Assessing Nitrogen Content of Golf Course Turfgrass Clippings Using Spectral Reflectance

Keskin, Muharrem; Dodd, Roy B.; Han, Yuechen; Khalilian, Ahmad

doi:10.13031/2013.17717

Cited by 38 publications

(17 citation statements)

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“…Taylor et al (1998) reported 60% fertilizer savings in a variable-rate nitrogen application based on readings of Bermuda grass with a reflectance-based sensor. Keskin et al (2004) used 4 wavelength bands at 550, 680, 770, and 810 nm to predict nitrogen content of golf course turfgrass clippings (R 2 > 0.82). Keskin et al (2008) developed a portable sensor to predict the visual quality of turfgrass plots from the reflectance of red and NIR bands (R 2 = 0.60).…”

mentioning

confidence: 99%

Utilization of color parameters to estimate moisture content and nutrient levels of peanut leaves

Keskin¹,

Karanlık²,

Keskin³

et al. 2013

Turk J Agric For

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10 m with a sampling rate of 1 min from 2005 to 2009 were used. In the second part of the study, autoregressive (AR) and artificial neural network (ANN) models were applied for prediction of wind speed. The prediction methods were assessed by comparing the characteristic frequency components of the prediction series and the real series. The best results were obtained from the ANN model; however, the AR model was found to moderately show the spectral characteristics.

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mentioning

confidence: 99%

Utilization of color parameters to estimate moisture content and nutrient levels of peanut leaves

Keskin¹,

Karanlık²,

Keskin³

et al. 2013

Turk J Agric For

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“…The wavelengths carrying the most useful information should be selected as key wavelengths for simplifying the PLSR models [34]. These selected key wavelengths can be a guide to facilitate the modeling procedure, interpret the predicted results, and develop the lower price and higher speed instrument for online practice [5].…”

Section: Selection Of Key Wavelengthsmentioning

confidence: 99%

Fusion of Spectra and Texture Data of Hyperspectral Imaging for the Prediction of the Water-Holding Capacity of Fresh Chicken Breast Filets

Wang

Zhuang

et al. 2018

Applied Sciences

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This study investigated the fusion of spectra and texture data of hyperspectral imaging (HSI, 1000-2500 nm) for predicting the water-holding capacity (WHC) of intact, fresh chicken breast filets. Three physical and chemical indicators-drip loss, expressible fluid, and salt-induced water gain-were measured to be different WHC references of chicken meat. Different partial least squares regression (PLSR) models were established with corresponding input variables including the full spectra, key wavelengths, and texture variables, as well as the fusion data of key wavelengths and the corresponding texture variables, respectively. The results demonstrated that for drip loss and expressible fluid, texture data was an effective supplement to spectra data, and fusion data as an input variable could effectively improve the predictive ability of the independent prediction set (Rp = 0.80, RMSEp = 0.80; Rp = 0.56, RMSEp = 2.10). While the best model to predict salt-induced water gain was based on key wavelengths (Rp = 0.69, RMSEp = 18.04), this was mainly because salt-induced water gain was measured on mince samples, which lacked the important physical structure to represent the texture information of meat. Our results of this study demonstrated the potential to further improve the evaluation of the WHC of chicken meat by HSI.

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“…Therefore, the most useful wavelengths, that is, informative wavelengths, are preferable to be selected for data analysis. As claimed by Keskin et al (Keskin et al 2004), the wavelengths holding the most useful information should be recommended while the wavelengths carrying irrelevant information should be eliminated. To sum up, the implementation of informative wavelength selection will not only contribute to the reduction of information dimensionality and multicollinearity, but also simplify the modelling process and accelerate the data analysis.…”

Section: Informative Wavelength Selectionmentioning

confidence: 99%

Selection of Informative Spectral Wavelength for Evaluating and Visualising Enterobacteriaceae Contamination of Salmon Flesh

Sun

2015

Food Anal. Methods

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Enterobacteriaceae is one kind of harmful microorganisms commonly presented in raw fish products, and detection of Enterobacteriaceae plays a very important role in evaluating microbial contamination. This work was carried out to exploit the potential of emerging hyperspectral imaging technique to determine the Enterobacteriaceae contamination of salmon flesh during cold storage. The spectral information ranging from 900 to 1700 nm (239 wavelengths) was extracted to relate to the Enterobacteriaceae loads (recorded as log 10 CFU/g) using partial least square (PLS) regression, developing a PLS model with correlation coefficient of prediction (r P ) of 0.94 and root mean square error of prediction (RMSEP) of 0.53 as well as residual predictive deviation (RPD) of 2.97. By applying successive projection algorithm (SPA), eight wavelengths at 924, 931, 964, 1068, 1262, 1373, 1628 and 1668 nm among the 239 wavelengths were selected as informative wavelengths to reduce the information redundancy and optimise the PLS model. With the eight informative wavelengths, a simplified PLS model defined as SPA-PLS was established with r P of 0.95, RMSEP of 0.47 and RPD of 3.23. To visualise the contamination degree of salmon flesh caused by Enterobacteriaceae, the SPA-PLS model was transferred to each pixel of images, and colourful distribution maps were produced with different colour represented different numbers of Enterobacteriaceae colonies. The results showed that hyperspectral imaging operating in 900-1700 nm is promising in evaluating Enterobacteriaceae contamination of salmon products. More studies are still required to further refine the multispectral imaging system to achieve online application.

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Assessing Nitrogen Content of Golf Course Turfgrass Clippings Using Spectral Reflectance

Cited by 38 publications

References 0 publications

Utilization of color parameters to estimate moisture content and nutrient levels of peanut leaves

Utilization of color parameters to estimate moisture content and nutrient levels of peanut leaves

Fusion of Spectra and Texture Data of Hyperspectral Imaging for the Prediction of the Water-Holding Capacity of Fresh Chicken Breast Filets

Selection of Informative Spectral Wavelength for Evaluating and Visualising Enterobacteriaceae Contamination of Salmon Flesh

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