Article pp.623-631 du Vol.23 n°5-6 (2003)

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ARTICLE ORIGINAL ORIGINAL PAPER

Soft X-ray image analysis to detect wheat kernels damaged by Plodia interpunctella

(Lepidoptera: Pyralidae)

C. Karunakaran¹, D. S. Jayas¹*, N. D.G. White²

SUMMARY

Canada has a legally defined zero tolerance for stored-product insects, but infested grain is found in grain handling facilities and seed warehouses. In this study, the efficiency of a soft X-ray method to detect wheat kernels damaged by larvae of the Indianmeal moth, Plodia interpunctella (Hubner) was determined. Feeding damage by larvae results in loss of germination during storage. Undamaged Canada Western Red Spring wheat kernels and those damaged by P. interpunctella larvae were X-rayed at 15 kV and 65 µA for 3 to 5 s. Normalized histogram values were determined for wheat kernels from the X-ray images and were placed into 23 groups. The 23 histogram groups and the area of wheat kernels were used to classify undamaged and damaged wheat kernels using parametric and non-parametric classifiers. There was no significant difference between the parametric and non-parametric classifiers for the identification of undamaged and damaged wheat kernels from the independent test sets. The linear function parametric classifier was selected as the best classifier and it correctly identified more than 98% of sound and 97% of wheat kernels damaged by P. interpunctella feeding.

Key words

Plodia interpunctella, X-ray imaging, grain damage, image analysis, insect infestation.

1 – INTRODUCTION

About 10 to 30% of grain produced in the world is lost every year during storage due to insect damage (WHITE, 1995). Insect infestations occur in grain

1. Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB, Canada R3T 5V6.

2. Agriculture and Agri-Food Canada, Cereal Research Centre, Winnipeg, MB, Canada R3T 2M9 .

* Corresponding author. Tel.: +1-204-474-6860; fax: +1-204-474-7568.

E-mail address: Digvir_Jayas@umanitoba.ca.

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when it is stored for long periods under conditions favourable for the growth of insects. In Canada, more than 80% of harvested grain is stored on farms (MUIR, 1997). Grain in farm bins is more prone to insect infestation than in commercial grain facilities due to its higher temperature near harvest, little physical grain movement, and prolonged storage times. Grain-feeding insect infestation, at low densities, in farm granaries in western Canada can be close to 50%

(MADRID et al., 1990). Failure to detect one infested load entering a primary elevator (grain-handling facility) has the potential to contaminate a significant por- tion of grain within the grain-handling system.

In spite of a zero tolerance for stored-product insects enforced by the Cana- dian Grain Commission, there is evidence of infestations in primary and terminal elevators (SMITH, 1985; SMITH and LOSCHIAVO, 1978). In one year, nearly one-half of the railcars loaded from primary elevators contained infested grain (SMITH, 1985).

Infestations in primary elevators may result from entry of infested grain from farms and in terminal elevators from entry of infested grain from primary elevators or pro- ducer railcars loaded directly by farmers. In 1991, during a severe infestation by Cryptolestes ferrugineus (Stephens), 91% of farmers delivered infested grain to one elevator in Saskatchewan; and that same year, there was a 200% increase in the insect-infested grain arriving at terminal elevators (SINHA and WHITE, 1991).

Detection of low levels of infestation in incoming grains at elevators and increased monitoring of grain within the elevator will reduce post-harvest losses caused by insects and also help maintain Canada’s reputation for high quality grains. Therefore, a rapid, accurate and objective test method to detect insect infestations in grain based on feeding damage is necessary. The presence of damaged kernels can indicate some live insects are present and warn of future infestation.

Among different methods that have been investigated for their potential to detect insect infestations, the soft X-ray method is an effective method to detect internal feeding weevils Sitophilus oryzae L., S. granarius L., S. zeamais Mots. in cereal grain (MILNER et al., 1950; STERMER, 1972; KEAGY and SCHATZKI, 1991, 1993). The Indianmeal moth, Plodia interpunctella (Hubner) is a common pest of stored cereals, especially in southern Ontario in farm-stored grain, grain elevators, and seed warehouses (SINHA and WATTERS, 1985). It would be benefi- cial to have tools to identify early damage of this pest before high populations are discovered by observations of webbing and flying adults.

The objectives of this study were to determine: i) the feasibility of a soft X- ray method to detect grain damage caused by P. interpunctella in Canada Western Red Spring (CWRS) wheat; and ii) to determine the classification accuracies of undamaged and damaged wheat kernels using histogram features derived from the X-ray images.

2 – MATERIALS AND METHODS

Five hundred sound and insect damaged CWRS wheat kernels were X-rayed to determine the potential of the soft X-ray method to identify infested kernels.

Wheat samples were clean and had no visible fungal or mechanical damage.

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Wheat samples were moisturized to about 15% moisture content and were infested with P. interpunctella adults. Damaged kernels fed on by larvae of P. interpunctella with germ, bran, and endosperm damage were identified with the naked eye and X-rayed.

X-ray images were acquired using a Lixi fluoroscope (Model: LX-85708, Lixi Inc., Downers Grove, IL) which produces soft X-rays and real-time images. The X-ray tube current and voltage can be adjusted in the range of 0 – 200 µA and 0 – 50 kV, respectively. Based on the trial images, 15 kV and 65 µA were determined to produce good quality X-ray images of wheat kernels. The grain kernels were placed manually on Saran Wrap (with kernel crease facing down) on the platform between the X-ray source and detection system and were X-rayed for 3 to 5 s at a time. The image formed on the detection system was captured by a black and white camera (Sony XC-75/75CE) and digitized by an image digitizer into 8-bit images at a resolution of 60 pixels/mm (Dazzle digital video creator, Dazzle Multimedia Inc., Fremont, CA).

Algorithms were developed to segregate grain kernels and the background in the X-ray images. The grey levels of an 8-bit grey image range from 0 to 255, 0 represents pure black and 255 represents pure white (figure 1a). The grey values of all digitized X-images of wheat kernels were in the range 25 to 252 (for the kernel shown in figure 1b, the range is 28 to 251). The grey value of the Saran Wrap (background) taken without the grain kernel at different times during the X-ray imaging experiment had a consistent and constant value of 252.

Therefore, kernels were segmented from the background by the simple thresh- olding method. After the segmentation process the original grey values of the grain kernels were superimposed on the thresholded images.

Figure 1

Histogram groups of a sound CWRS wheat kernel. a: grey scale; b: histogram groups.

28–251 28–65 66–96 97–127

128–158 159–189 190–220 221–251

0 50 100 150 250 255

Grey scale (a)

(b)

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The grey level intensity histogram was determined for all the segmented grain kernels (GONZALEZ and WOODS, 1998). The creases of sound kernels appeared brighter than the endosperm and were not of the same grey levels (figure 1b). Damaged portions of the kernels were brighter than the endosperm and hence it was not feasible to apply region growing or edge detection algorithms to locate the infested portions. Therefore, to detect damage caused by infestations, a normalized histogram of the intensity histogram was obtained for each kernel and was separated into 23 groups. The histogram groups are rotation-invariant and normalized histogram groups (each histogram value divided by the grain kernel area) are invariant to rotation and size of grain kernels. The first 22 groups contain the total number of pixels of the grain kernel with 10 grey level intervals beginning from grey level 251. The last group includes the total number of pixels with grey values less than 31. The normalized grey level grouping into seven groups with 30 grey level intervals of an undamaged kernel is shown in figure 1b.

The 23 histogram features and area of kernels extracted from the X-ray images were used to classify undamaged and damaged wheat kernels by the parametric and non-parametric classifiers using the DISCRIM procedure (SAS, 1990). The DISCRIM procedure develops a discrimination function from one or more quantitative variables and classifies observations into one of the groups. A parametric (normal) classifier is used if the within class distributions of the variables are multivariate normal, and a non-parametric method is used otherwise.

The DISCRIM procedure uses a data set called a ‘training set’ to derive the discriminant function and the calibration information can be stored and applied to other data sets called an independent ‘test set’. Two thirds of the sample was used for training and one third was used as the independent test set. The mean classification accuracies were determined from three trial tests by randomly selecting training and testing sets three times. The cross-validation and hold- out methods of the parametric and non-parametric classifier were used to determine the classification accuracies of undamaged and damaged kernels. In the hold-out method, the training and test data sets are independent of each other. In the cross-validation method, the training and test data sets are pooled together and n-1 out of the n observations are treated as the training set and the misclassification rate is estimated for each of the n training observations.

3 – RESULTS AND DISCUSSION

The X-ray images of wheat kernels damaged by P. interpunctella larvae are shown in figure 2. All the damaged kernels were recognized from the X-ray images due to consumption of germ by P. interpuntella larvae that appeared brighter than the germ area of the sound kernels. The feeding larvae of P. inter- puntella remain near the germ area of wheat kernels and kill the seeds in 4 d (MADRID and SINHA, 1982). They can consume about 64% of the grain kernel mass during development and they prefer bran to endosperm due to the presence of a greater amount of pyridoxine (12.6%) in the bran than in the endosperm (6%) (MADRID and SINHA, 1982). The life cycle of P. interpunctella

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was completed in 28 to 36 d when germ, endosperm, and bran were present but the time was delayed from 40 to 60 d when only germ and endosperm were present at 30 °C and 80% relative humidity (MADRID and SINHA, 1982). The larvae first consume the germ, then the bran, and finally the endosperm. They consume 75% of endosperm only after feeding on the germ and bran in 4 wk.

All kernels used in this study had the germ and portions of bran and endosperm consumed by larvae.

Figure 2

X-ray images of wheat kernels damaged by Plodia interpunctella larvae.

The 23 histogram features and area derived from 500 undamaged CWRS wheat kernels were grouped into a ‘Undamaged’ class and the 500 wheat kernels damaged by P. interpunctella into a ‘Damaged’ class for classification by the parametric and non-parametric classifiers. The grey level distribution and total grey values of undamaged and damaged kernels by P. interpunctella are shown in figures 3 and 4. The histogram group values of undamaged kernels were significantly higher up to the first five groups and significantly lower from 7^th to 23^rd group than the damaged kernels. The consumption of germ by P. interpunctella allowed better penetration of X-rays and hence the germ of damaged kernels appeared brighter than the undamaged kernels. This caused the total grey value of undamaged kernels to be significantly lower than the damaged wheat kernels due to less dense germ area of damaged kernels.

The mean classification accuracies by the cross-validation method of the parametric classifier using the linear and quadratic discriminant functions were 99.2 and 95.7% (undamaged), 97.3 and 97.0% (damaged), respectively (table 1). The cross-validation method of the non-parametric classifier correctly identified 99.5 and 94.8% of undamaged and damaged kernels by P. interpunc- tella larvae.

The hold-out method of the parametric classifier using the linear and quadratic functions identified higher percentages of sound and damaged kernels from the training sets than the independent test sets. The mean classification percentages determined by the hold-out method using the linear and quadratic functions for independent test sets were 98.7 and 93.3% (undamaged), and 96.7 and 97% (damaged), respectively (table 2). The mean classification accuracies by the hold-out method of the non-parametric classifier for independent test sets were 99.7% (undamaged) and 96.0% (damaged) and were higher than the classification accuracies for the training sets.

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Figure 3

Histogram groups of CWRS wheat kernels undamaged and damaged by Plodia interpunctella larvae. (Undamaged: 500 kernels; Damaged: 500 kernels).

Figure 4

Total grey values of CWRS wheat kernels undamaged and damaged by Plodia interpunctella larvae. (Undamaged: 500 kernels; Damaged: 500kernels).

In the cross-validation method, the non-parametric classifier and linear-function parametric classifier identified a significantly higher percentage of sound kernels than the quadratic-function parametric classifier (F = 154.5, p=

<0.0001). The linear-function and quadratic-function parametric classifiers identified a significantly higher percentage of damaged kernels by P. interpunctella than the non-parametric classifier (F = 10.79, p = 0.0103). There was no signifi-

0 2 4 6 8 10 12 14

1 3 5 7 9 11 13 15 17 19 21 23

Histogram group

Undamaged Damaged

Area (%)

Undamaged 5

4

3

2 Total gray value (x 106)

Damaged

Class

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cant difference between the parametric and non-parametric classifiers (hold-out method) for the identification of sound (F = 1.76, p = 0.2324) and damaged kernels (F = 0.13, p = 0.9415) by P. interpunctella from the independent test sets.

Hence, the linear function parametric classifier was selected as the best classifier for the identification wheat kernels damaged by P. interpunctella.

Table 1

Classification accuracies of CWRS wheat kernels undamaged and damaged by P. interpunctella using histogram features by the crossvalidation method

of the parametric and non-parametric classifiers

Table 2

Classification accuracies of CWRS wheat kernels undamaged and damaged by P. interpunctella using histogram features by the hold-out method

of the parametric and non-parametric classifiers

The canonical plot, determined using linear combinations of the variables, shows the grouping of undamaged and damaged kernels (figure 5). The complete separation of undamaged and damaged kernel classes based on the canonical discriminant function exhibit the difference in features derived from the X-ray images of undamaged and damaged wheat kernels. This resulted in correct identification of more than 98% of sound and 97% of damaged kernels by the linear function parametric classifier using histogram features for the independent test set. The identification percentage of damaged wheat kernels by P. interpunctella determined in this study was comparable to the identification percentage of damaged kernels by the rusty grain beetle, C. ferrugineus (KARU- NAKARAN et al., 2002a). Wheat kernels infested by C. ferrugineus larvae were correctly identified with more than 87% accuracy and by pupa and adults were identified with more than 96% accuracy by the non-parametric classifier from

Methods Linear-Parametric Quadratic-Parametric Non-Parametric Class to

from Undamaged Damaged Undamaged Damaged Undamaged Damaged Undamaged 99.2 ± 0.4 0.8 ± 0.7 95.7 ± 0.4 4.3 ± 0.4 99.5 ± 0.0 0.5 ± 0.0 Damaged 2.8 ± 0.7 97.3 ± 0.7 3.0 ± 0.9 97.0 ± 0.9 5.3 ± 0.7 94.8 ± 0.7

Methods Linear-Parametric Quadratic-Parametric Non-Parametric Class to

from Undamaged Damaged Undamaged Damaged Undamaged Damaged Training set

Undamaged 99.6 ± 0.1 0.4 ± 0.1 96.8 ± 0.7 3.3 ± 0.7 99.6 ± 0.1 0.4 ± 0.1 Larvae 2.1 ± 0.6 97.9 ± 0.6 2.5 ± 0.9 97.5 ± 0.9 3.8 ± 0.5 96.3 ± 0.5 Test set

Undamaged 98.7 ± 2.3 1.3 ± 2.3 93.3 ± 7.0 6.7 ± 7.0 99.7 ± 0.6 0.3 ± 0.6 Larvae 3.3 ± 2.1 96.7 ± 2.1 3.0 ± 3.0 97.0 ± 3.0 4.0 ± 3.0 96.0 ± 3.0

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the independent test sets. KARUNAKARAN et al. (2002b) reported recognition of more than 97% of kernels infested by different stages of the rice weevil, S. oryzae using features derived from the X-ray images and the recognition per- centage was higher than the 80 to 90% reported in previous studies (STERMER

1972; SCHATZKI and FINE, 1988; HAFF and SLAUGHTER, 1999). Eventual automa- tion of the soft X-ray technique for insect-feeding damage will benefit both cereal seed warehouse operators and the Canadian grain handling system.

Figure 5

Canonical plot for CWRS wheat kernels undamaged and damaged by Plodia interpunctella larvae.

ACKNOWLEDGEMENTS

We thank the Natural Sciences and Engineering Research Council of Can- ada and the Canada Research Chairs program for financially supporting this project.

Can1

Can2

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