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Wheat Spike Blast Image Classification Using Deep Convolutional Neural Networks
- Source :
- Frontiers in Plant Science, Frontiers in Plant Science, Vol 12 (2021)
- Publication Year :
- 2021
- Publisher :
- Frontiers Media SA, 2021.
-
Abstract
- Wheat blast is a threat to global wheat production, and limited blast-resistant cultivars are available. The current estimations of wheat spike blast severity rely on human assessments, but this technique could have limitations. Reliable visual disease estimations paired with Red Green Blue (RGB) images of wheat spike blast can be used to train deep convolutional neural networks (CNN) for disease severity (DS) classification. Inter-rater agreement analysis was used to measure the reliability of who collected and classified data obtained under controlled conditions. We then trained CNN models to classify wheat spike blast severity. Inter-rater agreement analysis showed high accuracy and low bias before model training. Results showed that the CNN models trained provide a promising approach to classify images in the three wheat blast severity categories. However, the models trained on non-matured and matured spikes images showing the highest precision, recall, and F1 score when classifying the images. The high classification accuracy could serve as a basis to facilitate wheat spike blast phenotyping in the future.
- Subjects :
- 0106 biological sciences
0301 basic medicine
Computer science
Plant Science
01 natural sciences
Convolutional neural network
SB1-1110
03 medical and health sciences
Disease severity
convolutional neural networks
controlled conditions
Original Research
severity classification
Contextual image classification
wheat blast
business.industry
Deep learning
Plant culture
deep learning
food and beverages
Agreement analysis
plant disease phenotyping
Pattern recognition
030104 developmental biology
breeding
RGB color model
inter-rater agreement
Spike (software development)
Artificial intelligence
F1 score
business
010606 plant biology & botany
Subjects
Details
- ISSN :
- 1664462X
- Volume :
- 12
- Database :
- OpenAIRE
- Journal :
- Frontiers in Plant Science
- Accession number :
- edsair.doi.dedup.....6b1049b6909ef616006b84e456d781eb
- Full Text :
- https://doi.org/10.3389/fpls.2021.673505