unmanned Aircraft Systems (UAS) has been proven as a cost and time effective way to perform tasks related to precision agriculture and decision making. Two machine learning (ML) algorithms have been implemented on UAS blue and red band imagery to delineate field areas and extents of various bare soil fields: the Random Forest non-parametric supervised classifier and the unsupervised non-parametric Mean Shift clustering algorithm. Both ML algorithms perform exceptionally well. The mean Area Goodness of Fit (AGoF) for bare soil areas was greater than 99% and the mean Boundary Mean Positional Error (BMPE) was lower than 0.6 m for the 11 surveyed fields. Such precisions with ML algorithms will enable an easy automated field boundary delineation based on UAS imagery. Such information is needed by growers and crop insurance agencies for an accurate determination of the crop insurance premiums. |
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| ISSN: | 1712-7971 |