Detection of anthracnose in tea plants based on hyperspectral imaging

Computers and Electronics in Agriculture 167 (2019) 105039

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Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag

Detection of anthracnose in tea plants based on hyperspectral imaging a

b

b,⁎

c

d

d,⁎

Lin Yuan , Peng Yan , Wenyan Han , Yanbo Huang , Bin Wang , Jingcheng Zhang Haibo Zhanga, Zhiyan Baoa

,

T

a

School of Information Engineering and Art and Design, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China Crop Production Systems Research Unit, United States Department of Agriculture, Agricultural Research Service, PO Box 350, Stoneville, MS 38776 USA d School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China b c

ARTICLE INFO

ABSTRACT

Keywords: Tea plant Anthracnose Hyperspectral image ISODATA classification

Anthracnose (Gloeosporium theae-sinesis Miyake) is an important and common foliar disease in tea plants and is a severe threat to tea quality and production. Hyperspectral imaging technology enables non-invasive, objective detection of the damages ca by foliar disease and offers significant potential for plant disease prevention and phenotyping. This study proposes a novel method for detecting anthracnose in tea plants based on hyperspectral imaging. By analyzing the spectral sensitivity, we identified disease-sensitive bands at 542, 686, and 754 nm and used these bands to create two new disease indices: the Tea Anthracnose Ratio Index (TARI) and the Tea Anthracnose Normalized Index (TANI). Based on an optimized set of spectral features, a strategy combining unsupervised classification and adaptive two-dimensional thresholding was developed to detect disease scabs. Compared with traditional pixel-based classification methods, the proposed method was not affected by leaf background differences and thereby provides an effective means for disease identification and damage analysis. The validation results gave an overall accuracy of 98% for identifying the disease at the leaf level and 94% at the pixel level. These results suggest that automated and accurate detection of anthracnose-infected tea leaves is possible by using hyperspectral imaging for practical tea-plant protection.

1. Introduction As one of the main commercial crops in China, tea is an important agricultural product and occupies a pivotal position in the agricultural economy in the vast tea areas of China (Zheng and Gao, 2015). Over 130 types of diseases afflict tea plants in China resulting in a 10%–20% yield reduction and seriously degraded tea quality (Qi and Zhang, 2016). In recent years, with global climate change, tea plant diseases are becoming more prevalent, making disease prevention and control a serious challenge (Reynolds, 2010; Han et al., 2016). Because tea leaves are edible, the food safety problem caused by excessive agricultural residues has attracted extensive attention worldwide. To avoid this problem, the detection and identification of tea plant diseases are crucial for disease prevention and plant phenotyping to enhance plant disease resistance. Traditional methods to detect, identify, and quantify diseases in tea plantations rely mainly on visual inspection, manual checks, and field sampling, which are subjective and time-consuming. Methods used for laboratory analyses, such as microscopy, molecular, biochemical, and

microbiological methods (Bock et al., 2010; Martinelli et al., 2014), are destructive and inefficient for plant protection management. In recent years, hyperspectral imaging (HSI), which integrates spectral information and image information, has shown significant advantages for nondestructive testing, plant disease diagnosis, and the safety of agricultural products (Gowen et al., 2007; Zhang et al., 2014; Berdugo et al., 2014; Thomas et al., 2018). Several studies have shown that HSI technology is a valuable tool for diseases detection, identification, and quantification (Del Fiore et al., 2010; Tian and Zhang, 2012; Mahlein, 2016). Del Fiore et al. (2010) showed that hyperspectral imaging is useful for assessing mycotoxin-producing pathogens in maize. Tian and Zhang (2012) confirmed that HSI technology may be used to detect cucumber downy mildew, with the accuracy of the algorithm reaching nearly 90%; Mahlein (2016) reviewed different plant disease detection technologies that benefit precision agriculture and plant phenotyping and confirmed that HSI technology has significant potential for detecting plant diseases. Both spectral analysis (Rumpf et al., 2010; Mahlein et al., 2010; Mahlein et al., 2013; Yuan et al., 2014) and machine learning methods

⁎ Corresponding authors at: Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China (W. Han). School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China (J. Zhang). E-mail addresses: [email protected] (W. Han), [email protected] (J. Zhang).

https://doi.org/10.1016/j.compag.2019.105039 Received 19 June 2019; Received in revised form 1 October 2019; Accepted 1 October 2019 0168-1699/ © 2019 Elsevier B.V. All rights reserved.

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Fig. 1. Schematic diagram of hyperspectral imaging system.

grouping and two-dimensional thresholding to detect anthracnose; and (3) evaluate the effectiveness of the proposed method at both pixel and leaf levels.

(Bauriegel et al., 2011; Elmasry et al., 2009; Behmann et al., 2014; Xie et al., 2017) are essential to process HSI data in disease detection and are effective to extract and use the information contained in high-dimensional data. Rumpf et al. (2010) used spectral analysis of hyperspectral reflectance to detect early Cercospora leaf scab, powdery mildew, and rust-diseased sugar beets before the appearance of visible symptoms, and Mahlein et al. (2010, 2013) used spectral reflectance to differentiate between foliar pathogens of sugar beet. Yuan et al. (2014) analyzed the spectrum to differentiate between yellow rust, powdery mildew, and aphids in winter wheat and achieved an overall accuracy of 75%, which is satisfactory for a discrimination model. With machine learning methods, Bauriegel et al. (2011) used spectral angle mapping with a HSI system to detect head blight disease in wheat, and Elmasry et al. (2009) detected chilling injury in red delicious apples by using HSI and neural networks. By applying ordinal classification with support vector machines to HSI, Behmann et al. (2014) quantified and visualized the distribution of progressive stages of senescence and to separate well-watered from drought-stressed plants. Xie et al. (2017) applied K-nearest neighbor and C5.0 models to HSI to classify healthy and gray-mold-diseased tomato leaves. In addition, unsupervised machine learning algorithms, such as k-means and the Iterative Self-Organizing Data Analysis Technique (ISODATA), also play an important role in monitoring plant disease (Yang et al., 2010). These applications, which use spectral analysis and machine learning algorithms, provide the basis and experience for using HSI to monitor tea plant diseases. Anthracnose (Gloeosporium theae-sinesis Miyake) is a common foliar disease in tea plants and is found in most tea production regions in China (Wang et al., 2015). Infection by anthracnose leads to a specific disease scab on leaves and a decline in vigor of tea trees, which could severely degrade the quality and yield of tea from the growth season (Chen et al., 2012). However, at present, few studies have investigated methods to nondestructively monitor anthracnose in tea plants. It is urgent to have a rapid and effective method to replace conventional subjective and tedious methods to detect the onset of this disease. Hence, HSI was chosen to help improve disease detection for tea plant management and to support phenotyping for disease-resistant cultivars. We used the Longjing-43 tea variety as a test case for the following research objectives: (1) apply spectral analysis to extract effective spectral features and characterize the anthracnose spectrum of tea plants; (2) create an analytical framework that combines ISODATA

2. Materials and method 2.1. Experimental design and acquisition of hyperspectral data 2.1.1. Data acquisition The experiment was done in an experimental tea plantation in Wuyi County, Zhejiang province, China. The study area has a warm and humid climate and is at a relatively high altitude, making it susceptible to anthracnose infestation. In September 2017, an anthracnose outbreak struck the susceptible variety of Longjing-43 in this experimental tea plantation. Healthy leaf samples and anthracnose-infected leaf samples were collected from the experimental tea plantation, and the leaves were transported from the field to the laboratory in ice boxes for testing. An indoor HSI system was used for acquiring images for spectral tests. A pair of diseased and healthy leaves was spread on a test table for acquiring images for spectral testing, and the leaves were photographed before the test. A total of 100 tea leaves (50 healthy and 50 with anthracnose) were tested. Leaves with bruises or that received abnormal illumination were removed, leaving 78 leaves (39 healthy and 39 with anthracnose) as samples for subsequent studies. For calibration and validation, the leaves were randomly split into subgroups of 26 (13 healthy leaves and 13 leaves with anthracnose) and 52 (26 healthy leaves and 26 leaves with anthracnose). The hyperspectral images were recorded in the laboratory under the controlled illumination of two 400 W halogen lamps. This study used a Cubert UHD 185 frame hyperspectral imager (http://cubert-gmbh. com/) (Bareth et al., 2015), which was developed by the Institute of Laser Technologies in Medicine and Metrology at the University of Ulm and Cubert GmbH, Germany. Fig. 1 shows a schematic diagram of the HSI system. The image has a spatial resolution of 1000 × 1000 pixels and a spectral resolution of 4 nm with 126 equally distributed bands in the range of 450–950 nm. The hyperspectral image has a spatial resolution of 0.1 mm/pixel. The lamps and the sensor were arranged above the imaging position of a single pot. All images were calibrated by subtracting the dark frame and calculating the absolute reflectance 2

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Fig. 2. Schematic diagram of process for selecting ROI from anthracnose-infected tea leaf.

by taking the ratio of the radiance of a leaf to that of a white reference panel.

spectral ratio of healthy and diseased samples for each band (Zhang et al., 2012). The ratio of the reflectance of diseased samples in the range 450–950 nm to the average reflectance of healthy samples gives the spectral response of samples infected by the disease. The direction (increase or decrease) and amplitude of the change in reflectance in each band can be interpreted as spectral traits of anthracnose. Therefore, these traits were used to distinguish the sensitive bands in the HSI analysis. The wavebands corresponding to the peak values of the ratio curve were identified as sensitive wavebands. In addition, we applied an independent t-test to examine the statistical significance of the difference for each waveband (p < 0.001). The bands determined by the t-test analysis to be sensitive to anthracnose were then selected. Based on the selected wavebands, we developed two anthracnosespecific spectral indices [the Tea Anthracnose Ratio Index (TARI) and the Tea Anthracnose Normalized Index (TANI)] by using ratio and normalization formats as basic structures (Zhang et al., 2019). These indices were calculated as follows:

2.1.2. Selection of region of interest To investigate the characteristics of the anthracnose-infected tea leaf spectrum and determine the spectral sensitivity, the region of interest (ROI) from which the spectral information was extracted should be predefined. To facilitate spectral comparisons, the healthy leaves and diseased leaves should have the same background in hyperspectral images within the spectral range 450–950 nm. In addition, the dimensions of the ROI should be the same, which means that the ROIs must be manually extracted from the diseased scabs and the healthy areas of each diseased leaf. A healthy ROI and a diseased ROI were thus defined for each leaf sample (both measuring 3.06 mm2). The spectral reflectance of the 3.06 mm2 in each ROI was extracted, and the mean value was calculated and used to represent the sample. Fig. 2 shows a schematic diagram of the selection of diseased and healthy ROIs of anthracnose-infected tea leaves.

TARI = a

2.2. Data analysis

Ref542nm Ref686nm

a = 3.317; b =

Fig. 3 shows the framework of this study, which consists of four main steps: (1) analysis of spectral response and sensitivity, (2) construction and optimization of spectral features, (3) visualization of lesion regions using ISODATA and two-dimensional (2D) thresholding classification, and (4) accuracy assessment.

TANI = a

Ref542nm

+b

Ref754nm Ref686nm

0.161; c = Ref686nm

Ref542nm + Ref686nm

a = 10.461; b = 0.594; c =

+c (1)

3.308 b 0.431

Ref754nm

Ref686nm

Ref754nm + Ref686nm

+c (2)

Ref542nm, Ref686nm, and Ref754nm are the reflectance at 542, 686, and 754 nm, respectively. The selection of these bands is discussed in Section 3.1. The coefficients a, b, and c in the TARI and TANI were determined by fitting to a Fisher linear discriminant function based on the training dataset. In this process, the two ratios in the TARI and TANI were treated as the independent variables (x1 and x2) whereas the infection status was treated as the dependent variable (y) with healthy and diseased situations indicated by 0 and 1, respectively. The coefficients for x1 and x2 obtained from the fits and the constant coefficients were assigned to the coefficients a, b, and c, respectively, in the TARI and TANI.

2.2.1. Spectral response analysis and feature extraction We used an independent t-test and spectral ratio analysis to analyze the spectral response of tea plant infected with anthracnose. Based on the results of the t-test and spectral ratio analysis, (1) we constructed two spectral indices for anthracnose-infected tea leaves, and (2) we investigated the sensitivity of some classic spectral features to plant stress to generate an optimal spectral feature set for detecting anthracnose. 2.2.1.1. Development of spectral index for anthracnose-infected tea leaves. To develop an index for differentiating between diseased and healthy leaves, referring the strategy for developing spectral indices that raised in Zhang et al. (2019), which included the sensitivity analysis of spectral bands and construction of the indices. For the sensitivity analysis, this study used a ratio analysis, which involved the

2.2.1.2. Generation of optimal spectral feature set for disease detection. In addition to the proposed spectral indices [the TARI (1) and the TANI (2)], we included 22 classic spectral features (SFs) that are potentially sensitive to plant stress to generate an optimal SF set (Table 1). In these SFs, ten derivative and continuous removal features captured plant 3

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Fig. 3. Technical flow chart for detecting anthracnose in tea plants.

absorption features around some classic wavelength ranges (e.g., blue edge, yellow edge, red edge) of plants and were correlated with plant biophysical status and stress. In addition, 12 classic narrow-band hyperspectral vegetation indices were considered because they are associated with plant pigments, water content, plant stress, and other biochemical properties such as cellulose and lignin. The sensitivity analysis of the classic features was done by ratio analysis and t-test analysis, and we selected the features with a ratio exceeding two and that passed the significance test (p < 0.001). Moreover, together with the proposed indices, a further cross-correlation check was conducted to avoid features containing highly redundant information. Every pair of SFs was subjected to a correlation analysis; if the value of R2 between the two features exceeded 0.8, the SF with weaker sensitivity was removed. Traversing all feature pairs guaranteed that the retained feature set had no two features with a high correlation (R2 > 0.8). In addition, to guarantee the diversity of the SFs, at least one feature from each feature category was retained.

number of clusters. Similar to the k-means clustering method, ISODATA assigns pixels to a cluster based on the shortest distance to the cluster center. The algorithm achieves self-adaptive clustering through iterative merging and splitting. Here, the hyperspectral image of each sample was classified based on the selected features by using ISODATA unsupervised classification with a minimum (maximum) class number of five (ten). After training, ISODATA automatically gives the most appropriate class numbers. The unsupervised method uses the minimum spectral distance to group each pixel into a class. The process begins by extracting arbitrary class means from the image statistics given the specified class number. It repeatedly classifies and recalculates new class statistics, which are then used for the next iteration. The process continues up to 100 iterations or until the convergence threshold reaches 99%. Here, the convergence threshold indicates the fraction of invariant pixels (no change of the cluster label) in all clusters with respect to the result of the last iteration. In all classifications, the convergence threshold was attained before reaching the maximum number of iterations. Unlike some classic supervised statistical methods (e.g., discriminant analysis, regression analysis), the training process in this method does not define a specific model but just determines the optimal thresholds. The simplicity of the method may enhance its generality in disease scab detection.

2.2.2. Analysis of hyperspectral images to detect disease scab Based on the optimal spectral feature set, conventional disease detection methods use the supervised classification algorithm for identification, which requires a large number of samples with healthy and diseased scabs marked for training. However, because of the difference in leaf background in the HSI data (i.e., spectral variation due to the difference in leaf biochemical and biophysical properties between the leaves), the classification model based on a selection of leaf samples is not suitable for other leaf samples. To overcome this problem, we propose a method that combines unsupervised classification and adaptive determination of 2D thresholds, which should be insensitive to leaf background difference and thereby provide a more robust classification.

2.2.2.2. Grouping-based two-dimensional thresholding for scab detection. The reflectance of plants in the visible range is mainly dominated by pigment content, whereas the reflectance in the NIR range is primarily dominated by cellular structure and canopy morphology (Curran, 1989; Thenkabail et al., 2000). The reflectance in Red and NIR bands are informative and can be related to the biophysical status of plants. The destruction of pigments and cellular structure by pathogens can reduce chlorophyll absorption (mainly in the Red band) and multi-scattering in the NIR range. Based on this expectation, we constructed a classic two-dimensional NIR-Red space. The distribution of pixels in this feature space can help to interpret their biophysical status. For example, the healthy pixels in the low-Red and high-NIR region indicate strong pigment absorption and multiple

2.2.2.1. ISODATA classification. This study applied the ISODATA algorithm as the core clustering method for disease detection. As an unsupervised classification method, an essential advantage of the ISODATA algorithm is that it does not require knowledge of the 4

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Table 1 Summary of first-derivative spectral features, continuous removal transformed spectral features, and vegetation indices used for leaf-level disease detection of anthracnose. Spectral features

Definition

Derivative features Db Maximum value of 1st derivative within blue edge λb Wavelength at Db Dy Maximum value of 1st derivative within yellow edge λy Wavelength at Dy Dr Maximum value of 1st derivative within red edge λr Wavelength at Dr SDr Sum of 1st derivative values within red edge Continuous removal features DEP550-750 The depth of the feature minimum relative to the hull WID550-750 The full wavelength width at half DEP (nm) AREA550-750 The area of the absorption feature that is the product of DEP and WID Vegetation index TVI Triangular Vegetation Index PHRI Physiological Reflectance Index, Estimate FPAR, N-stress at crop canopy TCARI Similar to Optimized Soil-Adjusted Vegetation Index (OSAVI) RVSI Red-edge Vegetation Stress Index ARI WI NDVI WDRVI ACI CRI700 RARS RGR

Anthocyanin Reflectance Index, Estimate Anths content from reflectance changes in the green region at leaf level Water Index Normalized Difference Vegetation Index Wide Dynamic Range VI, Estimate LAI, vegetation cover, biomass; Better than NDVI Anthocyanin Content Index, Estimate Anths content from reflectance from sugar maple leaves. Carotenoid Reflectance Index, Sufficient to estimate total Cars content in plant leaves Ratio Analysis of Reflectance Spectra, Estimate carotenoid pigment contents in foliage Estimate anthocyanin content with a green and a red band

Description/formula

Literatures

Blue edge covers 490–530 nm. Db is a maximum value of 1st order derivatives within the blue edge of 35 bands λb is wavelength position at Db Yellow edge covers 550–582 nm. Dy is a maximum value of 1st order derivatives within the yellow edge of 28 bands λy is wavelength position at Dy Red edge covers 670–737 nm. Dr is a maximum value of 1st order derivatives within the red edge of 61 bands λr is wavelength position at Dr Defined by sum of 1st order derivative values of 61 bands within the red edge

Gong et al. (2002)

In the range of 550–750 nm

Pu et al. (2003)

In the range of 550–750 nm In the range of 550–750 nm

0.5 * [120 * (R750 − R550)−200 * (R670 − R550)] (R550 − R531)/(R550 + R531)

Zhao et al. (2004) Gamon et al. (1992)

3[(R700 − R670) − 0.2 * (R700 − R550)] * R700/R670

Haboudanea et al. (2004)

[(R712 + R752)/2] − R732

Merton and Huntington (1999) Gitelson et al. (2001)

ARI = (R550)

−1

−1

− (R700)

R900/R970 (RNIR − RR)/(RNIR + RR) (0.1RNIR − Rred)/(0.1RNIR + Rred)

Naidu et al. (2009) Rouse et al. (1973) Gitelson (2004)

Rgreen/RNIR

van den Berg and Perkins (2005)

CRI700 = (R510)−1 − (R700)−1

Gitelson et al. (1996)

R760/R500

Chappelle et al. (1992)

RRed/RGreen

Gamon and Surfus (1999); Sims and Gomon (2002)

scattering. Therefore, based on the results of ISODATA classification, a 2D thresholding method was applied to detect disease scabs and to convert the multiple-classification image into a two-class image. A stepwise method was applied to determine the optimal thresholds, and a hundred evenly spaced intervals were defined within the data range (i.e., from minimum to maximum) of the two bands. Based on the training data, the overall accuracy was calculated by traversing all intervals. The cutoff was defined as a range when the highest accuracy was reached. Fig. 4 illustrates the framework used in this study for data analysis. Based on the result of ISODATA classification, a threshold value was set and, instead of using pixel-based classification, we used an ISODATAgrouping-based classification. According to the threshold value, if the center point of a class falls within the threshold value, all pixels of this class are categorized as diseased samples; otherwise, the pixels are categorized as healthy samples.

discrimination results obtained by using the above method with the results of actual visual interpretation. To evaluate the classification accuracies, the overall accuracy (OA) and Kappa coefficient were calculated from the confusion matrices. All statistical analysis and modeling were done by using MATLAB software (MathWorks, Inc., Natick, Massachusetts, USA). 3. Results and discussion 3.1. Spectral response and the proposed spectral indices for anthracnoseinfected tea leaves Fig. 5a compares the averaged spectra of anthracnose-infected tea leaves with healthy tea leaves and shows that the spectrum of anthracnose-infected tea leaves increases in the red band and decreases in the green and NIR bands. The reflectance of the red band is mainly influenced by pigment (i.e., chlorophyll and carotenoid) absorption. The disease destroys the chloroplast, which increases the red band reflectance. In addition, the pathogen’s infection of leaf tissue damages the cell structure of the leaves, which decreases the NIR platform. These changes also occur for diseases such as yellow rust in wheat (Yuan et al., 2014). As opposed to the other foliar diseases (e.g., yellow rust or powdery mildew in winter wheat), the disappearance of the green peak

2.2.3. Assessment of accuracy The accuracy of the classification results was validated based on visual interpretation of leaf lesions on ROIs obtained from false-color images. Note that, for healthy leaves, given the absence of disease scabs, the entire leaf (i.e., every pixel in the leaf area) is treated as healthy. The requisite accuracy could be obtained by comparing the 5

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Fig. 4. Extraction of infected area of anthracnose based on ISODATA classification and analysis of 2D scatter plot. 0.45

9000 8000

P-value (T-test)

Reflectance

p-value 2.5

0.35

7000 6000 5000 4000 3000 average-healthy

2000

average-disease

1000 0

3

686 nm

0.4

0.3

550

650

750

850

2

0.25

1.5

0.2 0.15

542 nm

1

754 nm

0.1

0.5

0.05 0

450

ratio

950

Ratio

10000

450

550

650

750

850

950

0

Wavelength (nm)

Wavelength (nm)

(b)

(a)

Fig. 5. (a) Averaged reflectance spectra for anthracnose-infected and healthy samples. (b) Analysis of waveband sensitivity to anthracnose-infected samples (p-value of independent t-test) and spectral ratio curve (diseased/healthy).

(p < 0.001) to anthracnose, which ensures statistically significant differences between diseased samples and healthy samples. Meanwhile, the spectral ratio curves reveal a clear spectral variation from the diseased samples, with one peak (686 nm) and two valleys (542 and 754 nm). The positions of the peak and valleys indicate the most sensitive bands for discriminating between diseased and healthy samples. In addition, the results of an independent t-test in the three wavebands also confirm that the difference between diseased and healthy samples was statistically significant. Therefore, we selected the three bands above for constructing the anthracnose-sensitive spectral indices. Considering that adverse spectral variation can enhance the sensitivity of a disease index, two types of indices (i.e., TARI and TANI) were constructed following the basic ratio and normalization structures. Given that the wavebands at 542 and 686 nm were at peak and valley of the spectral ratio curves, respectively (Fig. 5b), the combination of the two components in both the TARI and TANI provide complementary information from the spectral response of anthracnose-

Table 2 The fitted coefficients of TARI and TANI under different proportions of training data. Training Proportion

100% 80% 60% 40% 20%

TARI

TANI

a

b

c

a

b

c

3.317 3.319 3.320 3.319 3.319

−0.161 −0.161 −0.161 −0.161 −0.162

−3.308 −3.308 −3.307 −3.306 −3.305

10.461 10.462 10.460 10.451 10.427

0.594 0.590 0.590 0.595 0.615

−0.431 −0.429 −0.429 −0.433 −0.445

for anthracnose-infected tea leaves may be related to the dark brown color of the entire lesions. Fig. 5b shows the spectral ratio curves (diseased/healthy) and the pvalue curve of the independent t-test between diseased and healthy samples. The p-value curve shows clearly that, in addition to 510 nm, almost all bands in the range 450–950 nm are extremely sensitive 6

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Fig. 6. Histograms of tea leaves affected by anthracnose with (a), (b) clear bimodal pattern and (c), (d) obscure bimodal pattern.

Fig. 7. Display of optimal selected feature images of anthracnose-infected leaves of tea plants.

infected tea leaves [see Eqs. (1) and (2)]. To understand the sensitivity to sample size of the coefficients a, b, and c in both indices, a test was added. A gradient of the fraction (i.e., 20%, 40%, 60%, 80%, 100%) of pixel values from the training leaf samples was used to obtain corresponding fitting coefficients (Table 2). The resulting coefficients were relatively stable for the various training fractions, which confirmed the generalizability of the coefficients. We used histograms to display the ability of the proposed spectral indices to distinguish diseased and healthy areas on the leaf. For some samples, a clear bimodal pattern appears in the histograms (Fig. 6(a and b)). For other samples, factors such as the uneven distribution of chlorophyll in the healthy parts and the spectral heterogeneity in the

diseased parts complicate the histograms (Fig. 6(c and d)) so that those leaves do not have a typical bimodal pattern in the histograms. Therefore, it is difficult to directly identify the disease pixels based on the histogram and OTSU algorithm, so we instead adopt the featureclustering method for automated recognition of disease scabs. In addition to the two proposed indices, the results of the sensitivity analysis and autocorrelation analysis (Section 2.2.1.2) of the other classic SFs suggest that Ref686 nm, Db, and RVSI should be selected for disease detection. Together with TARI and TANI, all the five SFs will be used for subsequent ISODATA classification. Fig. 7 shows images of some typical samples of the six optimal selected features.

7

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Fig. 8. Determination of optimal cutoffs in NIR and Red 2D coordinates based on stepwise thresholding method for differentiating between diseased and healthy pixels (The different colors in the space indicate different ISODATA clusters). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 9. Distribution of healthy-leaf pixels in NIR-Red 2D space (based on the optimal cutoffs of NIR < 0.6 and Red > 0.1, for all healthy leaf samples, with no class located in the region that is assumed to be diseased. The different colors in the space indicate different ISODATA clusters). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) 8

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In this study, the accuracy of the method is evaluated on both the pixel level and the foliar level. Table 3 summarizes the accuracies of the calibration and validation samples at both levels. The results show that, on the pixel scale, the accuracies for both calibration and validation samples are satisfactory, with an OA of 96% and Kappa = 0.91 for the calibration samples, and with an OA of 94% and Kappa = 0.87 for the validation samples. In addition, by taking the leaf as a whole, the accuracy of the method was investigated at the leaf level. The results show that, for the calibration samples, all samples are correctly classified with an OA of 100% and Kappa = 1 whereas, for the validation samples, only one diseased sample was misclassified as being healthy, whereas all the other samples were classified correctly, given an OA of 98% and Kappa = 0.96. To facilitate the visual evaluation of the detection results, Fig. 10 illustrates fake-color raw HSI images, the ISODATA clusters, and the binary classification images for distinguished samples. To further verify the ISODATA and 2D thresholding approach for detecting anthracnose, we applied for comparison a conventional pixelbased classification method based on the same spectral feature set and thresholds. Fig. 11 shows the classification results based on these two methods. The results show that the ISODATA-group-based classification results adopted herein are clearly superior to the pixel-based classification results, which use only two-band thresholds. Although image clustering with clear boundaries is obtained when using only the ISODATA classification method, it is also important to determine which classes are diseased areas and which are healthy areas by using the appropriate thresholds in the two-dimensional NIR-Red space. Instead of setting thresholds for pixels, the proposed methods set thresholds for the centroid of each cluster, which thus has a relatively high tolerance for errors in scab detection. The traditional process for recognizing leaf disease requires identification of the ROIs of the healthy and diseased leaves and has to be

Table 3 Recognition accuracies of anthracnose-infected tea leaves at leaf and pixel scales.

Pixel level Leaf level

OA Kappa OA Kappa

Calibration

Validation

96% 0.91 100% 1

94% 0.87 98% 0.96

Note: OA and Kappa on the pixel scale are averages of the classification accuracies obtained by comparing the visual interpretation results with the model classification results of each diseased leaf.

3.2. Grouping-based ISODATA and two-dimensional thresholding for scab detection Based on the five optimal SF images, we used the unsupervised classification ISODATA method to generate the classification image. Next, a stepwise thresholding method was applied in NIR-Red 2D space to determine the optimal cutoffs for differentiating between diseased and healthy pixels. The results give NIR < 0.6 and Red > 0.1. Figs. 8 and 9 show the pixel distributions of five diseased samples and five healthy samples in NIR-Red 2D space by ISODATA classification. As shown in Fig. 8, the diseased samples are accurately classified according to the optimal thresholds. In addition, the results show that numerous pixels are diffused from the center and distributed in a fanshaped two-dimensional scatter pattern. Note that, across different leaves, the pixels in the area of the diseased scab tend to be located at the bottom-right of the fan area near the x axis. The distribution and the characteristics of the 2D scatter plot can be used to determine the cluster of the diseased pixels. For healthy samples, following the same procedure, no sample is classified as diseased (Fig. 9).

Fig. 10. Comparisons of HSI images, the ISODATA clusters, and the binary classification images for distinguished samples. 9

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4. Conclusion By using foliar hyperspectral images of anthracnose-infected tea leaves, we demonstrated the feasibility of using a comprehensive disease-scab-detecting strategy to identify the disease. The results lead to the following main conclusions: 1. Anthracnose-infected tea leaves produce a characteristic and specific reflectance spectrum that differs from the spectrum of healthy tea leaves. The spectral response was closely associated with changes in the foliar physiological status produced by the disease. Based on a spectral ratio analysis and an independent t-test, we identified three original bands (542, 686, and 754 nm) as being suitable for disease discrimination, and we used these bands to construct two disease indices: the Tea Anthracnose Ratio Index (TARI) and the Tea Anthracnose Normalized Index (TANI). 2. In addition to establishing the three bands sensitive to anthracnoseinfected tea leaves, the two proposed disease indices, and 22 spectral features (SFs), we present an autocorrelation analysis. Given the selection criteria, we constructed an optimized spectral feature set that includes the Ref686 nm, Db, RVSI, TARI, and TANI. 3. Based on the selected SFs, we developed an analytical framework for disease-scab detection that combines unsupervised classification and adaptive two-dimensional threshold determination. A comparison with direct pixel-based classification results suggests that the proposed method is insensitive to differences in leaf background and can effectively identify diseased tea leaves and analyze the degree of infection. The accuracies for both calibration and validation samples were satisfactory, with an OA of 96% and Kappa = 0.91 at the pixel level. Therefore, the proposed HSI method enables automated and accurate detection of tea plant anthracnose. Declaration of Competing Interest Fig. 11. ISODATA-group-based classification compared with pixel-based classification for disease detection.

The authors declared that there is no conflict of interest. Acknowledgements

combined with a machine learning algorithm. Due to the complexity of these models and the difference in background, such models are often unstable for different leaves (Behmann et al., 2014). Conversely, the proposed method makes full use of the abundant spectral and imaging information contained in HSI data. First, based on spectral information, the characteristics of tea plant anthracnose are extracted. Next, we combine image analysis and unsupervised machine learning methods to build an adaptive algorithm. Only feature selection and 2D spatial classification threshold need be determined by training, and no complex training modeling is required. In addition, we combine pixel clustering with 2D spatial classification to reduce the sensitivity of classification results to the threshold, thereby ensuring more robust disease identification. In general, the method proposed herein may be extended to automated identification and diagnosis of plant leaf damage, disease, and pest stress with clear spectral-response characteristics. At the same time, given the simultaneous occurrence of disease and pests, the identification of different types of disease and pests may be required in the future, which would test the specificity of the monitoring method. In addition, canopy scales could be considered, and/or the proposed bands could even be customized for the camera. Given the continuous increase of the spatial resolution of airborne and space-borne remotesensing sensors, the proposed plant disease detection technology could potentially be used for high-throughput disease monitoring and mapping using UAV-mounted hyperspectral or multispectral sensors or high-resolution satellite images.

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