@article{
 title = {Study and comparison of color models for automatic image analysis in irrigation management applications},
 type = {article},
 year = {2015},
 keywords = {Automatic irrigation computation,Color classification,Color spaces,Image processing in agriculture},
 pages = {158-166},
 volume = {151},
 month = {3},
 publisher = {Elsevier},
 day = {31},
 id = {729c9664-89b2-3209-8122-a70a86dd3603},
 created = {2023-02-17T10:47:58.642Z},
 accessed = {2023-02-17},
 file_attached = {true},
 profile_id = {f1f70cad-e32d-3de2-a3c0-be1736cb88be},
 group_id = {5ec9cc91-a5d6-3de5-82f3-3ef3d98a89c1},
 last_modified = {2023-02-28T07:44:54.143Z},
 read = {true},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 folder_uuids = {bd3c6f2e-3514-47cf-bc42-12db8b9abe45},
 private_publication = {false},
 abstract = {Images can provide valuable information on the percentage of ground cover, which is essential in determining crop irrigation needs. Techniques based on color analysis allow classifying accurately and efficiently soil/plant regions in the images. Many color spaces have been proposed, among them: RGB, rgb, XYZ, L*. a*. b*, L*. u*. v*, HSV, HLS, YCrCb, YUV, I1I2I3 and TSL. Different possibilities to model the probability distribution of a given color class appear for each space; one of the most widespread non-parametric methods is modeling using histograms. This presents various alternatives in order to represent a color class: the number of channels, which channels to use, and the size of histograms. Using a wide and varied set of images of lettuce crops (. Lactuca sativa)-previously classified manually in soil and plant pixels-a comprehensive analysis and comparison of the proposed color models has been conducted for the soil/plant classification problem. The experimental results demonstrate the superiority of models that separate luminance from chrominance. In particular, L*. a*. b* provides the best results with a* channel, producing a 99.2% of correct classification. Further processing stages improve this performance up to 99.5% accuracy, taking less than 1/3 of a second per image in a normal laptop. These results can be applied to reduce water consumption by optimizing the accuracy and efficiency of automatic image analysis of crops.},
 bibtype = {article},
 author = {García-Mateos, G. and Hernández-Hernández, J. L. and Escarabajal-Henarejos, D. and Jaén-Terrones, S. and Molina-Martínez, J. M.},
 doi = {10.1016/J.AGWAT.2014.08.010},
 journal = {Agricultural Water Management}
}

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Techniques based on color analysis allow classifying accurately and efficiently soil/plant regions in the images. Many color spaces have been proposed, among them: RGB, rgb, XYZ, L*. a*. b*, L*. u*. v*, HSV, HLS, YCrCb, YUV, I1I2I3 and TSL. Different possibilities to model the probability distribution of a given color class appear for each space; one of the most widespread non-parametric methods is modeling using histograms. This presents various alternatives in order to represent a color class: the number of channels, which channels to use, and the size of histograms. Using a wide and varied set of images of lettuce crops (. Lactuca sativa)-previously classified manually in soil and plant pixels-a comprehensive analysis and comparison of the proposed color models has been conducted for the soil/plant classification problem. The experimental results demonstrate the superiority of models that separate luminance from chrominance. 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