# Prediction of Maize Seed Vigor Based on First-Order Difference Characteristics of Hyperspectral Data

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

_{2}O

_{2}- and O

_{2}-based detection method [8], seed respiration detection method [9], and conductivity measurement [10,11]. However, these methods were mainly based on single physiological and biochemical characteristics of seeds, and do not fully reflect the comprehensive changes and influence of the relationships related to seed vigor.

## 2. Materials and Methods

#### 2.1. Sample Preparation and Data Collection

#### 2.2. Region of Interest Extraction

**I**after black and white correction was calculated [31]:

#### 2.3. Data Analysis and Processing Methods

**y**

_{i}(1 ≤ i ≤ n). When the sequence number i changes from k to k + 1, the amount of change of

**y**is defined as:

**y**

_{k}=

**y**

_{k}

_{+ 1}−

**y**

_{k}

**y**

_{k}is the first-order difference of the spectral data at point

**k**, which is defined by the difference between the spectral values at time k + 1 and time k.

#### 2.4. Seed Vigor Prediction

#### 2.5. Model Evaluation Index

^{2}), root mean square error (RMSE), and relative prediction deviation (RPD) were applied to test the performance of the proposed model. Assuming that the true value of the root length variable is

**y**

_{1},

**y**

_{2}, …,

**y**

_{n}, the R

^{2}, RMSE, and RPD can be calculated as follows:

**ŷ**is the predicted value of the samples by the regression model, $\overline{y}$ represents the mean of the actual measured value for the test sample, and n represents the number of test samples. In Formula (3), R

^{2}indicates the correlation between the predicted variable and the actual value. The closer to 1 R

^{2}is, the stronger is the ability for prediction of the model. RMSE is used to measure the quality of model fitting. The smaller the value of RMSE, the closer is the predicted value to the true value and the higher is the prediction accuracy. In Formula (3), SD represents the standard deviation of the test sample.

#### 2.6. Algorithmic Steps

**X**

_{1},…,

**X**

_{n}collected from the spectrum acquisition system, the general procedures of the model regression and root prediction were applied as follows.

**X**

_{k}=

**X**

_{k + 1}−

**X**

_{k}, and the new data matrix was constructed by all the first-order difference data and the raw hyperspectral data;

## 3. Results and Analysis

#### 3.1. Hyperspectral Features of Corn Seeds Based on First-Order Difference

**X**(

**x**

_{1},

**x**

_{2},…,

**x**

_{D})∈$\mathbb{R}$

^{N×D}, where

**x**

_{1}is the spectral reflectance of the first band, N is the number of samples, and D Is the number of spectral bands. Figure 5a shows the spectral reflectance and average spectral reflectance of the Zhengdan 958 for the training data in different spectral bands.

**x**, the first-order difference is specifically defined as follows:

**x**

^{k}= {∆

**x**

^{k}

_{1}, ∆

**x**

^{k}

_{2}, …, ∆

**x**

^{k}

_{i}, …, ∆

**x**

^{k}

_{255}}

**x**

^{k}(i) =

**x**

^{k}(i + 1) −

**x**

^{k}(i), (I = 1, 2, …, 256), and ∆

**x**

_{k}(i) is the first-order difference of the spectral data for the k-th corn seed in the i-th hyperspectral band. By the first-order difference of the spectral reflectance in the adjacent wavebands, the dynamic changes and correlation characteristics of the spectral reflectance for the corn seeds under different wavebands were reflected.

#### 3.2. Prediction of Root Length of Maize Seedlings Based on Regression Models

#### 3.3. Quantitative Prediction of Seedling Root Length

^{2}and RMSE. The performance of different data processing methods, such as first-order difference, curve fitting, MSC, SGS, and SNV were also compared to test and verify the efficiency of the proposed method. The prediction results obtained by different process data and regression models are compared in Table 1. It can be seen from the table that the prediction performance based on SVR and PCR models was the best. Especially, considering the spectral data and its first-order difference, the prediction result of seedling root length based on the SVR model showed the best performance, with the regression determination coefficient reaching 0.8319.

^{1}, the number of points is 7, the polynomial order is 3, the derivative order is 1 (in SGS pretreatment

^{2}), the number of points is 3, the polynomial order is 2, and the derivative order is 1.

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 5.**Spectral reflectance of seeds of Zhengdan 958. (

**a**) The original data and the data preprocessed by (

**b**) Savitzky-Golay Smoothing, (

**c**) multiplication scatter correction, and (

**d**) standard normal variate.

**Figure 6.**The correlation between the hyperspectral data/first-order difference and the seedling root length.

Regression Algorithm | PLS | SVR | PCR | |||
---|---|---|---|---|---|---|

Metrics | R^{2} | RMSE | R^{2} | RMSE | R^{2} | RMSE |

Raw data | 0.7945 | 1.3861 | 0.7273 | 2.2403 | 0.4011 | 2.2611 |

Raw data + First difference (FOD) | 0.7493 | 2.8348 | 0.8319 | 1.8479 | 0.8023 | 1.5404 |

First difference (FOD) | 0.7235 | 1.8914 | 0.6814 | 2.2378 | 0.7995 | 1.5506 |

Curve fitting fit | 0.6732 | 1.8199 | 0.1799 | 2.2238 | 0.3666 | 2.1851 |

MSC pretreatment | 0.5451 | 3.4245 | 0.0249 | 2.2653 | 0.3528 | 2.2711 |

SGS pretreatment^{1} | 0.2533 | 8.1531 | 0.5246 | 5.49 | 0.5531 | 7.3434 |

SGS pretreatment^{2} | 0.4825 | 2.0629 | 0.7390 | 2.2252 | 0.7391 | 1.5214 |

SNV pretreatment | 0.5459 | 3.0622 | 0.2040 | 2.2770 | 0.3458 | 2.2807 |

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**MDPI and ACS Style**

Cui, H.; Bing, Y.; Zhang, X.; Wang, Z.; Li, L.; Miao, A.
Prediction of Maize Seed Vigor Based on First-Order Difference Characteristics of Hyperspectral Data. *Agronomy* **2022**, *12*, 1899.
https://doi.org/10.3390/agronomy12081899

**AMA Style**

Cui H, Bing Y, Zhang X, Wang Z, Li L, Miao A.
Prediction of Maize Seed Vigor Based on First-Order Difference Characteristics of Hyperspectral Data. *Agronomy*. 2022; 12(8):1899.
https://doi.org/10.3390/agronomy12081899

**Chicago/Turabian Style**

Cui, Huawei, Yang Bing, Xiaodi Zhang, Zilin Wang, Longwei Li, and Aimin Miao.
2022. "Prediction of Maize Seed Vigor Based on First-Order Difference Characteristics of Hyperspectral Data" *Agronomy* 12, no. 8: 1899.
https://doi.org/10.3390/agronomy12081899