Drying Technology, 24: 879–888, 2006 Copyright # 2006 Taylor & Francis Group, LLC ISSN: 0737-3937 print/1532-2300 online DOI: 10.1080/07373930600734067
Computer-Vision System for Control of Drying Processes A. I. Martynenko School of Engineering, University of Guelph, Guelph, ON, Canada
Computer-vision system (CVS) for control of a drying process with a portable CCD camera with IEEE-1396 interface and configurable software LabView 7.0 and IMAQTM 6.1 was developed. An object area was continuously monitored through the CVS by extracting the green plane from the RGB color space followed by thresholding and pixel counting. An object color was continuously monitored through the CVS as color intensity in the huesaturation-intensity (HSI) color space. The observability of a drying process was provided due to online image analysis and correlation of image attributes (area, color, texture) with physical parameters of drying (moisture, quality). A relationship between area shrinkage and moisture content was used for online estimation of actual moisture content. A relationship between color intensity and quality was used for online estimation of quality degradation. Experimental study of the CVS for ginseng drying showed advantages of computer-vision for online monitoring of important state variables, such as moisture content and material quality. Color measurements demonstrated high sensitivity of quality to drying conditions: drying at 50C resulted in significant color changes and unacceptable quality degradation. The quality of roots in three-stage (38-50-38C) drying process was compatible with recommended isothermal (38C) drying due to significant (30–40%) reduction of drying time. This control strategy was used in a pilot batch dryer for temperature control with respect to quality. Testing of a pilot dryer with embedded CVS proved stability and robustness of control strategy, combined with high accuracy in the estimation of moisture content (8–14% of error with 95% confidence). The composite moisture measurements at the endpoint demonstrated uniform drying of root mixture to target moisture content 0.1 g/g (db) with minor variations between individual roots in the range of 0.07–0.12 g/g. Keywords Color; Ginseng; Machine-vision; Moisture; Quality; Shrinkage
INTRODUCTION The use of computer-vision for industrial control keeps on growing.[1,2] Recent advances in software development essentially extended the area of computer-vision applications for fruit grading,[3] cereal grain classification,[4–7] apple slices dehydration,[8] and food inspection.[9–11] Computer-vision offers a tremendous resolution for the Correspondence: A. Martynenko, School of Engineering, University of Guelph, Guelph, ON, Canada N1G 2W1; E-mail:
[email protected]
quantifying of morphological,[4,12] color,[13,14] and textural[6] features of agricultural and food materials. Selection of the minimal set of non-correlated features, sufficient for discrimination of object attributes in an informational space, was recognized as one of the most important issues in image analysis.[7,15] Usually it requires careful image pre-processing: segmentation, pixels clustering, optimal thresholding, and advanced data analysis.[3,16] To improve a computer vision systems a variety of learning techniques were developed.[17] An application of computer vision for apple slices dehydration has been recently reported.[8] Experiments showed significant changes in the shape, color and texture of apple slices, produced by drying. However, the gap between image attributes and physical parameters of drying (moisture, quality) essentially limits the use of computer vision for control of drying processes. The objective of this study was to develop a CVS for automated control of a drying process with practical application for ginseng root drying. Ginseng root is a good example of temperature-sensitive biomaterial,[18] which requires careful thermal processing. The best temperature for ginseng root has been reported as 38C;[19,20] however, drying at this temperature to target moisture 0.1 g=g (db) takes about two weeks. Increasing the temperature to 45–50C essentially accelerates drying; however this results in undesirable browning.[19] To avoid quality degradation Li[21] proposed a three-stage temperature control 38-5038C; changing air temperature according to the critical root moisture content. As highlighted by Davidson et al.,[22] a lot of complex mechanisms are involved in quality changes, which make it difficult to control. A ginseng quality is specified as a desirable color, texture, and moisture content.[23] All of these variables are measurable with computer-vision. It follows that appropriate control of ginseng drying can be developed on the basis of computer vision and online image processing. To achieve the objective, the procedures of image segmentation, feature extraction, and data analysis for ginseng roots were developed. Relationships between image attributes and physical parameters of drying (moisture and quality) were established.
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MATERIALS AND METHODS Computer-Vision System The hardware consisted of a portable compact CCD Fire-i camera (Unibrain Inc.) with a built-in 4.65 mm lens with anti-reflective coating connected to a personal computer (P4, 2.4GGz) using PCI IEEE-1394 FireWire adapter (Unibrain Inc.). The software consisted of NI-IMAQ data acquisition driver for IEEE-1394, LabVIEW 7.0 and IMAQ6.1TM Vision Builder (National Instruments). Digital camera and data acquisition interface were configured with a measurement and automation explorer NI-MAX (National Instruments). The camera was mounted on a vertical stand, which provided easy vertical movement and stable support. The depth of field was enough to obtain quality images with a high contrast of boundaries and high color resolution. The image resolution was 0.1 mm=pixel and 0.08 mm=pixel in the horizontal and vertical directions, respectively. Twenty-four-bit RGB-images were converted to square pixels with the resolution 0.01 mm2=pixel. Uniform illumination was provided with SYLVANIA CF15EL=830 diffuse fluorescent bulbs with a corrected color temperature of 4200 K and a color reproduction index about 95%. Image capturing, processing, and subsequent analysis were performed online using a LabVIEW graphical interface. Image Analysis An image analysis included image segmentation, features extraction, and data analysis. Image segmentation was designed to separate region of interest (ROI) from background. Extraction of morphological, color, and textural features was provided every hour with the library of virtual instruments, embedded in NI-IMAQ6.1. Image features, determined as time-dependent variables, were used further in data analysis to calculate physical (moisture and quality) and rate (drying rate, quality degradation) parameters of drying. Image Segmentation The first step of the image analysis was image segmentation. This algorithm, based on edge detection, operated by finding the optimal threshold in RGB color space that maximizes the difference between images. The output [0,256] gray image was then converted into binary [0,1] image with 1 assigned to the object and 0 assigned to the background. This binary image was used for two purposes: (a) estimation of morphological features (area) and (b) masking original color image for extraction of color and textural features. Multiplication of the original image on its binary mask enabled a conversion of all background pixels to zero intensity pixels and elimination this class from next calculations.
FIG. 1. Image segmentation.
The block-scheme of image segmentation procedure is shown in Fig. 1. An original color image was filtered twice: in RGB color space to extract morphological features and in HSI (hue-saturation-intensity) color space to extract color features.
Feature Extraction Morphological Features. The software library of NIIMAQ 6.1 enabled the quantification of surface area, length, width, radius, shape factor, and their statistical (mean and variance) characteristics. Length-to-width ratio was used for the identification of object orientation in the XOY plane. To distinguish the object area from isolated small clusters of pixels, the procedure of multithreshold filtering with next particle analysis was applied. Object area was determined as the largest object on the binary image. Overall surface area was obtained by the conversion of ‘‘1’’ pixels of binary image into area through the conversion coefficient of 0.01 mm2=pixel with the next multiplication by p for cylindrical geometry. Color Features. Color features were extracted as means and variances of red (R), green (G), and blue (B) channels in RGB color space and color intensity (I) in HSI color space. To avoid effects of size sampling on color intensity distribution, the number of pixels for each intensity line was normalized with respect to the overall number of pixels in the extracted area. The histogram of color intensity was treated as a fuzzy variable with lightness as a support. Average color intensity was calculated from intensity histogram on the basis of center-of-gravity defuzzification.[24] Means and variances were used to test statistical hypothesis (F-test) of color changes on each interval of observation. The flow chart of features extraction is shown in Fig. 2.
Drying Chamber Drying of ginseng roots was carried out in a specially designed drying chamber (Fig. 3). For automated monitoring
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FIG. 3. Experimental drying chamber with computer-vision system.
thermocouple measurements was 0.1C and the relative error between measurements was 0.05C. Air relative humidity was measured using a humidity sensor HIH3602C (Honeywell Inc.) with a sensitivity of 25 mV=% RH and an accuracy of 2%. Air flow rate was measured with an anemometer ALNOR-6350 (Cole-Palmer) with an accuracy of 5%. Root weight was measured continuously with a digital balance HF-8000 (A&D Engineering) with a serial interface to the control computer. Data from the thermocouples, humidity sensors and the digital balance were recorded continuously by National Instruments Lab VIEW 7.0 through a data acquisition interface card.
FIG. 2.
Flow chart of feature extraction.
of visual color and area changes during drying the chamber was made from a 190-cm-long Plexiglas tube with an inside diameter of 40 cm. It was connected to an air conditioning unit that produced constant airflow with regulated temperature and humidity. Temperatures of air and root were measured with identical T-type thermocouples with a spherical junction 0.85 mm in diameter. Measurements with the thermocouples were made using an interface card NI PCI-6220 (National Instruments) with built in cold-junction compensation in the control computer. Based on the calibration, absolute error associated with the
Samples The ginseng roots were obtained from Hare Farms (Waterford, Ontario) in October 2004. Both harvests were taken from four-year ginseng plots. Ginseng population contained roots of different shapes and sizes in the range from 4 to 40 mm in diameter. Fresh ginseng roots were stored in a refrigerator at 5 2C during the experimental study period (3 months). Prior to each drying experiment, roots were washed and care was taken to drain the roots and remove surface water. Data Analysis Data analysis was provided to relate the set of morphological, color, and textural features with the set of physical parameters of drying (moisture, quality). Moisture content
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was calculated from continuous weighing on a dry matter basis (g=g): X¼
mðtÞ mdm mdm
ð1Þ
The dry matter weight (mdm) was determined after each experiment based on 24-hour oven drying at 102C. Average moisture values were estimated at hourly intervals. An exponential equation was used to describe moisture changes over time: X ðtÞ ¼ Xe þ ðX0 Xe Þekt
ð2Þ
or in dimensionless form: wðtÞ ¼
X ðtÞ Xe ¼ ekm t X0 Xe
ð3Þ
Equilibrium moisture content (Xe, g=g db) was determined from ginseng sorption isotherms.[21] The effect of root maturity on equilibrium moisture content was neglected. The initial moisture content (X0) was verified based on the mass measurements at the end of the experiment and analysis of dry matter weight. In each experiment, the final target for moisture content was 0.1 g=g db. The accuracy of the computer vision system was evaluated as errors in area, color and texture estimation. The error in area estimation due to isolated or small clusters of pixels, mainly located at the boundaries of adjacent regions was evaluated by comparison with binary images, obtained with reference high-resolution (1392 1040) CCD camera DFW-SX900 (Sony Corp., Japan) with an automatic adjustment of white balance and 25-mm F=1.4 Mega Pixel Iris (model 23FM25SP, Tamron) lens. The error in color estimation was evaluated by comparing standard color indices from bright yellowish to beige, corresponding to the color of the ginseng roots. These indices were calculated in XYZ color space, provided by a standard Minolta colorimeter (CR-300, Japan). To avoid possible effects of non-uniform lighting, each sample was imaged three times for different angle orientation of roots (0, 120, 240) in the plane of measurement. The color was calculated as the average of three measurements. The error in texture estimation was evaluated by using a set of samples with calibrated grids of different sizes. Performance in the estimation of periodical components was estimated as a signal-to-noise ratio in power spectrum density function, calculated with FFT. All experiments were carried out with three replications in a random order to exclude the influence of uncontrolled changes including ageing and moisture loss. The correlations between morphological, color and textural features were tested with cross-correlation analysis (SAS 9.1). The significance of features and their interactions was tested by standard ANOVA procedures. Adequacy of linear relationships between image attributes and physical parameters
was tested on the basis of Fisher criteria with 0.95 confidence level. The average moisture content and color of ginseng roots in each batch were determined on the basis of composite sample measurements.
RESULTS AND DISCUSSION Image Segmentation The first step in image analysis was image segmentation. The original color images of the ginseng batch at the beginning (a) and at the end of drying (b) are shown in Figs. 4a and b. Images represent the random mixture of ginseng roots without a contrast background, so it makes difficult to segment underlying layers by simple filtering.[8] Hence, the special algorithm of image segmentation, relevant to physical shrinkage of ginseng roots, was developed. Previous experiments showed that the area shrinkage of fully-exposed ginseng roots at the end of drying is about 0.5 0.5 of the initial area.[25] This knowledge was used as the reference point to normalize area ratio in the range [1,0] with 1 assigned to the initial state (fresh roots) and 0 to the final state (dried roots). To quantify the shrinkage-relevant difference between Figs. 4a and b, both images were decomposed on red, green, and blue planes in RGB color space. Histograms of color distribution in R, G, and B for ginseng roots showed that all red pixels were concentrated in the region of high color intensities with saturation at 255, blue pixels in the range of low color intensities from 140 to 0 (interfering with background pixels), and green pixels were distributed over the entire intensity scale with maximum in the range 160–200.[25] Mean values of R, G, and B color distributions show some difference caused by drying (Table 1). It follows that drying caused the shift of red, green, and blue color intensities to lower numbers. This shift of average color intensity can be used as the measure of specific color sensitivity. The differences in red and blue color intensities were not statistically significant. The green plane appeared to be the most sensitive channel, providing the best discrimination between fresh and dry ginseng roots images. The most significant changes in green occurred in the range of [150 . . . 210]. Hence the median value 180 was chosen as the optimal threshold for conversion of a grey image into binary image. Binary images (Figs. 4c and d) were filtered from original color images in the green plane with the next thresholding at 180. Corrected binary images were used as the mask for color intensity images. Morphological Features (Shrinkage) Structural changes in ginseng roots during drying were accompanied by volumetric shrinkage and a decrease in the projected area. The surface area of roots was calculated
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FIG. 5. Kinetics of area shrinkage in drying process (circles, points of observation; solid line, exponential fit). FIG. 4. Segmentation of original images of fresh (a) and dry (b) ginseng roots with thresholding and conversion to binary images (c, d).
from binary image (Figs. 4c and d). The ratio between initial area A0 (Fig. 4c) and endpoint area Ae (Fig. 4d) in the batch corresponded to real physical shrinkage of individual roots (0.55–0.45). Area shrinkage n(t) was calculated as a time-dependent dimensionless variable, changing from 1 (initial state) to 0 (dried state): ni ¼
Ai Ae A0 Ae
ð4Þ
The example of area shrinkage of ginseng roots during 100 h of drying at temperature 38C, relative humidity 12% and air flow rate 1 m=s is presented in Fig. 5. Kinetics of shrinkage followed exponential behavior in all experiments over the range of experimental conditions from 38C to 50C, from 12 to 25% relative humidity, and air velocity from 1 m=s to 3 m=s. In time domain it could be expressed as an exponential model: nðtÞ ¼ expkS t
(Eq. (3)). The coefficient of determination (0.995) of exponential fit reflected good accuracy of image analysis for surface area estimation. The exponential behavior of area shrinkage is going along with the results presented by Fernandez et al.[8] The relationship between dimensionless values of shrinkage and moisture content is shown in Fig. 6. It was linear in the range from 0.9 to 0.1 of moisture content: w ¼ an þ b
ð6Þ
with a ¼ 1.0124, b ¼ 0.0755, standard error 0.016, and R2 ¼ 0.99. The linear relationship between shrinkage and moisture ratio in this range could be related to the phenomenon of free water evaporation.[26] A small deviation from linearity at the beginning of drying (1–0.9) can be explained
ð5Þ
with ks ¼ 0.042 h1, which corresponds to the drying rate constant km calculated from continuous weighing TABLE 1 Averagea color intensity of fresh and dry roots in RGB color space Red (R) Green (G) Blue (B)
Fresh
Dry
Difference
p-Value
250 2.5 185 15 50.5 8.8
249 12.5 151 17.5 44 10.4
1 34 6.5
0.7645 0.0189 0.4141
a
Mean values and standard deviations (95% confidence) were calculated from four identical drying experiments (38C temperature, 1 m=s air velocity, and 12% relative humidity).
FIG. 6. Relationship between area shrinkage and moisture ratio (triangles, 38C; circles, 50C; line, regression).
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by the effect of quick collapse of porous cellular structure. Deviation from linearity at the end of drying (below 0.1) can be related to critical water content 0.25–0.3 g=g (db). Below this critical point the area shrinkage is not correlated with moisture losses. Color Features (Color Intensity) Color intensity was extracted as a mean value of histogram of color intensity distribution in HSI color space. Kinetics of color changes during drying at different air temperatures are presented in Fig. 7. It follows that color degradation was proportional to drying time and air temperature. The rate of color degradation was 0.056 h1 at the temperature 38C, and 0.29 h1 at the temperature 50C. The increase of rate with temperature can be explained with Arrhenius-type temperature dependence of non-enzymatic browning.[27] From drying experiments at the recommended temperature 38C it was concluded that the acceptable level of ginseng browning is above 158 of color intensity. However, 40 h drying at 50C resulted in unacceptable browning of ginseng roots (color intensity 154). Three-stage drying (38-50-38C) provided color intensity at the end of drying at the threshold value 158. It seems that three-stage drying can be a good alternative of isothermal drying, providing acceptable material quality, which is compatible with isothermal 38C drying. Color changes measured by image analysis and standard colorimeter gave high average correlation with R2 ¼ 0.95. These results were similar to those, reported for chromatic parameters ‘‘a’’ and ‘‘b’’ in lab color space, reported by Krokida et al.[28] and Fernandez et al.[8] It follows that color intensity of root surface, measured with CVS, can
FIG. 7. Color changes of ginseng roots at different drying scenarios: (a) isothermal drying at 38C (diamonds); (b) isothermal drying at 50C (rectangles); (c) non-isothermal drying 38-50-38C (triangles). Air velocity 1 m=s, relative humidity 12%.
be used as a process variable for monitoring of quality degradation in ginseng drying process. EVALUATION OF THE CVS PERFORMANCE FOR FEEDBACK CONTROL Performance of the CVS for control of drying processes was tested in industrial conditions on pilot batch dryer. Since the bulk average moisture content cannot be measured directly, it was estimated using CVS observer. Estimated moisture content was used as a global feedback parameter for the identification of the drying stage and adjustment of the drying conditions according to the specified control strategy. Subsequently, the control system consisted of three modules: CVS observer, estimator, and controller (Fig. 8). CVS observer identified area shrinkage n(t) and color C(t) of ginseng roots in the batch as a time-dependent process variable. The estimator was a real-time module, which used the information from the CVS observer about shrinkage at a particular temperature T to estimate the rate constant ks,T by fitting to the exponential model (Eq. (5)). It was running in a regime of continuous loop execution in the range i ¼ 3, . . . , 1, delivering a dynamic set of coefficients {n0, ks, ne} of shrinkage model. From inputs ks,T, Xe, and Xc1,2,3 the estimator calculated current bulk average moisture content Xi (Eq. (2)) and time estimate test to the next critical control point Xc: test ¼
1 Xi Xe ln ks;T Xc Xe
ð7Þ
The estimate of moisture and time {Xi, test} were used as an input to the digital controller to adjust the drying temperature according to moisture content.A three-stage strategy of ginseng drying[21] entailed online identification of three critical control points: bulk average moisture content Xc1 ¼ 1.0 g=g (db) to change the drying temperature from 38 to 50C; bulk average moisture content Xc2 ¼ 0.25 g=g db to turn back from 50 to 38C; and bulk average moisture content Xc3 ¼ 0.1 g=g db to stop drying. Observer and controller were developed as reconfigurable LabVIEW
FIG. 8. The structure of computer vision control system for ginseng drying: n(t), area shrinkage; C0, Ci, initial and current color; X0, Xe, Xc, Xi, initial, equilibrium, critical, and current moisture content, respectively.
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applications with an extensive set of optimized functions for image processing, blob analysis, spatial measurements, calibration, and advanced logic control of digital I=O devices. The performance of the CVS for online identification of the pair {Xi, test} was evaluated on the basis of batch drying experiments, which included isothermal drying experiments (38C) and three-stage (38-50-38C) drying experiments with three different root distributions. The performance evaluation included the estimation of the shrinkage-moisture relational model for moisture and time prediction. Online identification of the critical control points was provided by three alternative ways: (a) CVS estimation of area shrinkage and shrinkage=moisture relationship (Eq. (6)); (b) the exponential model (Eq. (2)); and (c) experimental measurements. The CVS estimation of shrinkage was used for the exponential approximation of shrinkage rate constant ks,38 (Eq. (5)) and calculation of moisture content (Eq. (6)). Equation (7) was employed to predict the drying time to reach critical control points. Hence, performance of CVS for accurate estimation of critical control points (t1, t2, and t3) was tested with respect to both predicted and experimental data. Testing of Accuracy in Moisture Estimation The shrinkage=moisture relational model (Eq. (6)) was used to predict moisture content in the critical control points with the bulk moisture content Xc1 ¼ 1.0 g=g, Xc2 ¼ 0.25 g=g, Xc3 ¼ 0.1 g=g (db). The real values of bulk average moisture content were determined from balance readings in three-stage batch drying experiments (Fig. 9). Prediction error was calculated as a sum of squared errors (SSE) with respect to each control point. It followed that moisture content in the critical control point could be predicted with a standard error of 4–6%. Results of calculation
FIG. 9. Estimation of moisture in critical control points with CVS for nine three-stage batch drying experiments.
TABLE 2 Accuracy of moisture prediction in critical control points Critical moisture content (g=g) Xc1 ¼ 1.0 Xc2 ¼ 0.25 Xc3 ¼ 0.1
Mean
SSE
RMSE
0.94 0.24 0.1
0.0613 0.0114 0.0054
0.062 0.021 0.014
are presented in Table 2. The predictive model had a tendency to underestimate the value of moisture content. The linear relationship between shrinkage and moisture rates (Eq. (6)) was evaluated in a series of three-stage (38-50-38C) batch drying experiments. Exponential fitting of shrinkage kinetics at correspondent stages of drying approximated the rate constants ks. A comparison of ks from shrinkage measurements versus km from weight measurements for two temperatures (38C and 50C) is shown in Fig. 10. It follows that the estimate of ks-value from shrinkage analysis was close to the experimental km-value only for 38C (coefficient of determination R2 ¼ 0.93 and RMSE ¼ 0.0008 h1). However, for high temperature (50C) there was no linear relationship between the estimated ks-values and experimental km. At 50C the drying rate was slower than shrinkage kinetics. It can be concluded that for high (50C) temperature the shrinkagemoisture model has a tendency to overestimate the real kinetics of moisture transfer. Taking into account the temperature 38C for the third stage of drying, the shrinkage-moisture relational model was used for estimation of average moisture content at
FIG. 10. Relationship between estimated ks and km and for three-stage batch drying at 38C (circles) and 50C (squares).
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TABLE 3 Moisture content at the endpoint of drying: estimation vs. experiment Average bulk moisture content Composition Measured Experiment of root sizes Estimate value Deviation 10 11 12 13 14 15 16 17
‘‘Normal’’ ‘‘Small’’
‘‘Large’’
0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
0.13 0.14 0.09 0.07 0.09 0.11 0.10 0.11
0.03 0.04 0.01 0.03 0.01 0.01 0.00 0.01
the endpoint. The results of the experimental estimation of average moisture content and direct moisture measurements at the endpoint of drying are summarized in Table 3. The discrepancy between the estimated and measured values of bulk average moisture content may be related to non-uniform drying conditions in the batch. However, from Table 3 it follows that the moisture content at the endpoint could be predicted as an interval estimate from 0.08 to 0.12 with 95% confidence. The standard error of estimation of moisture content at the endpoint was 0.0176 g=g. Testing of Accuracy in Time Estimation A performance evaluation of CVS for time estimation was done on the basis of nine batch experiments (3 isothermal and 6 three-stage) with some variation in size assortment (normal, large, and small) and moisture content. The time to the critical control point was estimated from the recurrent approximation of the shrinkage rate factor ks for isothermal conditions and Eq. (7). The results of the time estimation with the shrinkage=moisture relational model are shown in Fig. 11. The standard error of linear regression was 3.09 h with the coefficient of determination 0.99. The accuracy in the estimation of critical control points in time domain was 10% with 95% confidence. It follows that the model could be successfully used for the control of ginseng drying and is robust to uncertainty in size assortment, initial moisture content and drying conditions. The results of testing the computer-vision intelligent controller in isothermal drying are shown in Fig. 12a, and results of testing the computer-vision intelligent controller in three-stage batch drying in Fig. 12b. From the experimental data it follows that the estimator gives an overestimation of drying time at t1 and t2, while the exponential predictor gives an underestimation at the
FIG. 11. Estimation of time in critical control points with CVS for three-stage batch drying experiments.
FIG. 12. Accuracy of prediction of critical control points with CVS-estimator and predictor for isothermal (a) and three-stage (b) drying.
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first critical control point t1 and an overestimation at the second point t2. At the endpoint of drying, the estimation of t3 was close to the prediction. The error in estimating the critical control points was not evenly distributed on the drying cycle. For the first critical control point (Xc1 ¼ 1.0 g=g), the error of time prediction was greatest (11%). For the second critical point (Xc2 ¼ 0.25 g=g), the error was about 8%. The minimal error 5% occurred at the endpoint of drying (Xc3 ¼ 0.1 g=g). It follows that towards the end of drying the accuracy of time prediction increases. The moisture content estimate X(t) was used as a dynamic variable in global control loop, providing observability of the drying process. Color degradation was estimated as an independent dynamic variable with another model (see Fig. 7). This estimate was used to prevent quality degradation below specified threshold. Errors for estimation of moisture content and quality were calculated as discrepancy between estimation from the observer and direct measurements. Additionally, the user-friendly graphical interface was developed. The operator was able to specify drying conditions (temperatures for each stage of drying, relative humidity, air velocity, size), initial, equilibrium, and critical moisture contents, as well as the rate of image sampling. Online estimates of shrinkage, color, and moisture content in each stage of drying were displayed online. The quality of drying process was indicated ‘‘EXCELLENT,’’ ‘‘HIGH,’’ ‘‘SATISFACTORY,’’ or ‘‘NON SATISFACTORY.’’ If quality was within an allowable threshold, then ‘‘DRYING IN PROGRESS’’ was displayed. Otherwise it was displayed ‘‘CORRECTION’’ and correction of the drying regime was required.
CONCLUSIONS Experimental study of the computer-vision system (CVS) for control of ginseng drying showed advantages of computer-vision for online monitoring of important state variables, such as shrinkage, color, and texture. Area shrinkage was identified from image morphological attributes, providing sufficient discriminatory information about moisture loss in the range from 2.6 to 0.3 g=g and temperatures from 38 to 50C. Moisture was determined from the relationship between area shrinkage and moisture content with 8% error and 95% confidence. Color measurements demonstrated high sensitivity of quality to drying conditions. Drying at 50C resulted in significant color changes and unacceptable quality degradation. However, the quality of three-stage (38-50-38C) drying was compatible with recommended isothermal (38C) drying due to significant (30–40%) reduction in drying time. Online estimates of moisture and color were used for temperature control in the pilot batch dryer with embedded computer-vision observer (IMAQTM Vision Builder) and
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controller (Lab View 7.0). Experiments showed the stability and robustness of the control system, combined with high accuracy in the estimation of drying time (8–14% of error with 95% confidence). The discrepancy between the estimation of moisture from the shrinkage= moisture relational model and direct measurements did not exceed 20%. Composite moisture measurements at the endpoint demonstrated the uniform drying of root mixture to the average moisture content of 0.1 g=g, with minor variations between individual roots in the range of 0.07–0.12 g=g. The results demonstrated the feasibility of CVS as an accurate online tool for a closed-loop food drying. Data extracted from image analysis represent both quality factors perceived by consumers (color, texture) and process parameters (moisture content, drying rate), important for the development of ‘‘smart’’ drying technologies. ACKNOWLEDGMENTS I would like to express my gratitude to Dr. Valerie Davidson and Dr. Ralph Brown, who helped me very much with valuable comments. I would like to express special acknowledgement to Scott Noble for his help in the development of IMAQ Vision Builder control applications.
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