Abstract
Stripe rust and leaf rust with similar symptoms are two important wheat diseases. In this study, to investigate a method to identify and assess the two diseases, the canopy hyperspectral data of healthy wheat, wheat in incubation period, and wheat in diseased period of the diseases were collected, respectively. After data preprocessing, three support vector machine (SVM) models for disease identification and six support vector regression (SVR) models for disease index (DI) inversion were built. The results showed that the SVM model based on wavelet packet decomposition coefficients with the overall identification accuracy of the training set equal to 99.67% and that of the testing set equal to 82.00% was better than the other two models. To improve the identification accuracy, it was suggested that a combination model could be constructed with one SVM model and two models built using K -nearest neighbors ( K NN) method. Using the DI inversion SVR models, the satisfactory results were obtained for the two diseases. The results demonstrated that identification and DI inversion of stripe rust and leaf rust can be implemented based on hyperspectral data at the canopy level.
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Claims 4
Claim · model-written summary
The wavelet packet SVM achieved 99.67% training accuracy and outperformed the other two models.
- Taxon
- Wheat
Triticum aestivumt0 - Outcome
- overall identification accuracy of the training sete0
- Direction
- bettere0
- Value
- 99.67%e0
- Comparator
- other two modelse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The results showed that the SVM model based on wavelet packet decomposition coefficients with the overall identification accuracy of the training set equal to 99.67% and that of the testing set equal to 82.00% was better than the other two models.”
t0“Wheat Stripe Rust and Wheat Leaf Rust”
Claim · model-written summary
The wavelet packet SVM achieved 82.00% testing accuracy and outperformed the other two models.
- Taxon
- WheatTriticum aestivumt0
- Outcome
- testing sete0
- Direction
- bettere0
- Value
- 82.00%e0
- Comparator
- other two modelse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The results showed that the SVM model based on wavelet packet decomposition coefficients with the overall identification accuracy of the training set equal to 99.67% and that of the testing set equal to 82.00% was better than the other two models.”
t0“Wheat Stripe Rust and Wheat Leaf Rust”
Claim · model-written summary
The authors suggest combining one SVM and two nearest-neighbor models to improve identification accuracy.
- Taxon
- WheatTriticum aestivumt0
- Outcome
- combination model could be constructed with one SVM model and two models built using K -nearest neighbors ( K NN) methode0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“To improve the identification accuracy, it was suggested that a combination model could be constructed with one SVM model and two models built using K -nearest neighbors ( K NN) method.”
t0“Wheat Stripe Rust and Wheat Leaf Rust”
Claim · model-written summary
SVR disease-index inversion gave satisfactory results for both rust diseases.
- Taxon
- WheatTriticum aestivumt0
- Outcome
- satisfactory resultse0
- Value
- two diseasese0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Using the DI inversion SVR models, the satisfactory results were obtained for the two diseases.”
t0“Wheat Stripe Rust and Wheat Leaf Rust”