Discriminating crop and other canopies by overlapping binary image layers

Doi, Ryoichi

doi:10.1117/1.oe.52.2.020502

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…Grayscale images that show the intensity values of R and G were prepared [ 21 ]. Each grayscale image was subjected to brightness adjustment of the entire image [ 11 ].…”

Section: Methodsmentioning

confidence: 99%

“…In the same image, the Microsoft Office gamut was pasted to monitor how the pixels of various colors are shown by the handling processes. From the RGwhtB or the RGrgbyB JPEG image, the grayscale images that show the intensity values of B, cyan (C), magenta (M), yellow (Y), key black (K), and lightness ( L *) and the values of a * and b * were prepared [ 21 ]. CMYK images were generated with the International Color Consortium profile of US Web Coated (SWOP) v2 for digital output such as color printing.…”

Section: Methodsmentioning

confidence: 99%

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Pixel Color Clustering of Multi-Temporally Acquired Digital Photographs of a Rice Canopy by Luminosity-Normalization and Pseudo-Red-Green-Blue Color Imaging

Doi

Arif

Setiawan

et al. 2014

The Scientific World Journal

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Red-green-blue (RGB) channels of RGB digital photographs were loaded with luminosity-adjusted R, G, and completely white grayscale images, respectively (RGwhtB method), or R, G, and R + G (RGB yellow) grayscale images, respectively (RGrgbyB method), to adjust the brightness of the entire area of multi-temporally acquired color digital photographs of a rice canopy. From the RGwhtB or RGrgbyB pseudocolor image, cyan, magenta, CMYK yellow, black, L*, a*, and b* grayscale images were prepared. Using these grayscale images and R, G, and RGB yellow grayscale images, the luminosity-adjusted pixels of the canopy photographs were statistically clustered. With the RGrgbyB and the RGwhtB methods, seven and five major color clusters were given, respectively. The RGrgbyB method showed clear differences among three rice growth stages, and the vegetative stage was further divided into two substages. The RGwhtB method could not clearly discriminate between the second vegetative and midseason stages. The relative advantages of the RGrgbyB method were attributed to the R, G, B, magenta, yellow, L*, and a* grayscale images that contained richer information to show the colorimetrical differences among objects than those of the RGwhtB method. The comparison of rice canopy colors at different time points was enabled by the pseudocolor imaging method.

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“…Grayscale images that show the intensity values of R and G were prepared [ 21 ]. Each grayscale image was subjected to brightness adjustment of the entire image [ 11 ].…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Pixel Color Clustering of Multi-Temporally Acquired Digital Photographs of a Rice Canopy by Luminosity-Normalization and Pseudo-Red-Green-Blue Color Imaging

Doi

Arif

Setiawan

et al. 2014

The Scientific World Journal

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“…Doi [84] used ML knowledge to discriminate rice from weeds from paddy fields by overlapping and merging 13 layers of binary images of red-green-blue and other color components (cyan, magenta, yellow, black, and white). These color components were captured using a digital camera (Cyber-shot DSC T-700, Sony) and used as input to specify the pixels with target intensity values based on mean ranges with ±3× standard deviation.…”

Section: Machine Learning (Ml)mentioning

confidence: 99%

Weed Detection in Rice Fields Using Remote Sensing Technique: A Review

et al. 2021

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This paper reviewed the weed problems in agriculture and how remote sensing techniques can detect weeds in rice fields. The comparison of weed detection between traditional practices and automated detection using remote sensing platforms is discussed. The ideal stage for controlling weeds in rice fields was highlighted, and the types of weeds usually found in paddy fields were listed. This paper will discuss weed detection using remote sensing techniques, and algorithms commonly used to differentiate them from crops are deliberated. However, weed detection in rice fields using remote sensing platforms is still in its early stages; weed detection in other crops is also discussed. Results show that machine learning (ML) and deep learning (DL) remote sensing techniques have successfully produced a high accuracy map for detecting weeds in crops using RS platforms. Therefore, this technology positively impacts weed management in many aspects, especially in terms of the economic perspective. The implementation of this technology into agricultural development could be extended further.

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“…The number of analyzed pixels in the target area was 1350. Using Adobe Photoshop 7.0, the grayscale images that show the intensity values of R, G, and B, and the value of a* were prepared (Doi, 2013). The R + G (RGB yellow) grayscale image was prepared by merging the R and G grayscale images at the same weight (Doi, 2014).…”

Section: Digital Photography and Handling Digital Photographsmentioning

confidence: 99%

Detection of Cassava Leaves in Multi-Temporally Acquired Digital Images of a Cassava Field Under Different Brightness Levels by Simultaneous Binarization of the Images Based on Indices of Redness/Greenness

Srisutham¹,

Doi²,

Polthanee³

et al. 2014

MAS

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Plant leaf area reveals various types of abnormalities which can enable appropriate plant/crop management actions. The quantification of plant leaf area is now feasible using commonly available digital photographing tools. Changes in brightness, however, make it difficult to compare leaf areas in digital photographs acquired at multiple time points. This difficulty could be overcome by employing an index of redness/greenness (R/G), which was suggested to be one of the best indices to discriminate between plant leaves and other objects such as soils. R/G and other indices were examined when discriminating cassava leaves from other objects in a field. A surveillance camera captured digital photographs on a daily basis. Of these, 183 photographs were stored. They were pasted into a single image file and simultaneously analyzed. The International Commission on Illumination color model's a* was the best index in the discrimination, with a distinctiveness score of 1.36. R/G was the second best, with a distinctiveness score of 0.70. The percentage of leaf-likely pixels followed sigmoidal patterns with time, resulting in great coefficients of determination of 0.981 (a*) and 0.965 (R/G). The percentage of leaf-likely pixels and cassava leaf weight had a real-time response relationship. The range of the 95% confidence limit was narrowed from -16 to +14% of a predicted value of 98% leaf-likely pixels for R/G to ±12% for a*. Thus, the simultaneous binarization and the detection of leaf-likely pixels in the photographs acquired under different brightness levels was enabled with improved discrimination accuracy by employing a*.

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Discriminating crop and other canopies by overlapping binary image layers

Cited by 6 publications

References 6 publications

Pixel Color Clustering of Multi-Temporally Acquired Digital Photographs of a Rice Canopy by Luminosity-Normalization and Pseudo-Red-Green-Blue Color Imaging

Pixel Color Clustering of Multi-Temporally Acquired Digital Photographs of a Rice Canopy by Luminosity-Normalization and Pseudo-Red-Green-Blue Color Imaging

Weed Detection in Rice Fields Using Remote Sensing Technique: A Review

Detection of Cassava Leaves in Multi-Temporally Acquired Digital Images of a Cassava Field Under Different Brightness Levels by Simultaneous Binarization of the Images Based on Indices of Redness/Greenness

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