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Acta Agronomica Sinica ›› 2023, Vol. 49 ›› Issue (8): 2275-2287.doi: 10.3724/SP.J.1006.2023.21060

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• TILLAGE & CULTIVATION · PHYSIOLOGY & BIOCHEMISTRY • Previous Articles     Next Articles

Hyperspectral remote sensing detection of Fusarium head blight in wheat based on the stacked sparse auto-encoder algorithm

LIN Fen-Fang1,2,3(), CHEN Xing-Yu1, ZHOU Wei-Xun1, WANG Qian2, ZHANG Dong-Yan2,*()   

  1. 1 School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China
    2 National Engineering Research Center for Agro-Ecological Big Data Analysis and Application (Anhui University), Hefei 230601, Anhui, China
    3 Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Kaifeng 475004, Henan, China
  • Received:2022-09-07 Accepted:2023-02-10 Online:2023-08-12 Published:2023-02-28
  • Contact: ZHANG Dong-Yan E-mail:linfenfang@126.com;zhangdy@ahu.edu.cn
  • Supported by:
    This study was supported by the National Natural Science Foundation of China(42271364);Science and Technology Plan in Jiangsu Province(BK20211287);Open Fund of Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University)the Ministry of Education(GTYR202104)

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Abstract:

Fusarium head blight (FHB) has the characteristics of rapid onset and short cycle. The deep learning feature extraction method was used to establish a disease severity detection model to provide guidance for the prevention and control of FHB. The hyperspectral data of wheat ears from flowering to maturity under three varieties from 2018 to 2020 were collected. The spectral curves of wheat ears were obtained by morphological processing and multi-source scattering correction. Then spectral features of FHB were extracted by stacked sparse auto-encoder (SSAE), combined with Softmax classifier and the partial least squares regression method to detect FHB. Through pre-training, the two-layer SSAE model with 12-6 neurons performed better, the mean square error of the model was lower, and the characteristics of each disease level were significantly different. The deep learning features extracted by the trained SSAE model were the basis of the establishment of FHB disease severity level discrimination model and severity prediction model. The overall accuracy and Kappa coefficient of the model were 88.2% and 0.84, respectively, and the accuracy was the highest for the variety of ‘Huaimai 35’. The prediction coefficient of determination (R2) and root mean square error (RMSE) of the model for the test set of all varieties were 0.927 and 0.062 in the severity prediction model, respectively, and R2 for each variety was around 0.95. The FHB prediction model based on SSAE deep learning features has higher accuracy than those with several common FHB spectral indices. Hyperspectral remote sensing had the characteristics of large amount of data and many spectral bands. The stack sparse auto-encoder builded a more complex model by adding the limiting conditions of sparse representation to the auto-encoder model, and increasing the number of hidden layers and hidden neurons. The extracted spectral features can better reflect the spectral characteristics of FHB in all aspects, so the detection model of FHB constructed by using these features has higher accuracy, which provides a reference for timely and accurate monitoring of FHB.

Key words: Fusarium head blight, stacked sparse auto-encoder, hyperspectral, detection, wheat

Fig. 1

Schematic diagram of the methodology adopted in the study"

Fig. 2

Morphological treatment of wheat ears (a): the original image; (b): the image after morphological processing; (c): binary image."

Fig. 3

Dr component of wheat ears in YDbDr space (a) and OTSU threshold segmentation results of wheat ears (b)"

Table 1

Severity grade of FHB"

级别
Grade		 病害严重度DI值范围
Range of DI		 病害等级
Grade of disease
1 0<DI<0.05 健康 Healthy
2 0.05≤DI<0.20 轻度 Slight
3 0.20≤DI<0.50 中度 Moderate
4 DI≥0.50 重度 Serious

Fig. 4

Structure of stack sparse auto-encoder (SAE) The first box means the data input layer, the second box for the first layer SAE, the third box for the second layer SAE, the fourth box for the output layer, and the fifth box for the connected classifiers or regression methods in the figure."

Fig. 5

Reflectance spectral curves of wheat ears with different varieties and disease levels Different colors represent disease levels, among which, black for healthy, red for slight, blue for moderate, and green for serious."

Fig. 6

Mean square error of the model with different combinations of neurons at each layer"

Fig. 7

SAE features of all disease grades at each layer under different neurons (a): the first layer; (b): the second layer. Different types of lines represent disease levels, double dotted lines for healthy, dashed lines for slight, straight lines for moderate, and dotted lines for serious."

Fig. 8

Overall accuracy and kappa coefficient of the discrimination model"

Fig. 9

Producer accuracy (a) and User accuracy (b) for each disease level under different varieties"

Fig. 10

Accuracy of the prediction models (a): all varieties test set; (b): Xinong 979 test set; (c): Huaimai 35 test set; (d): Luomai 10 test set."

Table 2

Spectral disease indices of FHB and their calculation formulas"

光谱指数
Spectral index		 光谱指数全称
Full name of spectral index		 计算公式
Formula of calculation
REHBI Red-edge head blight index $\frac{\left( 842-665 \right)\times \left( {{R}_{\text{Re3}}}-{{R}_{\text{R}}} \right)-\left( 783-665 \right)\times \left( {{R}_{\text{NIR}}}-{{R}_{\text{R}}} \right)}{\text{2}}$
FDI Fusarium disease index $\frac{{{R}_{560}}-{{R}_{663}}}{{{R}_{560}}+{{R}_{663}}}$
WSI Wheat scab index $\frac{\text{S}{{\text{D}}_{450-488}}-\text{S}{{\text{D}}_{500-540}}}{\text{S}{{\text{D}}_{450-488}}+{{\operatorname{SD}}_{500-540}}}$
FCI Fusarium classification index $\frac{2\left( {{R}_{668}}-{{R}_{471}} \right)-{{R}_{539}}}{4}$
HBI Head blight index ${{R}_{550-560}}-{{R}_{665-675}}$

Fig. 11

The comparison of prediction accuracy of models constructed by deep learning features and various spectral indices (a): SSAE; (b): WSI; (c): FCI; (d): FDI; (e): REHBI; (f): HBI."

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