Prediction of the colorimetric parameters and mass loss of heat‐treated bamboo: Comparison of multiple linear regression and artificial neural network method

Gürgen, Ayşenur; Topaloğlu, Elif; Ustaömer, Derya; Yıldız, Sibel; Ay, Nurgül

doi:10.1002/col.22393

Cited by 7 publications

(1 citation statement)

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“…Partial least squares regression is a data analysis method which focuses on the characteristics of principal component analysis, typical correlation analysis, and linear regression analysis methods in the modeling process. However, since PLSR can effectively use spectral information, it has been widely used in the remote sensing of vegetation and has achieved good research results [51,52].…”

Section: Regression Techniquesmentioning

confidence: 99%

Estimation of the Yield and Plant Height of Winter Wheat Using UAV-Based Hyperspectral Images

Tao

Feng

et al. 2020

Sensors

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Crop yield is related to national food security and economic performance, and it is therefore important to estimate this parameter quickly and accurately. In this work, we estimate the yield of winter wheat using the spectral indices (SIs), ground-measured plant height (H), and the plant height extracted from UAV-based hyperspectral images (HCSM) using three regression techniques, namely partial least squares regression (PLSR), an artificial neural network (ANN), and Random Forest (RF). The SIs, H, and HCSM were used as input values, and then the PLSR, ANN, and RF were trained using regression techniques. The three different regression techniques were used for modeling and verification to test the stability of the yield estimation. The results showed that: (1) HCSM is strongly correlated with H (R2 = 0.97); (2) of the regression techniques, the best yield prediction was obtained using PLSR, followed closely by ANN, while RF had the worst prediction performance; and (3) the best prediction results were obtained using PLSR and training using a combination of the SIs and HCSM as inputs (R2 = 0.77, RMSE = 648.90 kg/ha, NRMSE = 10.63%). Therefore, it can be concluded that PLSR allows the accurate estimation of crop yield from hyperspectral remote sensing data, and the combination of the SIs and HCSM allows the most accurate yield estimation. The results of this study indicate that the crop plant height extracted from UAV-based hyperspectral measurements can improve yield estimation, and that the comparative analysis of PLSR, ANN, and RF regression techniques can provide a reference for agricultural management.

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Section: Regression Techniquesmentioning

confidence: 99%