Optimal vision system design for characterization of apples using US/VIS/NIR spectroscopy data

Sharifzadeh, Sara; Clemmensen, Line Katrine Harder; Ersbøll, Bjarne Kjær; Vega, M. V. M.

doi:10.1109/iwssip.2013.6623437

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…This manuscript demonstrates the performance of all the best models that not only contain PLS, SVR, and boosting but also ElasticNet, Lasso, and Ridge [27,28] because these three methods are also used frequently. Table 6 shows that PLS, SVR, and boosting algorithms are the best because the RPD levels of ElasticNet, Lasso, and Ridge are only level B or level C. The best model for the PLS algorithm is PLS_RBF, which has LV and Gamma parameters of 27 and 0.1, respectively.…”

Section: The Best Regression Models Of Visible Near-infrared (Vis-nir)mentioning

confidence: 92%

Prediction of Soil-Available Potassium Content with Visible Near-Infrared Ray Spectroscopy of Different Pretreatment Transformations by the Boosting Algorithms

Jin

et al. 2020

Applied Sciences

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The application of visible near-infrared (VIS-NIR) analysis technology to quantify the nutrients in soil has been widely recognized. It is important to improve the performance of regression models that can predict the soil-available potassium concentration. This study collected soil samples from southern Anhui, China, and concentrated on the modelling methods by using 29 pretreatment methods. The results show that a combination of three methods, Savitzky–Golay, standard normal variate, and dislodge tendency, exhibited better stability than others because it was the most capable of achieving levels A and B of the ratio of performance of deviation. The boosting algorithms that form an ensemble of multiple weak predictors exhibited better performance than partial least square (PLS) regression and support vector regression (SVR) for the prediction of soil-available potassium. These regression models could be employed to precisely predict the soil-available potassium concentration.

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Section: The Best Regression Models Of Visible Near-infrared (Vis-nir)mentioning

confidence: 92%

Prediction of Soil-Available Potassium Content with Visible Near-Infrared Ray Spectroscopy of Different Pretreatment Transformations by the Boosting Algorithms

Jin

et al. 2020

Applied Sciences

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“…The first real data set is the spectroscopic data of an apple type called Rajka. This is the same data set used in (Sharifzadeh et al, 2013). Spectroscopic mea- surements were performed in 825 wavelengths (306 -1130 nm) and there were 185 data points (apple samples) in total.…”

Section: Prediction Of Solvable Solid Content (Ssc) Of Apple Using Spmentioning

confidence: 99%

Sparse supervised principal component analysis (SSPCA) for dimension reduction and variable selection

Sharifzadeh

Ghodsi

Clemmensen

et al. 2017

Engineering Applications of Artificial Intelligence

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Optimal vision system design for characterization of apples using US/VIS/NIR spectroscopy data

Cited by 2 publications

References 5 publications

Prediction of Soil-Available Potassium Content with Visible Near-Infrared Ray Spectroscopy of Different Pretreatment Transformations by the Boosting Algorithms

Prediction of Soil-Available Potassium Content with Visible Near-Infrared Ray Spectroscopy of Different Pretreatment Transformations by the Boosting Algorithms

Sparse supervised principal component analysis (SSPCA) for dimension reduction and variable selection

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