# Hyperspectral Technique for Detection of Peanut Leaf Spot Disease Based on Improved PCA Loading

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Overview of Experimental Site

^{2}. Before planting, a compound fertilizer of 81 kg ha

^{−1}containing N-P

_{2}O

_{5}-K

_{2}O was applied, and local agronomic measures were used to control weeds, pests, and other diseases. Natural peanut leaf spot disease appeared in the experimental field, with symptoms not apparent in the early growth stage. Symptoms of leaf spot disease appeared in some field areas after 60 days of seedling emergence, with varying degrees of severity appearing in mid-August. Consequently, peanut plants of different severity were tested on 15 August, 19 August, and 23 August.

#### 2.2. Data Collection

#### 2.2.1. Disease Severity Assessment

#### 2.2.2. Spectral Reflectance Collection

#### 2.3. Feature Wavelength Selection

#### 2.3.1. Principal Component Analysis Loading

- (1)
- Decentralize all samples in $D$:$${x}^{\left(i\right)}={x}^{\left(i\right)}-\frac{1}{m}{\sum}_{j=1}^{m}{x}^{\left(i\right)}$$
- (2)
- Calculate the covariance matrix (or correlation matrix) ${\mathrm{XX}}^{\mathrm{T}}$ of $D$ based on the initial variable characteristics.
- (3)
- Find the eigenvalues of ${\mathrm{XX}}^{\mathrm{T}}$ with their corresponding standard eigenvectors.
- (4)
- Take out the eigenvectors corresponding to the largest $k$ eigenvalues $\left({w}_{1},{w}_{2},\dots ,{w}_{k}\right)$, normalize them, and form the eigenvector matrix $W$.
- (5)
- Transform each sample ${x}^{\left(i\right)}$ in $D$ into a new sample:$${z}^{\left(i\right)}={W}^{T}{x}^{\left(i\right)}$$
- (6)
- Obtain the reduced-dimensional data set ${D}^{\prime}$:$${D}^{\prime}=\left[{z}^{\left(1\right)},{z}^{\left(2\right)},\dots {z}^{\left(m\right)}\right]$$

#### 2.3.2. Improved PCA Loading Method

#### 2.3.3. Correlation Optimization Feature Wavelength

Algorithm 1. The feature wavelength is optimized by correlation |

Input: $D=n\times m,C=\left\{{c}_{1},{c}_{2},\cdots ,{c}_{k}\right\},W=\left\{{w}_{1},{w}_{2},\cdots ,{w}_{n}\right\}$ |

Output: $F$ |

1: function WAVELENGTHOPTIMIZATION $\left(D,C,W\right)$ |

2: $F\leftarrow 0$ |

3: for i := 1 to k − 1 do |

4: $for\text{}j:=i+1\mathrm{to}kdo$ |

5: $if\mathrm{PEANSON}\text{}\left({c}_{i},{c}_{j}\right)0.8then$ |

6: $ifW\text{}\left({c}_{i}\right)\mathrm{W}\text{}\left({c}_{j}\right)then$ |

7: $F\leftarrow {c}_{j}$ |

8: else |

9: $F\leftarrow {c}_{i}$ |

10: end if |

11: end if |

12: end for |

13: end for |

14: return F |

15: end function |

#### 2.4. Classification Methods

#### 2.4.1. K-Nearest Neighbor

#### 2.4.2. Support Vector Machine

#### 2.4.3. Backward Propagation Neural Network

## 3. Evaluation Indicators

#### 3.1. Evaluation Indicators of Variability

#### 3.2. Evaluation Indicators of Separability

## 4. Results

#### 4.1. Spectral Features of Leaf Spot Disease

#### 4.2. Result of Feature Wavelength Selection

#### 4.2.1. Comparison with Traditional PCA Loading

#### 4.2.2. Comparison with Other Feature Wavelength Selection Methods

## 5. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 3.**Average reflectance and sensitivity for different leaf spot disease levels. (

**a**) Average reflectance, (

**b**) sensitivity.

**Figure 4.**Scatter plot and statistical histogram of combinations between the first five principal components. PC: principal component.

**Figure 5.**Loading coefficient curves of each principal component and the selected feature wavelengths. (

**a**) PC1, (

**b**) PC2, (

**c**) PC3.

**Figure 6.**Loading coefficient curves of the improved PCA loading method and the selected feature wavelengths.

**Figure 8.**Classification results of the feature wavelengths selected by PCA loading and I-PCA loading. (

**a**) and (

**b**) are OA and Kappa for KNNs, (

**c**) and (

**d**) are OA and Kappa for SVMs, and (

**e**) and (

**f**) are OA and Kappa for BP.

Level | Disease Severity | Area Ratio |
---|---|---|

A | Asymptomatic | $0$ |

I | Initially symptomatic | $0\u20130.1$ |

M | Moderately symptomatic | $0.1\u20130.25$ |

S | Severely symptomatic | $0.25\u20130.5$ |

Predicted Class | Actual Class | |||||
---|---|---|---|---|---|---|

A | I | M | S | Totals | UA | |

A | ${X}_{11}$ | ${X}_{12}$ | ${X}_{13}$ | ${X}_{14}$ | ${\displaystyle \sum}_{i=1}^{4}}{X}_{1i$ | $\frac{{X}_{11}}{{\sum}_{i=1}^{4}{X}_{1i}}$ |

I | ${X}_{21}$ | ${X}_{22}$ | ${X}_{23}$ | ${X}_{24}$ | ${\displaystyle \sum}_{i=1}^{4}}{X}_{2i$ | $\frac{{X}_{22}}{{\sum}_{i=1}^{4}{X}_{2i}}$ |

M | ${X}_{31}$ | ${X}_{32}$ | ${X}_{33}$ | ${X}_{34}$ | ${\displaystyle \sum}_{i=1}^{4}}{X}_{3i$ | $\frac{{X}_{33}}{{\sum}_{i=1}^{4}{X}_{3i}}$ |

S | ${X}_{41}$ | ${X}_{42}$ | ${X}_{43}$ | ${X}_{44}$ | ${\displaystyle \sum}_{i=1}^{4}}{X}_{4i$ | $\frac{{X}_{44}}{{\sum}_{i=1}^{4}{X}_{4i}}$ |

Totals | ${\displaystyle \sum}_{i=1}^{4}}{X}_{i1$ | ${\displaystyle \sum}_{i=1}^{4}}{X}_{i2$ | ${\displaystyle \sum}_{i=1}^{4}}{X}_{i3$ | ${\displaystyle \sum}_{i=1}^{4}}{X}_{i4$ | $-$ | $-$ |

PA | $\frac{{X}_{11}}{{\sum}_{i=1}^{4}{X}_{i1}}$ | $\frac{{X}_{22}}{{\sum}_{i=1}^{4}{X}_{i2}}$ | $\frac{{X}_{33}}{{\sum}_{i=1}^{4}{X}_{i3}}$ | $\frac{{X}_{44}}{{\sum}_{i=1}^{4}{X}_{i4}}$ | $-$ | $-$ |

**Table 3.**Eigenvalues, contribution rates, and cumulative contribution rates of each principal component.

PCs | Eigenvalues | Contribution Rate (%) | Cumulative Contribution Rate (%) |
---|---|---|---|

PC1 | 339.39 | 56.47 | 56.47 |

PC2 | 158.17 | 26.32 | 82.79 |

PC3 | 43.13 | 7.178 | 89.97 |

PC4 | 26.60 | 4.43 | 94.39 |

PC5 | 9.29 | 1.55 | 95.94 |

Methods | Number of Wavelengths | Feature Wavelengths (nm) |
---|---|---|

CARS | 10 | 404, 411, 435, 504, 534, 584, 667, 884, 989, 996 |

LSMI | 3 | 519, 667, 850 |

RF | 6 | 465, 536, 626, 701, 932, 997 |

Relief-F | 7 | 412, 421, 458, 540, 660, 760, 996 |

SPA | 3 | 547, 696, 958 |

UVE | 9 | 405, 412, 489, 534, 595, 682, 882, 988, 999 |

PCA Loading | 6 | 626, 672, 679, 706, 757, 880 |

I-PCA Loading | 3 | 570, 671, 750 |

Methods | KNN | SVM | BP | |||
---|---|---|---|---|---|---|

OA (%) | Kappa (%) | OA (%) | Kappa (%) | OA (%) | Kappa (%) | |

CARS | 90.34 | 86.92 | 95.33 | 93.71 | 95.37 | 93.78 |

LSMI | 90.65 | 87.35 | 93.77 | 91.60 | 92.73 | 90.19 |

RF | 88.16 | 83.98 | 94.08 | 92.01 | 94.03 | 91.96 |

Relief-F | 95.02 | 93.28 | 97.51 | 96.65 | 96.73 | 95.60 |

SPA | 87.85 | 83.57 | 92.52 | 89.95 | 91.48 | 88.55 |

UVE | 90.97 | 87.78 | 96.26 | 94.97 | 95.42 | 93.83 |

PCA Loading | 94.70 | 92.85 | 96.57 | 95.39 | 96.98 | 95.93 |

I-PCA Loading | 95.33 | 93.70 | 96.88 | 95.81 | 95.73 | 94.26 |

Predicted Class | Actual Class | |||||
---|---|---|---|---|---|---|

A | I | M | S | Totals | UA (%) | |

A | 74 | 1 | 0 | 0 | 75 | 98.67% |

I | 4 | 99 | 2 | 0 | 105 | 94.29% |

M | 0 | 1 | 71 | 2 | 74 | 95.95% |

S | 0 | 0 | 0 | 67 | 67 | 100.00% |

Totals | 78 | 101 | 73 | 69 | 321 | $-$ |

PA (%) | 94.87% | 98.02% | 97.26% | 97.10% | $-$ | OA = 96.88% |

HM (%) | 96.77% | 96.15% | 96.60% | 98.55% | $-$ | Kappa = 95.81% |

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## Share and Cite

**MDPI and ACS Style**

Guan, Q.; Zhao, D.; Feng, S.; Xu, T.; Wang, H.; Song, K.
Hyperspectral Technique for Detection of Peanut Leaf Spot Disease Based on Improved PCA Loading. *Agronomy* **2023**, *13*, 1153.
https://doi.org/10.3390/agronomy13041153

**AMA Style**

Guan Q, Zhao D, Feng S, Xu T, Wang H, Song K.
Hyperspectral Technique for Detection of Peanut Leaf Spot Disease Based on Improved PCA Loading. *Agronomy*. 2023; 13(4):1153.
https://doi.org/10.3390/agronomy13041153

**Chicago/Turabian Style**

Guan, Qiang, Dongxue Zhao, Shuai Feng, Tongyu Xu, Haoriqin Wang, and Kai Song.
2023. "Hyperspectral Technique for Detection of Peanut Leaf Spot Disease Based on Improved PCA Loading" *Agronomy* 13, no. 4: 1153.
https://doi.org/10.3390/agronomy13041153