Abstract
The timely and accurate identification of stripe rust and leaf rust is essential in effective disease control and the safe production of wheat worldwide. To investigate methods for identifying the two diseases on different wheat varieties based on image processing technology, single-leaf images of the diseases on different wheat varieties, acquired under field and laboratory environmental conditions, were processed. After image scaling, median filtering, morphological reconstruction, and lesion segmentation on the images, 140 color, texture, and shape features were extracted from the lesion images; then, feature selections were conducted using methods including ReliefF, 1R, correlation-based feature selection, and principal components analysis combined with support vector machine (SVM), back propagation neural network (BPNN), and random forest (RF), respectively. For the individual-variety disease identification SVM, BPNN, and RF models built with the optimal feature combinations, the identification accuracies of the training sets and the testing sets on the same individual varieties acquired under the same image acquisition conditions as the training sets used for modeling were 87.18–100.00%, but most of the identification accuracies of the testing sets for other individual varieties were low. For the multi-variety disease identification SVM, BPNN, and RF models built with the merged optimal feature combinations based on the multi-variety disease images acquired under field and laboratory environmental conditions, identification accuracies in the range of 82.05–100.00% were achieved on the training set, the corresponding multi-variety disease image testing set, and all the individual-variety disease image testing sets. The results indicated that the identification of images of stripe rust and leaf rust could be greatly affected by wheat varieties, but satisfactory identification performances could be achieved by building multi-variety disease identification models based on disease images from multiple varieties under different environments. This study provides an effective method for the accurate identification of stripe rust and leaf rust and could be a useful reference for the automatic identification of other plant diseases.
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Claims 5
Claim · model-written summary
Individual-variety SVM, BPNN and RF models reached 87.18–100.00% identification accuracy on training and testing sets of the same varieties and image conditions.
- Taxon
- wheat
Triticum aestivume0 - Interactor
- stripe ruste0pathogen
- Second interactor
- leaf ruste0pathogen
- Outcome
- identification accuraciese2
- Value
- 87.18–100.00%e2
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The timely and accurate identification of stripe rust and leaf rust is essential in effective disease control and the safe production of wheat worldwide”
e2“the identification accuracies of the training sets and the testing sets on the same individual varieties acquired under the same image acquisition conditions as the training sets used for modeling were 87.18–100.00%”
Claim · model-written summary
Most identification accuracies of individual-variety testing sets for other varieties were low.
- Taxon
- wheatTriticum aestivume0
- Interactor
- stripe ruste0pathogen
- Second interactor
- leaf ruste0pathogen
- Outcome
- identification accuracies of the testing sets for other individual varietiese3
- Direction
- lowe3
- Comparator
- same individual varietiese2
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The timely and accurate identification of stripe rust and leaf rust is essential in effective disease control and the safe production of wheat worldwide”
e2“on the same individual varieties”
e3“but most of the identification accuracies of the testing sets for other individual varieties were low”
Claim · model-written summary
Multi-variety models reached 82.05–100.00% identification accuracy on the training set, the multi-variety testing set and all individual-variety testing sets.
- Taxon
- wheatTriticum aestivume0
- Interactor
- stripe ruste0pathogen
- Second interactor
- leaf ruste0pathogen
- Outcome
- identification accuraciese4
- Value
- 82.05–100.00%e4
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The timely and accurate identification of stripe rust and leaf rust is essential in effective disease control and the safe production of wheat worldwide”
e4“identification accuracies in the range of 82.05–100.00% were achieved on the training set, the corresponding multi-variety disease image testing set, and all the individual-variety disease image testing sets”
Claim · model-written summary
Identification of stripe rust and leaf rust images could be greatly affected by wheat variety.
- Taxon
- wheatTriticum aestivume0
- Material
- wheat varietiese5
- Interactor
- stripe ruste5pathogen
- Outcome
- identification of images of stripe rust and leaf ruste5
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The timely and accurate identification of stripe rust and leaf rust is essential in effective disease control and the safe production of wheat worldwide”
e5“the identification of images of stripe rust and leaf rust could be greatly affected by wheat varieties”
Claim · model-written summary
Satisfactory identification performance is achievable by building multi-variety disease identification models from images of several varieties under different environments (recommended).
- Taxon
- wheatTriticum aestivume0
- Outcome
- building multi-variety disease identification modelse6
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The timely and accurate identification of stripe rust and leaf rust is essential in effective disease control and the safe production of wheat worldwide”
e6“satisfactory identification performances could be achieved by building multi-variety disease identification models based on disease images from multiple varieties under different environments”