Table 5.
Random Forest (RF) applications in hyperspectral image analysis of food products.
| Study | Wavelength range (nm) | Spectral pre-processing | Image processing | Random Forest characteristics |
RF Computational software | Classification accuracy | References | |
|---|---|---|---|---|---|---|---|---|
| Number of decision trees | Training set: Validation set | |||||||
| Detection of scab disease on potatoes | 900–1700 | – | Greedy Stepwise; Image thresholding: Otsu algorithm; Gaussian blurring cluster | 500 | 75:25 | WEKA | 97% | Dacal-Nieto et al. (2011) |
| Detection of bruises in apple | 400–1000 | – | Image thresholding: Otsu algorithm | 130 | 75:25 | PYTHON | 100% | Che et al. (2018) |
| Identification of rice seed cultivar | 900–1700 | – | Image thresholding | – | 75:25 | MATLAB R2009b | 100% | Kong et al. (2013) |
| Classification of degree of bruising in apples | 400–1000 | Standard normal variate (SNV), 1st Derivative, Savitzky-Golay (SG) smoothing | Image thresholding | – | 70:30 | MATLAB 9.0; PYTHON |
92% | Tan et al. (2018) |
| Determination of honey floral origin | 400–1000 | – | Image thresholding | – | 70:30 | MATLAB R2012a | 92% | Minaei et al. (2017) |
| Inspection for varietal purity of rice seed | 900–1700 | – | Image thresholding | 500 | 75:25 | MATLAB | 84% | Vu et al. (2016) |
| Identification of freezer burn on frozen salmon surface | 900–1700 | Standard normal variate (SNV) | Image thresholding | 50 | 75:25 | MATLAB R2015b | 98% | Xu et al. (2016) |
| Detection and classification of virus on tobacco leaves | 400–1000 | Standard normal variate (SNV); Successive projections algorithm (SPA) | Image thresholding | 71 | 67:33 | MATLAB | 85% | Zhu et al. (2017) |
| Discrimination of kiwifruits treated with different concentrations of forchlorfenuron | 900–1700 | Standard normal variate (SNV); Successive projections algorithm (SPA) | Image thresholding | 200 | 67:33 | MATLAB R2012a | 94% | Dong et al. (2017) |
| Detection of fungal infection in strawberry | 400–1000 | Baseline correction; Savitzky-Golay second derivate | Image thresholding | 10 | 75:25 | WEKA | 89% | Siedliska et al. (2018) |