README.md
In niconaut/crop.row.finder: Crop Row Detector
CropDectectR: a crop row detection package
Important Update on 6/19/20!
A lot more has been researched since version 0.9000, around mid-July we will be adding more capabilities to the package that include: biomass identification with deep learning, GPS positioning, row matching and labeling throughout different images, and true distance apart for images in the same longitudinal line.
Introduction
crop.row.finder is a package developed to identify crop rows from a drone image. It uses techniques such as: ExG color transformation, Otsu Transformation, and Morphology.
Workflow
A color image of crop rows is first transformed into a grayscale image using ExG[1], then made binary using the Otsu Transformation[2]. The binary image is then modified using morphology[3] to best isolate crop row centers and remove all unwanted noise (leaves, grass patches, weeds).
Once modified to an acceptable level, the image is rotated to find the degree of rotation that gives the most vertical crop rows. Crop rows need to be vertical so when taking the average of each column (value between [0,1]), high value columns can be identified as crop rows.
Ratios between local maxima and minima determine the 'goodness' of the crop row. large ratios = well defined crop rows, and the rotation with the most 'good ratios' is the best rotation for crop row identification of that image.
Results
Demonstration of transformation on crop rows.
Before:
After:
Most Recent Work (cleaning up code for github)
Isolating individual rows:
[1] Woebbecke et al., 1995
D.M. Woebbecke, G.E. Meyer, K. Von Bargen, D.A. Mortensen
Color indices for weed identification under various soil, residue, and lighting conditions
Transactions of the ASAE, 38 (1) (1995), pp. 259-269
[2] Otsu, 1975
N. Otsu
A threshold selection method from gray-level histograms
Automatica, 11 (285–296) (1975), pp. 23-27
[3] https://en.wikipedia.org/wiki/Mathematical_morphology
niconaut/crop.row.finder documentation built on June 29, 2020, 6:10 a.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
CropDectectR: a crop row detection package
Important Update on 6/19/20!
A lot more has been researched since version 0.9000, around mid-July we will be adding more capabilities to the package that include: biomass identification with deep learning, GPS positioning, row matching and labeling throughout different images, and true distance apart for images in the same longitudinal line.
Introduction
crop.row.finder is a package developed to identify crop rows from a drone image. It uses techniques such as: ExG color transformation, Otsu Transformation, and Morphology.
Workflow
A color image of crop rows is first transformed into a grayscale image using ExG[1], then made binary using the Otsu Transformation[2]. The binary image is then modified using morphology[3] to best isolate crop row centers and remove all unwanted noise (leaves, grass patches, weeds).
Once modified to an acceptable level, the image is rotated to find the degree of rotation that gives the most vertical crop rows. Crop rows need to be vertical so when taking the average of each column (value between [0,1]), high value columns can be identified as crop rows.
Ratios between local maxima and minima determine the 'goodness' of the crop row. large ratios = well defined crop rows, and the rotation with the most 'good ratios' is the best rotation for crop row identification of that image.
Results
Demonstration of transformation on crop rows.
Before:
After:
Most Recent Work (cleaning up code for github)
Isolating individual rows:
[1] Woebbecke et al., 1995 D.M. Woebbecke, G.E. Meyer, K. Von Bargen, D.A. Mortensen Color indices for weed identification under various soil, residue, and lighting conditions Transactions of the ASAE, 38 (1) (1995), pp. 259-269
[2] Otsu, 1975 N. Otsu A threshold selection method from gray-level histograms Automatica, 11 (285–296) (1975), pp. 23-27
[3] https://en.wikipedia.org/wiki/Mathematical_morphology
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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