Images from conventional black and white video systems are being digitized in computers to measure the area of small particles. The method was developed to accurately measure the area of trash in cotton. However, it can be applied to similar area measuring problems. Because a video system exhibits different sensitivities to light at different wavelengths, its output changes as the color of an image changes. Additionally, the spectral distribution of illumination influences the light reflected from objects. Video measurements for the background material were used to set the threshold value needed to identify image pixels associated with trash particles. The effect of particle color on its measured area was demonstrated with painted dots on panels and actual cotton samples.

A portable, prototype NIR spectrophotometer was redesigned for improved accuracy and ease of use. The instrument was used to estimate soil organic carbon (or organic matter), moisture content, and clay content of 30 Illinois surface soils in the laboratory. Accuracy of carbon estimation by partial least squares regression was similar to that obtained with the previous design. The unit was also used to estimate soil organic carbon through the profile of two Illinois soils. Results of this test were promising, but need to be verified over a wider range of soils.

Imaging in the near-infrared region has been used frequently to detect bruises on `Delicious' apples. Pixel intensities from bruised and nonbruised regions within an image of an apple are compared to characterize the time effects. Near-infrared reflectance from a bruised site is generally lower than the reflectance from a nearby nonbruised region. This difference usually reaches a maximum 24 hours after inducing the bruise. As the bruise ages in storage, reflectance from the bruised region increases. The reflectance continues to increase and eventually exceeds the reflectance from a nonbruised region.

Digital images of reflected light in both the near-infrared (NIR) and visible wavelengths from the surface of bruised and unbruised Golden Delicious apples were captured for classifying bruise damage. Each of the attributes of two models for color representation, RGB and HSI, were compared to NIR for their ability to discriminate bruised from unbruised tissue. The surface reflectance for good tissue decreased from the fruit center outward, except saturation which increased. Reflectance of good tissue also varied adjacent to the bruised area compared to a location 60 degrees away. NIR, green, hue, and red were the features which showed the most contrast between bruised and undamaged tissue. This contrast did not decrease for green, red, and hue as storage time increased.

Five classification techniques were compared for their accuracy in classifying normal, septicemic, and cadaver chicken carcasses, based on their optical reflectance spectra in the visible and near-infrared regions (504 - 888 nm). The techniques compared were the multiple- linear-regression, closest-class-mean, k-nearest-neighbor, artificial-neural-network (ANN), and principal-component/Mahalanobis-distance methods. The spectra were obtained with a diode array spectrophotometer system. The collection of the data and the development of the multiple linear regression model were described previously (Chen and Massie, 1992). The best results were obtained with the ANN model using the reflectances at the 8 optimal wavelengths identified by the multiple-linear regression method. The overall classification accuracy of this model was 91.6%. However, another ANN model with 192 inputs, which resulted in an overall accuracy of 90.4%, was preferred, because it utilized a broader range of reflectances (512.9 to 851.6 nm) without performing a wavelength search. This model yielded a 94.4% accuracy for the normal carcasses, 83.3% for the septicemic carcasses, and 94.3% for the cadaver carcasses.

The dichromatic reflection paradigm describes light reflection from optically inhomogeneous materials as the sum of body (diffuse) and interface (specular) reflections. Interface reflection represents unaltered light reflected from a material's surface. Body reflection represents light altered by the material's pigments and thus may provide information about the identity of the material. Wood is an optically inhomogeneous material that is also anisotropic. This latter property adds further complexity to the analysis of wood-surface images by creating localized magnitude differences in interface reflection as surface texture and fiber orientation change. This paper presents the results of a study that tested whether the use of only the body component of reflected light can significantly improve the classification of wood-surface features. To this end, reflectance curves of various Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) veneer features were separated into body and interface components, and their dimensionality reduced to a small number of basis function. Two discriminant functions, one constructed from body reflectances and the other from total reflectances, were then developed from the reduced reflectance data. The performance of the two discriminant functions were compared by classifying a new set of wood-feature spectral reflectances with each discriminant function.

Near-infrared transmittance spectra (740 - 1140 nm) were gathered on single kernels of intact wheat (Triticum aestivum) for the purpose of establishing the feasibility of measuring wheat hardness (i.e., texture) by spectroscopy. Spectra of kernels from a ten-variety hardness standardization set were modeled using multiple linear regression (MLR) on log(1/T) and d²log(1/T)/d(lambda) ² (up to five terms for each) and partial least squares (PLS) analysis (up to nine factors). Near-infrared diffuse reflectance hardnesses, determined by an official method of the American Association of Cereal Chemists, were the reference values. Single kernel hardness models were then applied to five varieties of wheat excluded in calibration. Results indicated that single kernel hardness by optical measurement of intact kernels is possible, presumably to the extent of the correlation between hardness and vitreousness. However, there is some doubt as to whether intact-kernel transmittance measurements are sensitive enough to measure the biochemical component (presumably, a low-molecular weight protein) that determines hardness. Five-term log(1/T) MLR and eight- factor PLS models provided the best modeling performances. Single kernel hardness models were used to examine kernel-to-kernel variation in hardness. By way of example, when the eight-factor PLS model was applied to the standardization set, Bennett had the least variation (standard deviation of 5.1 NIR-hardness units), and Nugaines had the most (s.d. equals 14.9 NIR-h.u.). Soft wheats tended to have more variation than hard wheats.

A color classification program was developed for classifying the corn germplasm into seven different color groups based on kernel colors. This heuristic based rule supervised color classification program has an overall accuracy of 99%.

The relationship between isolated starch granule morphometry and wheat hardness was studied. Starch granule size and shape may relate to grain millability, rheological properties of dough, and baking quality. Twenty four Kansas wheats were studied: 14 hard red winter (HRW) and 10 soft red winter (SRW). Isolated starch granules were viewed with light microscopy to obtain black and white images which were recorded on video tape. A program was designed to keep track of the taped images and measure starch granules without operator intervention. The data base of starch granule size and shape features of the 24 samples contained 152,237 granule observations. The number of observations per sample varied from 3,238 to 14,671. Distinguishing HRW from SRW wheat samples was accomplished by evaluation of starch granule morphometry. Several data manipulations and transformations were performed in analysis of the data. Information carried in two shape descriptors, which reflect aspect ratio and equivalent diameter distribution, was used to distinguish starch granules of HRW and SRW wheats. The percentage of starch granules in the aspect ratio range of 1.65 - 1.95 was 25.8 - 31.5% for HRW and 19.9 - 25.4% for SRW.

An image processing algorithm has been developed for machine recognition of weevils and/or weevil damage in film x-ray images of wheat kernels [8 bits, (0.25 mm)²/pixel]. The 8 bit grey scale image is converted to a binary image of interior edges and lines using a Laplacian mask, zero threshold, and background removal. In undamaged kernels the predominant feature of this image is a line representing the central crease of the kernel. In insect-damaged kernels this feature is disrupted and additional edges or lines are seen at angles to the crease. The algorithm uses convolution masks to look for intersections (45 or 90 degree angles with 4 or 5 pixel length sides) at 8 orientations. Recognition varies with insect stage; at least 50% of infested kernels are machine recognized by the 4th instar (26 - 28 days). This is comparable to 50% recognition by humans at 25.5 days for images of similar resolution. False positive responses are limited to 0.5%.

Objective measurement of meat tenderness has been a topic of concern for palatability evaluation. In this study, a real-time ultrasonic B-mode imaging method was used for measuring beef palatability attributes such as juiciness, muscle fiber tenderness, connective tissue amount, overall tenderness, flavor intensity, and percent total collagen noninvasively. A temporal averaging image enhancement method was used for image analysis. Ultrasonic image intensity, fractal dimension, attenuation, and statistical gray-tone spatial-dependence matrix image texture measurement were analyzed. The contrast of the textural feature was the most correlated parameter with palatability attributes. The longitudinal scanning method was better for juiciness, muscle fiber tenderness, flavor intensity, and percent soluble collagen, whereas, the cross-sectional method was better for connective tissue, overall tenderness. The multivariate linear regression models were developed as a function of textural features and image intensity parameters. The determinant coefficients of regression models were for juiciness (R² equals .97), for percent total collagen (R² equals .88), for flavor intensity (R² equals .75), for muscle fiber tenderness (R² equals .55), and for overall tenderness (R² equals .49), respectively.

A metal-oxide semiconductor (MOS) linear image sensor consisting of an array of 128 self- scanning, linear photodiodes was used to measure the velocity of falling grain. The sensor was used to obtain an image of the falling grain wait a known time interval and then obtain a second image. Comparison of the two images permitted determination of the position displacement experienced by the grain. Optical relationships were used to convert the image displacement to grain displacement. Grain displacement divided by the time interval yielded grain velocity. This paper addresses the sensor and data acquisition hardware, the digital signal processing software, and calibration of the sensor.

This paper discusses methods used to evaluate a feature space for identification of non-lint material (trash) in cotton samples. A main criterion for accepting any feature in the identification task was invariance under translation, rotation, and, in most cases, scale. In subsequent processing, most features were normalized. Classical grouping was performed in an n-dimensional feature space using divisive hierarchical clustering based on the Euclidian distance metric. The best results for identifying bark, stick, and leaf/pepper trash in the sample data set was 92%. By category, bark was identified correctly 88%, stick 84%, and leaf/pepper 94% of the time. Identification between leaf and pepper could be handled by defining an area cutoff in the pepper-leaf continuum.

Understanding the process of sprinkler droplet formation and behavior while in flight is crucial to the improvement of water and chemical application by sprinkler irrigation. A relatively simple and inexpensive method is presented which allows in-flight characterization of irregularly shaped liquid droplets and ligaments produced by irrigation sprinkler devices. A high-speed photographic image acquisition probe was constructed to allow nonintrusive, direct investigation of water jet breakup, drop formation and behavior, size, and shape of monodispersed drops at various locations from the nozzle. Image contrast and motion freezing was accomplished by backlighting the liquid particles with a 1.5 microsecond(s) duration stroboscope. High resolution film allowed analysis over a wide range of particle sizes (0.3 mm to 50 mm). Image processing and analysis was performed on digitized images using commercially available software. This software provided geometrical size and shape parameters of the breakup fragments and allowed discrimination between in and out-of-focus drops by post analysis. Applications of the method included: studying the effect of wind direction and speed, nozzle type, and pressure on sprinkler jet breakup.

Almost two billion conifer seedlings are produced in the U.S. each year to support reforestation efforts. Seedlings are graded manually to improve viability after transplanting. Manual grading is labor-intensive and subject to human variability. Our previous research demonstrated the feasibility of automated tree seedling inspection with machine vision. Here we describe a system based on line-scan imaging, providing a three-fold increase in resolution and inspection rate. A key aspect of the system is automatic recognition of the seedling root collar. Root collar diameter, shoot height, and projected shoot and root areas are measured. Sturdiness ratio and shoot/root ratio are computed. Grade is determined by comparing measured features with pre-defined set points. Seedlings are automatically sorted. The precision of machine vision and manual measurements was determined in tests at a commercial forest nursery. Manual measurements of stem diameter, shoot height, and sturdiness ratio had standard deviations three times those of machine vision measurements. Projected shoot area was highly correlated (r² equals 0.90) with shoot volume. Projected root area had good correlation (r² equals 0.80) with root volume. Seedlings were inspected at rates as high as ten per second.

Tomato seedling images were acquired in a two-camera image acquisition system for the seedling quality inspection. It uses two back-lit light sources, orthogonally mounted, to create seedling silhouettes for the top view and the side view image acquisitions. The images were acquired with either only back illumination or both back and side illuminations. Two adaptive thresholding techniques were selected and evaluated for segmentation accuracy. Both methods, one is based on histogram analysis and the second technique is based on the analysis of intensity and gray level gradient of local pixels, had similar performance for given lighting conditions. It is found, however, that the lighting strategy affected the image formation process. In addition, it affected the accuracy of segmentation process which separates seedling from its background.

Seed vigor and germination tests have traditionally been used to determine deterioration of seed samples. Vigor tests describe the seed potential to emerge and produce a mature crop under certain field conditions and one measure is seedling growth rate. A machine vision system was developed to measure root growth rate over the entire germination period. The machine vision measurement technique was compared to the manual growth rate technique. The vision system provided similar growth rate measurements as compared to the manual growth rate technique. The average error between the system and a manual measurement was -0.13 for the lettuce test and -0.07 for the sorghum test. This technique also provided an accurate representation of the growth rate as well as percent germination.

A computer vision system was developed for the classification of plant somatic embryos. The embryos are in a Petri dish that is transferred with constant speed and they are recognized as they pass a line scan camera. A classification algorithm needs to be installed for every plant species. This paper describes an algorithm for the recognition of Norway spruce (Picea abies) embryos. A short review of conifer micropropagation by somatic embryogenesis is also given. The recognition algorithm is based on features calculated from the boundary of the object. Only part of the boundary corresponding to the developing cotyledons (2 - 15) and the straight sides of the embryo are used for recognition. An index of the length of the cotyledons describes the developmental stage of the embryo. The testing set for classifier performance consisted of 118 embryos and 478 nonembryos. With the classification tolerances chosen 69% of the objects classified as embryos by a human classifier were selected and 31$% rejected. Less than 1% of the nonembryos were classified as embryos. The basic features developed can probably be easily adapted for the recognition of other conifer somatic embryos.

Shape parameters such as aspect, roundness, and the ratio of thickness to perimeter were used to describe plant shape and are different according to the species that they represent. Color slide images of several species of plants were digitized for computer analysis. Three optical methods were tested to separate target plants from the soil and residue background. The separation method that provided the best contrast was the normalized difference index. Subtracting the blue or the red raster from the green raster also provided good separation on soils with little residue. Once the plant image had been isolated from the background, leaf edges were automatically traced using a commercial software package. Analysis of the shape of the plant outline was then performed, resulting in the plant shape parameters. Grasses and broadleaf plants had similar values for each shape parameter during the first ten days after emergence. After this period, differences occurred between grasses and broadleaf plants. The parameter that best discriminated grasses from broadleaf plants was the aspect (major axis length/minor axis length). However, when a grass sends out more than one shoot radially from the stem, the aspect will be similar to broadleaf plants. This study contributes to the design of a system that can determine weed populations and identify plant species without the use of human intervention.

A monochrome video camera and a set of narrow bandpass optical filters were used to capture images in 13 spectral bands from 400 nm to 1000 nm. Images of 4 plant species were captured. Pixel data was extracted from the images in user defined areas on the plant leaves. Attempts were made to classify the plants based on the spectral information in the extracted data.

Growing plants, soil types, and surfaces and residues on a soil surface have distinct natural light reflectances. These reflectance characteristics have been determined using current spectroradiometry technology. Detection of plants is possible based upon the distinct reflectance characteristics of plants, soil, and residues. An optical plant reflectance sensor was developed which utilizes a pair of red and near infrared sensitive photodetectors to measure the radiancy from the plant and soil. Another pair of sensors measures radiancy from a highly radiant reference surface to accommodate varying intensities of the natural light. The ratio of the target and reference radiancies is the target reflectance. Optical filters were used to select the spectral bandwidth sensitivities for the red and NIR photodetectors. The reflectance values were digitized for incorporation into a normalized difference index in order to provide a stronger indication that a live plant is present within the field of view of the sensor. This sensor system was combined with a microcontroller for activating a solenoid controlled spray nozzle on a single unit prototype spot agricultural sprayer.

Somatic embryogenesis is a process that allows for the in vitro propagation of thousands of plants in sub-liter size vessels and has been successfully applied to many significant species. The heterogeneity of maturity and quality of embryos produced with this technique requires sorting to obtain a uniform product. An automated harvester is being developed at the University of Florida to sort embryos in vitro at different stages of maturation in a suspension culture. The system utilizes machine vision to characterize embryo morphology and a fluidic based separation device to isolate embryos associated with a pre-defined, targeted morphology. Two different backpropagation neural networks (BNN) were used to classify embryos based on information extracted from the vision system. One network utilized geometric features such as embryo area, length, and symmetry as inputs. The alternative network utilized polar coordinates of an embryo's perimeter with respect to its centroid as inputs. The performances of both techniques were compared with each other and with an embryo classification method based on linear discriminant analysis (LDA). Similar results were obtained with all three techniques. Classification efficiency was improved by reducing the dimension of the feature vector trough a forward stepwise analysis by LDA. In order to enhance the purity of the sample selected as harvestable, a reject to classify option was introduced in the model and analyzed. The best classifier performances (76% overall correct classifications, 75% harvestable objects properly classified, homogeneity improvement ratio 1.5) were obtained using 8 features in a BNN.

A new sensing technique was investigated to nondestructively measure the peel thickness of oranges destined for fresh market consumption. Coherent polarized laser emissions diffused by the subcuticular layers of the peel were filtered and imaged into a matrix CCD camera. Images were analyzed using conventional high-speed pixel operations. Resulting correlations suggest that this method may be a successful tool in real-time food processing operations providing the packer and the consumer with an objective evaluation of peel thickness, and subsequently, edible volume, juice content, and the ease with which the peel can be removed.

A range-image sensor for controlling an agricultural spraying system and the military range- image technology upon which the sensor is based are discussed. Initial testing in an orange grove in Orlando, Florida indicates that sensor control optimizes the chemical spraying process and reduces environmental pollution.

The quality of flour is mainly determined on the basis of its color and protein content. Flour color grading is performed off-line from the milling process, in a laboratory. Thus there is a need for an instrument to measure flour color on-line and give an alarm if the result is outside the specified acceptable range. Based on the analysis of the color properties of flour and optical measurement methods used in experiments, two measurement approaches are proposed, the `all spectrum method' and the `abridged spectrum method.' Taking into account the costs of the sensor to be used under real quality testing conditions, the method based on the abridged spectrum is regarded as more appropriate. This paper describes a design for a four-channel color measuring system with an optical fiber sensor head. The sensor has been employed for the practical implementation of the method under laboratory conditions, giving satisfactory results.

Laser diffraction provides a sensitive non-contacting method for measuring the diameter of a thin plant axis, and for monitoring changes related to growth or altered water status. A 2 mwatt HeNe laser beam diffracted by the plant axis produces a series of diffraction fringes which are related to the diameter (d) of the axis by the equation d equals (nR(lambda) /y), where n is the order of the diffraction fringe measured, R is the distance between the plant and the projection screen, a ground glass plate, (lambda) is the wavelength of the beam (0.6328 micrometers ), and y is the distance between the center of the diffraction pattern and the fringe of order n. The advantages of this optical technique for measuring the diameters of small cylindrical plant axes are: (1) it is non-contacting, (2) it does not require calibration, (3) no lenses are required, (4) rigid body movement of the plant axis has no effect on measurement, provided that the object remains inside the coherent and collimated light field, and (5) measurement accuracy of +/- 5% is relatively easy to achieve.

Watercore in apples is a physiological disorder that affects the internal quality of the fruit. Growers can experience serious economic losses due to internal breakdown of the apple if watercored apples are placed unknowingly into long term storage. Economic losses can also occur if watercore is detected and the entire `lot' is downgraded; however, a gain can be obtained if watercored fruit is segregated and marketed as a premium apple soon after harvest. Watercore is characterized by the accumulation of fluid around the vascular bundles replacing air spaces between cells. This fluid reduces the light scattering properties of the apple. Using machine vision to measure the amount of light transmitted through the apple, watercored apples were segregated according to the severity of damage. However, the success of the method was dependent upon two factors. First, the sensitivity of the camera dictated the classes of watercore that could be detected. A highly sensitive camera could separate the less severe classes at the expense of not distinguishing between the more severe classes. A second factor which is common to most quality attributes in perishable commodities is the elapsed time after harvest at which the measurement was made. At the end of the study, light transmission levels decreased to undetectable levels with the initial camera settings for all watercore classes.

Spectral reflectance characteristics of peanut kernels were measured using ultraviolet, visible, and infrared sensors. Kernels were classified into categories based on their spectral characteristics. Correlations of the reflectance data with qualitative and quantitative quality measurements then indicated the effectiveness of various optical sensors in identifying poor quality peanuts. The accuracy of various sensors when identifying poor quality peanuts is reported.

Experimental results of paddy rice, soybeans, wheat, and drosophila melanogasters irradiated with an FIR laser are summed up. FIR laser induced biological effects are described, including the effects on esterase isozyme, the soma clonal variation of rice, and the genetic expressions of the d. melanogaster.