Table 4 Summary of selected references applying hyperspectral imaging to seed fungus damage detection
From: Hyperspectral imaging for seed quality and safety inspection: a review
Seed | Spectral rangea | Varieties | Sample numbers | Features | Signal mode | Data analysis strategies | Main application type | Classification result (highest accuracy) | References | ||
|---|---|---|---|---|---|---|---|---|---|---|---|
Spectra/image | Extraction/selection methods | Analysis level | Classification/regression methods | ||||||||
Barley | 900–1700 (1000–1600) | 1 variety, 2 fungi | 6300 | Spectra and image | PCA | Reflectance | PWb prediction map and OWc (single kernels) | LDA, QDA, MDA | Fungus (Ochratoxin A and Penicillium) damage detection | > 82% | Senthilkumar et al. [43] |
Canola | 960–1700 (1000–1600) | 1 variety, 2 fungi, | 3300 | Spectra and image | PCA | Reflectance | OW (single kernels) | LDA, QDA, MDA | Fungus (Aspergillus glaucus and Penicillium spp.) damage detection | > 90% | Senthilkumar et al. [44] |
Corn | 900–1700 | 3 varieties, 5 treatments | 585 | Spectra | No | Reflectance | OW (single kernels), PW prediction map | PLS-DA | Fungus (Aflatoxin B1) damage detection | 96.90% | Kandpal et al. [49] |
Corn | 400–900 for fluorescence | 1 variety, 3 treatments | 492 | Spectra | No | Reflectance | PW spectra | spectral index | Fungus (Aflatoxin A. flavus) damage detection | 93% | Yao et al. [54] |
Corn | 400–701 for fluorescence, 461–877 for reflectance | 1 variety, 3 treatments | 300 | Spectra | PCA | Reflectance | OW (single kernels), PW PCA | LS-SVM, KNN | Fungus (Aflatoxin A. flavus) damage detection | > 91% (KNN) | Zhu et al. [53] |
Hick peas, green peas, lentils, pinto beans and kidney beans | 960–1700 (1000–1600) | 5 different pulses, 2 fungi | Over 10,000 kernels | Spectra and image | PCA | Reflectance | OW (single kernels), PW PCA | LDA, QDA | Fungus (Penicillium commune Thom, C. and A. flavus Link, J.) damage detection | 96%-100% | Karuppiah et al. [48] |
Maize | 850–2800 (1000–2500) | 4 varieties | 120 | Spectra | PCA | Reflectance | OW (single kernels), PW prediction map | SVM, SVR | Fungus (Aflatoxin B1) damage detection | R2 = 0.77 | Chu et al. [63] |
Maize | 1000–2500 | 1 variety, 5 treatments | 150 | Spectra | PCA, FDA | Reflectance | OW (single kernels), PW PCA | FDA | Fungus (Aflatoxin B1) damage detection | 88% | Wang et al. [58] |
Maize | 1000–2500 | 1 variety, 5 treatments | 120 | Spectra | PCA | Reflectance | OW (single kernels) | FDA | Fungus (Aflatoxin B1) damage detection | 98% | Wang et al. [41] |
Maize | 960–1662 (1000–2498) | 1 variety, 3 treatments | 36 | Spectra | No | Reflectance | OW (single kernels), PW prediction map | PLS-DA | Fungus (Fusarium) damage detection | 77% (PLS-DA) | Williams et al. [60] |
Maize | 1000–2498 | 1 variety, nine treatments | 160 | Spectra | PCA, variable importance plots | Reflectance | OW (single kernels), PW PCA and prediction map | PLSR | Fungus damage detection | R2 = 0.87 | Williams et al. [59] |
maize | 400–700 | 1 variety, 2 fungi, 3 treatments | 180 | Spectra | No | Reflectance | OW (single kernels) | discriminant analysis | Fungus (Toxigenic and atoxigenic A. flavus) damage detection | 94.40% | Yao et al. [56] |
Maize | 400–1000 | 12 varieties, 4 fungi | Unknown | Spectra | PCA | Reflectance | OW (bulk samples), PW PCA | ANOVA, Fisher’s LSD test | Fungus (Aspergillus strains) damage detection | Fisher’s LSD test | Del Fiore et al. [51] |
Oat50 | 1000–2500 | 1 variety, 4 treatments | 180 | Spectra | PLSR | Reflectance | OW (single kernels), PW prediction map | PLSR, PLS-LDA | Fungus (Fusarium) damage detection | R2 = 0.8 | Tekle et al. [61] |
Peanut | 970–2570 (1000–2000) | 1 variety, 2 treatments | 149 | Spectra | PCA | Reflectance | OW (single kernels), PW prediction map | PCA | Moldy kernel detection | 98.73% | Jiang et al. [50] |
Peanut | 967–2499 | 1 variety, 2 treatments | More than 10,000 pixels | Spectra | ANOVA, NWFE | Reflectance | OW (single kernels), PW prediction map | SVM | Fungus (Aflatoxin) damage detection | > 94% | Qiao et al. [45] |
Rice | 400–1000 | 1 variety, 6 treatments | 210 | Spectra | No | Reflectance | OW (bulk samples) | SOM, PLSR | Fungus (Aspergillus) damage detection | R2 = 0.97 | Siripatrawan and Makino [62] |
Watermelon | 948–2016 | 1 variety, 2 treatments | 96 | Spectra | Intermediate PLS (iPLS) | Reflectance | OW (single kernels) PW prediction map | PLS-DA, LS-SVM | Fungus (Cucumber green mottle mosaic virus) damage detection | 83.3% (LS-SVM) | Lee et al. [47] |
Watermelon | 400–1000 | 1 variety, 2 treatments | 336 | Spectra | Intermediate PLS (iPLS) | Reflectance | OW (single kernels), PW prediction map | PLS-DA, LS-SVM | Fungus (Acidovorax citrulli) damage detection | > 90% | Lee et al. [46] |
Wheat | 528–1785 | 4 varieties, 2 fungi | 803 | Spectra | PCA | Reflectance | OW (single kernels), PW spectra | LDA | Fungus (Fusarium) damage detection | > 91% | Barbedo et al. [55] |
Wheat | 528–1785 | 33 varieties, 3 treatments | 10,862 | Spectra | No | Reflectance | OW (single kernels), PW spectra | spectral index | Fungus (Fusarium head blight) damage detection | 81% | Barbedo et al. [52] |
Wheat | 400–1000 (450–950) | 1 variety, 3 treatments | 800 | Spectra and image | PCA, STEPDISC | Reflectance | OW (single kernels) | LDA | Fungus (Fusarium) damage detection | 92% | Shahin and Symons [42] |
Wheat | 900–1700 (1000–1600) | 1 variety, 3 fungi | 1200 | Spectra and image | STEPDISC, GLCM, GLRM, PCA | Reflectance | OW (single kernels) | LDA, QDA, MDA | Fungus (Penicillium spp., Aspergillus glaucus group, and Aspergillus niger) damage detection | > 95% | Singh et al. [64] |
Wheat | 1000–1700 (1013–1650) | 3 varieties | – | Spectra | PCA | Reflectance | OW (bulk, single kernels), PW PCA | PLS-DA, iPLS-DA | Fungus (Fusarium) damage detection | 99% | Serranti et al. [57] |