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.
Highlights
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
Digital image processing technology has been widely used in the field of agriculture [1,2], and can closely monitor the diseases that affect plant growth [3], identify and diagnose plant leaf diseases, capture the core content for remote exploration of multi-spectrum and high
After performed statistical analysis all the data and image processing, we obtained a better result in disease leaf image segmentation by the BP neural network
Summary
Digital image processing technology has been widely used in the field of agriculture [1,2], and can closely monitor the diseases that affect plant growth [3], identify and diagnose plant leaf diseases, capture the core content for remote exploration of multi-spectrum and high-. Using the BP neural network for image segmentation, recognition, recovery, matching and classification analysis We applied this technology to the forest models research area. Sasaki et al [11] researched the image automatic diagnosis technology for anthracnose in cucumbers in 1999, and developed the relevant model for analysis of cucumber disease images in 2003, Sammany et al [12] integrated the neural network and SVM technology to improve the accuracy of image recognition in 2006. Powdery mildew caused by pathogens causing, specificity parasitic on the surface of plants and produce pathogenic fungi with white powder disease symptoms They belong to ascomycotina pyrenomycetes Erysiphales erysiphaceae, having higher parasitic specificity. In 2014–2016, we observed the characteristics of anthracnose and powdery mildew disease for sandalwood in the Hainan provincial state-owned ‘Daodong’ forest farm. That has no specific permissions were required for the location, and confirm that the field studies did not involve endangered or protected species
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