Image-based field monitoring of Cercospora leaf spot in sugar beet by robust template matching and pattern recognition
Introduction
Sugar beets (cv. Beta vulgaris), which account for 20% of the world’s sugar production (FAO, 2009), are large-rooted plants with high content of sucrose for sugar extraction. However, they are vulnerable to many pathogens which negatively affect sugar production. Among those, Cercospora leaf spot (CLS) is one of the most destructive foliar diseases in sugar beets with high incidences affecting over one-third of sugar beet cultivation area worldwide (Holtschulte et al., 2000) and approaching 40% sugar losses (Robert and Extension plant pathologist, 2013). Therefore, fungicide spraying is necessary and the most important tool for disease control and plant protection (Ioannidis and Karaoglanidis, 2010). Usually, profitable and optimal decisions for fungicide spraying are made in terms of disease severity and local weather conditions (Jones and Windels, 1991), by which both economic losses and chemical usage can be reduced. Therefore, disease severity assessment is a crucial premise to facilitate optimal disease management.
The traditional way of CLS damage assessment is manual field monitoring, which is a process in which specialists first need to walk into the fields at certain intervals and assess the disease severity by naked eye observation based on a certain rating system, such as a single leaf scale based (Jones and Windels, 1991) or whole plant scale based (Vereijssen et al., 2003). However, this hand-operated field monitoring has limitations of being laborious, discontinuous, somewhat subjective with large-scale fields, and imprecise with subtle disease variations.
In recent years, image sensing techniques have captured many researches’ attention and been broadly introduced into plant disease study for their benefits in automatic, low-cost, noninvasive disease analysis capabilities. And a number of prior researches have demonstrated their inspiring ideas and results in foliar disease detection and measurements using both color images (visible spectrum) and spectral imaging. Sankaran et al. (2010) provides a comprehensive overview for currently used technologies that can assist in monitoring health and diseases in plants under field conditions, in which both visible and spectroscopic imaging techniques can be accessible. Polder et al. (2014) designs a mobile platform consisting of two RGB–NIR multispectral cameras, a color camera, a black and white camera and two infrared LED floodlights to automatically detect diseased tulip plants infected by tulip breaking virus (TBV) in open fields. Diseased plants were detected using Fisher’s linear discriminant classification with four features derived from four bands (R, G, B and NIR) of the multispectral images. Camargo and Smith (2009) develop a colored image based method that can identify the visual symptoms from plant disease regions with a large range of intensity distributions by analyzing the gradient change between the neighborhoods of each pixel. Huang (2007) employs an artificial neural network along with color and texture features for detecting and classifying three foliar diseases in Phalaenopsis seedlings based on color images. Mahlein et al., 2012, Rumpf et al., 2010 introduce hyperspectral imaging to detect and differentiate foliar diseases of sugar beets in the pixel level by analyzing the reflectance properties of the plant and diseases. Oberti et al. (2014) investigates the sensitivity of disease detection from different view angles of the canopy by multispectral image analysis and suggests a range of view angles from 40° to 60° to improve early detection of disease symptoms. However, most of the researches in this field have previously focused on detecting diseases under controlled environments, static images with zoom-in or small patches of leaves, and without considering soil background, which limit their further applications for long-term, consistent, and comprehensive disease observation under real field conditions.
Alternatively, this study focuses on monitoring CLS development from plant-level RGB image sequences under real field conditions. In our previous work (Zhou et al., 2014, Zhou et al., 2013a, Zhou et al., 2013b), we proposed a robust template matching based image algorithm, which achieved continuous small-scale CLS observation under various illumination changes. However, the challenging issue of sandy soil in real field was not addressed. In this study, we aim to solve the soil problem and also extend the small-scale based CLS observation into single leaf scale based, which, in addition to achieving motivations for complying with one of the manual CLS assessment criteria by specialists, such as a rating system used in Minnesota and North Dakota (Jones and Windels, 1991, Windels et al., 1998), also avoids potentially inconsistent disease observations due to mutual overlap of cluster leafage. For achieving field monitoring for a single leaf based CLS development, the algorithm should meet two fundamental capabilities. First is continuity, which can align identical leaves through neighboring frames for continuous leaf tracking. Second is discriminability, which can further classify CLS against the field background. However, challenges in the complex and unstructured field conditions will disturb the robustness of image algorithms. Firstly, changeable illumination can lead the intensities of plant images captured under natural sunlight conditions to vary over time. Secondly, constant changes in movements with translation, slight rotation, and occlusion of non-rigid plant, caused by both internal circadian growth and external environment interactions, are difficult for identical leaf alignment. Thirdly, cluttered field scenes with the key problem of sandy soil resembling the reddish brown color of CLS will increase the difficulties for CLS classification and detection. Meanwhile, highlighted leaf stalks and specular reflection which share the light-brown color with the inner of spots are also parts of the problem.
In this study, we propose an algorithm that consists mainly of a two-stage framework and post-processing to realize the research goal of the continuity and discriminability for a single leaf based CLS monitoring as well as attack the challenges in real field conditions. The first stage employs a robust template matching called orientation code matching (OCM) (Ullah et al., 2001) for identical leaf alignment from adjacent time-series frames against various illuminations and plant movements. Then, an extended adaptive OCM is used to achieve the continuity for the leaf tracking. The second stage uses a pattern recognition method of support vector machine (SVM) for further classifying CLS against the field background. We propose a novel three feature combination of L∗, a∗, Entropy × Density as inputs for SVM, which has strong CLS discrimination power from the clutter field background with sandy soil, leaves, leaf stalks and specular reflection. Finally, an edge detection based post-processing is conducted to filter false positive noise aroused from the proposed feature to improve the precision of classification results. A schematic diagram of the proposed algorithm is shown in Fig. 1. In summary, this study provides a visible image sensing technique for automatic and effective CLS progress monitoring in a single leaf manner under challenging real field conditions. Additionally, to the best of our knowledge, this is the first study to achieve continuous foliar disease monitoring on a single leaf scale from plant-level image sequences.
Section snippets
Field treatment
The field experiment was conducted in a test field with planted area of 12.5 × 6.6 m2 located at the Central Agricultural Experiment Station in Naganuma, Hokkaido, Japan. Sugar beet plants (cv. Amaibuki, 2004; Nippon Beet Sugar Manufacturing Co., Ltd., Japan) were initially seeded on March 18th, 2013, and then transplanted into the field on May 9, 2013. Irrigation was applied only during the seedling period and after transplanting, the plants were under rain-fed conditions. Fertilizer was applied
Results and discussion
This section shows experimental results by the proposed algorithm for a single leaf based continuous CLS observation under real field conditions. The performances of the proposed method for CLS classification are quantitatively evaluated. Furthermore, comparative experiments were conducted to demonstrate the effectiveness of the proposed method in both matching and classification aspects.
Conclusions
In this paper, we proposed a novel foliar disease monitoring algorithm for continuous observation of the disease development under real field conditions. There are a number of innovations and contributions in this work. First, we introduced a robust template matching method of OCM and extended it as adaptive OCM for robust foliage tracking against various field conditions and plant movements. We applied this method on a single leaf scale for disease observation, by which stable tracking results
Acknowledgements
We would like to thank Seth Richter at Dept. of Electrical and Computer Engineering, Utah State University for improving the language of the manuscript. We would also like to thank Prof. Takayuki Tanaka at Graduate School of Information Science and Technology, Hokkaido University for helpful comments. This work is supported in part by the China Scholarship Council (CSC) to Rong Zhou under the Grant No. 2011635044, which we gratefully acknowledge.
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