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Preliminary research on the identification system for anthracnose and powdery mildew of sandalwood leaf based on image processing
- Source :
- PLoS ONE, Vol 12, Iss 7, p e0181537 (2017), PLoS ONE
- Publication Year :
- 2017
- Publisher :
- Public Library of Science (PLoS), 2017.
-
Abstract
- This paper presents a survey on a system that uses digital image processing techniques to identify anthracnose and powdery mildew diseases of sandalwood from digital images. Our main objective is researching the most suitable identification technology for the anthracnose and powdery mildew diseases of the sandalwood leaf, which provides algorithmic support for the real-time machine judgment of the health status and disease level of sandalwood. We conducted real-time monitoring of Hainan sandalwood leaves with varying severity levels of anthracnose and powdery mildew beginning in March 2014. We used image segmentation, feature extraction and digital image classification and recognition technology to carry out a comparative experimental study for the image analysis of powdery mildew, anthracnose disease and healthy leaves in the field. Performing the actual test for a large number of diseased leaves pointed to three conclusions: (1) Distinguishing effects of BP (Back Propagation) neural network method, in all kinds of classical methods, for sandalwood leaf anthracnose and powdery mildew disease are relatively good; the size of the lesion areas were closest to the actual. (2) The differences between two diseases can be shown well by the shape feature, color feature and texture feature of the disease image. (3) Identifying and diagnosing the diseased leaves have ideal results by SVM, which is based on radial basis kernel function. The identification rate of the anthracnose and healthy leaves was 92% respectively, and that of powdery mildew was 84%. Disease identification technology lays the foundation for remote monitoring disease diagnosis, preparing for remote transmission of the disease images, which is a very good guide and reference for further research of the disease identification and diagnosis system in sandalwood and other species of trees.
- Subjects :
- 0106 biological sciences
Leaves
Support Vector Machine
Kernel Functions
lcsh:Medicine
Plant Science
Forests
01 natural sciences
Machine Learning
Digital image
Digital image processing
Image Processing, Computer-Assisted
Operator Theory
lcsh:Science
Multidisciplinary
Ecology
biology
Plant Anatomy
Plant Fungal Pathogens
food and beverages
04 agricultural and veterinary sciences
Terrestrial Environments
Horticulture
Feature (computer vision)
Physical Sciences
Powdery mildew
Research Article
Computer and Information Sciences
Neural Networks
Imaging Techniques
Feature extraction
Plant Pathogens
Image processing
Research and Analysis Methods
Ecosystems
Artificial Intelligence
Support Vector Machines
Plant Diseases
040101 forestry
Sandalwood
Ecology and Environmental Sciences
lcsh:R
Biology and Life Sciences
Image segmentation
Plant Pathology
biology.organism_classification
Powdery Mildew
Plant Leaves
Agronomy
Santalum
0401 agriculture, forestry, and fisheries
lcsh:Q
Mathematics
Neuroscience
010606 plant biology & botany
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 12
- Issue :
- 7
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....ee72d41e6a4c74521bf4bb1a46166509