Stink bugs are significant pests of cotton in the southeastern USA, causing millions of dollars in control costs and crop losses each year. New methods to detect stink bug damage must be investigated in order to reduce these costs and optimize pesticide applications. One such method would be to detect the volatile organic compounds (VOCs) emitted from cotton plants damaged by stink bugs. A portable device was developed to draw VOCs from the head space of a cotton boll over carbon black-polymer composite sensors. From the response of these sensors, this device would indicate if the boll was fed upon by a stink bug or not. The device was 100% accurate in distinguishing bolls damaged by stink bugs from undamaged controls when tested under training conditions. However, the device was only 57.1% accurate in distinguishing damaged from undamaged bolls when tested 24 h after it was trained. These results indicated that this device was capable of classifying cotton as damaged or undamaged by differentiating VOCs released from undamaged or damaged bolls, but improvements in design are required to address sensitivity to fluctuations in environmental conditions.
Cotton containing transgenes from the bacterium
In order to preserve the benefits of
Because current detection methods are far less than optimal, novel sensing technologies for detecting stink bugs should be evaluated. Such methods include exploiting volatiles released from cotton damaged by these insects. Cotton plants and bolls release chemicals as part of a defensive mechanism against pests [
Because cotton releases volatiles systematically in response to piercing/sucking damage from hemipteran insects, these volatiles can potentially be detected as a characteristic of boll damage in cotton. Therefore, the detection of these volatiles can indirectly indicate the presence of these pests.
Carbon black-polymer composites have been shown to express variable resistance to electrical current in the presence of certain volatiles [
Carbon black-polymer composite sensors have a number of benefits over similar sensors. These sensors have been shown to outperform tin oxide and conducting polymer arrays in resolving analytes [
Cotton volatiles have previously been detected using a commercially available electronic nose [
This research was aimed at developing a pest and damage (PAD) detector using carbon black-polymer composites to detect cotton bolls fed upon by insects. This device would be used specifically by growers and consultants for detecting bolls damaged by stink bugs, replacing the practice of counting insects and hand opening bolls. This device would also be less expensive, readily available, and easy to interpret and provide rapid results for in-field decision making. Furthermore, because optimal carbon black-polymer composites were identified for detecting damaged cotton, our specific array of sensors will not require training, be cheaper than commercial units ($500 versus $10 K), and reduces total sampling time (10 s versus 60 s) compared with a commercial version previously tested [
Adults of
Cotton,
Carbon black-polymer composite sensors were constructed by drop coating a carbon black-polymer mixture onto a custom printed circuit board (PCB). The custom PCBs were fabricated by Pad2Pad (Mahwah, NJ, USA) and were 29 mm × 14 mm with 4 sets of 6 interdigitated electrodes (Figure
Polymers used to fabricate carbon black-polymer composite sensors. Baseline resistances were taken at 52.4% RH and 36.1°C.
Sensor number | Polymer | Baseline resistance [kΩ] |
---|---|---|
4 | Poly(styrene-co-allyl alcohol) | 170 |
8 | Poly(vinylpyrrolidone) | 77 |
9 | Poly(ethylene glycol) | 760 |
10 | Poly(ethylene oxide) | 9.0 |
Printed circuit board for carbon black-polymer composite sensors.
Components of the PAD detector (Figure
Components of the pest and damage detector.
The control circuit (Figure
Control circuit for controlling the pump and valve with the Arduino microcontroller board.
The sensor circuit (Figure
Sensor circuit for measuring voltage drop across the sensors using the Arduino microcontroller board. Resistors
In addition to the Arduino Uno
An activated charcoal filter (developed in-house) was added to the ambient air intake in order to reduce volatiles in the ambient air, and a sampler was added to the sample air intake in order to reduce volatile head space and reduce false negatives (Figure
Cages were placed over forty cotton bolls (each about 12–14 days after anthesis) by securing nylon stockings over polystyrene foam cups as described by Greene et al. [
To sample cotton volatiles, the cages were removed, and the boll was placed in the boll sampler (Figure
Field sampling cotton bolls with pest and damage (PAD) detector.
Seven analog input channels of the datalogger were used. The first four channels were used to record the voltage drop across the carbon black-polymer sensors. The fifth channel was used to monitor the status of the solenoid valve. The sixth and seventh channels were used for analog output from the temperature and humidity sensors, respectively. The voltage range for all channels was ±5 V, except a voltage range of ±1 V was used for channel six. A sampling frequency of 10 Hz was used for all channels.
The resistances of the sensors were calculated using the voltage drop across the sensor. A calibration curve was created for each sensor position using known resistors and the following equation:
The percent change in resistance was calculated by dividing the difference between the maximum and baseline resistances by the baseline resistance. The baseline resistance was defined as the sensor resistance immediately after the solenoid valve was energized, and the maximum resistance was defined as the largest resistance measured during the sampling cycle. These changes in resistance were used as features for detection. Samples from bolls which had not produced internal signs of feeding injury (i.e., warts) after 72 h and samples from bolls where the stink bugs had died during testing were discarded.
For clarity, data were graphed as a 3-period central moving average. Each datum point was graphed as the average of that datum point, the datum point immediately preceding it, and the datum point immediately after it. This allowed the trend of data to be observed more easily.
After 48 h, the mean change in resistance for each sensor was compared between treatments by Student’s
After 48 to 72 h, significance of differences between changes in resistances between treatments was determined by paired
After 48 h, there was a significant difference in percent change of resistance for undamaged and damaged bolls with sensor 8 (
Mean (±SD) maximum change in resistance for various carbon black-polymer
composite sensors in response to volatiles from undamaged and damaged bolls after
48 h using a 3, 6, and 11 s sample. Asterisks (
Sample | Sensor number | Control [%] |
Damaged [%] |
---|---|---|---|
3 s | 9 | 1.39 ± 1.36 | 0.349 ± 0.698 |
10 | 0.542 ± 0.262 | 0.778 ± 0.423 | |
4 | 1.40 ± 0.409 | 1.50 ± 0.184 | |
8 |
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6 s | 9 | 1.76 ± 1.11 | 0.349 ± 0.698 |
10 | 0.542 ± 0.262 | 0.845 ± 0.445 | |
4 | 1.45 ± 0.373 | 1.60 ± 0.107 | |
8 |
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| |
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11 s | 9 | 2.24 ± 1.58 | 0.795 ± 0.932 |
10 | 0.542 ± 0.262 | 0.845 ± 0.445 | |
4 | 1.45 ± .373 | 1.60 ± 0.107 | |
8 |
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Sensor response of sensor 8 to undamaged and damaged cotton bolls after 48 h. Data are shown as a 3-period (300 ms) central moving average.
A logistic regression using the resistance change for sensor 8 after 3 s of sampling was 77.7% accurate. However, a logistic regression using the resistance change for sensor 8 after 6 s of sampling was 100% accurate in distinguishing the sensor response from undamaged and damaged bolls. This logistic regression was 57.1% accurate in classifying undamaged and damaged bolls after 72 h.
After 72 h, no sensors showed a significant difference between treatments with a 6 s sample, using a Student’s
Mean (±SD) maximum change in resistance for various carbon black-polymer composite sensors in response to volatiles from undamaged and damaged bolls after 72 h using a 6 s sample.
Sensor number | Control [%] |
Damaged [%] |
---|---|---|
9 | 2.17 ± 2.12 | 2.40 ± 1.94 |
10 | 0.418 ± 0.503 | 0.270 ± 0.497 |
4 | 1.05 ± 0.300 | 1.30 ± 0.393 |
8 | 7.20 ± 4.32 | 9.14 ± 7.08 |
Maximum sensor response of sensor 8 at various temperatures to undamaged and damaged cotton bolls after 72 h with 6 s sample. Lines represent an exponential regression for responses to damaged and undamaged bolls.
Maximum sensor response of sensor 8 at various relative humidity to undamaged and damaged cotton bolls after 72 h with 6 s sample. Lines represent an exponential regression for responses to damaged and undamaged bolls.
Response of sensor 8 to undamaged and damaged cotton bolls after 72 h. Data are shown as a 3-period (300 ms) central moving average.
In order to account for the effects of environmental factors, data after both 48 and 72 h were examined by paired
Mean (±SD) differences in maximum change in resistance for various carbon black-polymer composite sensors in
response to volatiles from undamaged and damaged bolls after 6 s sample.
Sensor number | Difference between treatments [%] |
---|---|
9 | 0.20 ± 2.3 |
10 | −0.050 ± 0.61 |
4 | 0.20 ± 0.41 |
8 |
|
Several studies have demonstrated the potential for using electronic nose technology to monitor pest damage in crop plants. Rice plants subjected to different densities of adult brown plant hopper,
Testing after 48 h showed that sensor 8 was able to differentiate damaged bolls from undamaged bolls. A logistic regression indicated that the sensor could distinguish between undamaged and damaged bolls with 100% accuracy, which showed potential for using this sensor to detect stink bug damage. In comparison, under laboratory conditions, a commercially available, 32-sensor device (Cyranose 320), distinguished between undamaged and damaged bolls with 90% accuracy [
Testing after 72 h, the logistic regression showed 57.1% accuracy and that no sensors were able to detect damaged bolls from undamaged bolls. This was likely due to effects of environmental factors such as temperature or relative humidity. Because more samples were collected after 72 h and sampled over a longer period, samples were collected over a larger range of temperature and relative humidity. This wide range of testing conditions could have contributed to the insignificance of the differences between treatments because the maximum change in resistance for sensor 8 varied exponentially with temperature and relative humidity. This indicated that the device was likely very sensitive to environmental factors and should be trained under the same conditions as testing. Sensor drift due to variation in temperature and humidity may be controlled by periodic calibration with an internal standard based on cotton volatiles [
One of the potential benefits of using an electronic nose device would be quick decision making. The sampling time can potentially be reduced to 6 s with our prototype, compared with a sample time exceeding 20 s for manual boll examination. Using a 5 s purge cycle, this means total time from initiation to a decision could be 11 s. This would be a desirable and minimal time for evaluation of individual bolls using a nondestructive technique.
Drawbacks of this device include the necessity of training the device under the same environmental conditions as those used for testing the device. Because it has been shown that environmental factors may affect sensor response, it would be beneficial to train the device in the same environmental conditions to minimize the effects of these factors. Future studies should include data collection in a variety of environmental conditions in order to account for environmental factors such as temperature or humidity. Alternatively, these factors may be controlled by standardizing conditions within the unit.
This study demonstrated the potential of utilizing volatiles released from damaged cotton bolls to assess stink bug damage using an electronic nose device (Clemson PAD). The sensors developed for this study were highly effective in discriminating between undamaged and damaged bolls and could be incorporated into improved commercial detectors for use by pest management professionals.
The authors declare that they have no conflict of interests regarding the publication of this paper.
The author would like to acknowledge Dr. Ya-ping Sun for the use of his lab and Dr. Monica Veca for her help fabricating the sensors. Thanks are due to technical contribution no. 6296 of the Clemson University Experiment Station. The author would also like to acknowledge the support of Cotton Incorporated and the South Carolina Cotton Board. This material is based upon work supported by NIFA/USDA, under Project no. SC-1700369.