Soil characterization and monitoring in agriculture represent the primary key-factors influencing its productivity and the quality of the produced products. A correct and continuous knowledge of agricultural soil characteristics can help to optimize its use and its degree of exploitation both in absolute terms and with reference to specific cultivations. Soil characterization is conventionally performed adopting integrated physical-chemical analyses based on soil portion (samples), properly sampled, classified and then delivered to specialized laboratories. Such an approach obviously requires a chain of actions and it is time consuming. In this work it is examined the possibility offered by multi and hyperspectral digital imaging based spectrophotometric techniques in order to perform fast, reliable and low cost “in situ” analyses to identify and quantify specific soil attributes, of primary importance in agriculture, as: water, basic nutrients and organic matter content. The proposed hardware and software (HW&SW) integrated architecture have been specifically developed, and their response investigated, with the specific aim to contribute to study a set of “flexible”, and very simple, procedures to apply in order to be utilized to operate, not only in agricultural soil characterization, but also in other fields as the environmental monitoring and polluted soils reclamation.

There have been great interests in using twin-screw extruders under high moisture conditions to produce textured vegetable proteins. Unlike the low moisture extrusion counterpart, a product extruded under high moisture can have well-defined fiber orientation and bears a strong resemblance to muscle meat. The textural properties of such extruded products are important for consumer acceptance. In this study, we developed a novel fluorescence polarization based technique that measures the fiber formation of extruded protein products. The experimental results using our new technique showed good agreements with results obtained from visual inspection and digital imaging of the dissected samples. The new technique provides an in vivo and noninvasive approach to characterize the fiber formation of textured vegetable proteins under high moisture extrusion. It has a potential to be used as a real time monitoring tool in food extrusion studies.

Radial basis function networks (RBFN) have been widely used for function approximation and pattern classification as an alternative to conventional artificial neural networks. In this paper, reflectance spectroscopy and chemical measurements of total soluble solids (TSS)content were used to develop a nondestructive technique for predicting the TSS and a relationship was also established between the TSS content in pears determined by diffuse reflectance spectra (4200-12500cm^-1) and by chemical measurements. The effectiveness of the radial basis function networks of nonlinear calibration model was presented and compared with the linear algorithms of the partial least squares calibration models. The results show that the relatively coefficient of determination (r) of prediction obtained with linear partial least squares and the nonlinear radial basis function networks are 0.72, 0.83 and the root mean square error of prediction are 0.49, 0.45 respectively. Our results revealed that the calibration model of radial basis function networks produced better prediction of TSS than the model of partial least squares when the samples consist of multi-components.

Wavelet transform (WT) has proven a powerful and efficient tool for dealing with chemical data due to its characteristic of dual localization and has been widely used in analytical chemistry. This paper aims at serving three purposes: First, it gives a review of the applications of the wavelet transform in infrared spectroscopy; Second, it gives a quick summary of aspects and properties of wavelets and wavelet transforms which are needed in order to understand how to (pre-) process data from spectrometry with wavelet methods; Third, it shows on a typical example (apple NIR spectra) how wavelet transforms can be used in order to extract quantitative information. The sugar content of intact apple was measured by NIRS and analyzed by wavelet transform, which is a new development in signal treatment method in recent years. The results show that the spectra treated with wavelet transform indicate more effectively the relationship with sugar content in intact apple. Compared with original spectra, wavelet transform of three-size has the most marked relation with sugar content. The predicting precision of five-element regression is the best and the scale 3 is the best for its 0.904 correlation efficient of determination and the 0.777 in standard error of prediction which is less than that of primitive spectra. Therefore, the conclusion of improved predicting precision for quantitative detection of sugar content in intact apple with wavelet transform can be drawn.

The principle of the ultraviolet and visible (UV-VIS) transmittance method to detect the quality of intact poultry egg and the characteristic of the transmittance spectra of the eggshell, egg content and intact egg were investigated in the spectra range of 200-800 nm using UV-VIS (UV-1100 nm)spectrophotometer at the air condition of 28±2°C and less than 55% R. H. The results indicated that eggshell thickness, different testing position and surface color of the eggshell influenced on spectroscopic characteristic of poultry eggs at a certain extent. There was obvious difference about transmittance spectra between the fertilized and non-fertilized eggs’ content. Two wave crests about 610 nm and 710 nm, and one wave trough about 650 nm in the spectral wavelength range of 500-760 nm were found in this research. A relationship between the intact egg transmittance and the storage time was developed and the correlation equation was established by linear regression analysis of SPSS10.0 with the correlation coefficients was 0.935 based on the sensitive spectral wavelength at the sensitive wavelength of 465 nm. There was significance at the α=0.01 level between the transmittance and the different storage time.

In this paper, a study for the prediction of organileptic properties of snack food in real-time using RGB color images is presented. The so-called organileptic properties, which are properties based on texture, taste and sight, are generally measured either by human sensory response or by mechanical devices. Neither of these two methods can be used for on-line feedback control in high-speed production. In this situation, a vision-based soft sensor is very attractive. By taking images of the products, the samples remain untouched and the product properties can be predicted in real time from image data. Four types of organileptic properties are considered in this study: blister level, toast points, taste and peak break force. Wavelet transform are applied on the color images and the averaged absolute value for each filtered image is used as texture feature variable. In order to handle the high correlation among the feature variables, Partial Least Squares (PLS) is used to regress the extracted feature variables against the four response variables.

A line-scanned based digit image description method was developed, where a digit image was scanned horizontally and the continuous pixel with similar information was described as a line segment, only the horizontal coordinate of the start pixel and the end were recorded to a node, and all the nodes on the same row were linked as a linked list called horizontal line list, all the horizontal line list of the image were stored in an array called image list by their vertical coordinate. The adjacent relationship between two vertical neighbor line segment was judged by the end coordinate of one line segment and the start of the others, in this way, all the adjacent horizontal line were move to a new array from image list to symbol an object, which was segmenting, where the operating of image filtering, object detecting, contour tracing was finished in one times. The method was applied to a realtime machine vision system (the computer is P4 1.8G, 128M RAM) for fruit quality inspection. The image resolution was 100 x 120, and the image of fruit on the image was about 40% to the whole. The processing rate was over 180 images per second.

The objective of this research is to develop algorithms to classify varieties of rice seeds based on external features. The rice seeds used for this study involved five varieties of Jinyou402, Shanyou10, Zhongyou207, Jiayou and IIyou3207. Images of rice seeds were acquired with a color machine vision system. Each image was processed to extract twenty-two quantitative features. The classification ability of all the features was evaluated for different varieties recognition. The shape difference between Jinyou402 and Shanyou10 is obvious. The classification of Jinyou402 and Shanyou10 achieved an accuracy of 100% when a single feature such as the length-width ratio was used. Jinyou402 and IIyou couldn't be classified very well using one or two features. Then a perceptron was created and achieved an accuracy of 100% for both of Jinyou402 and IIyou. The shape difference between Jinyou402 and Zhongyou207 is obscure with naked eyes. All features were analyzed with principal components analysis method. A two-layer back propagation network was created and trained using gradient descent with momentum and adaptive learning rate. Nr. of hidden nodes was tested and early stopping skill was used. The total error of the finally established net is 2% for the classification of Jinyou402 and Zhongyou207. At last, all the images of five varieties were recognized as five classes. Another feed-forward network was created and trained using conjugate gradient back-propagation with Polak-Ribiere updates. Samples were disordered to train the network. The network achieved an average accuracy of about 85% for the five varieties.

Chicken tissue acts as a turbid medium in optical wavelength. Optical characterization data of fresh chicken dark and white meat were studied using the theory of light diffusion. The gaussian-like transmission profile was used to determine the transport mean free path and absorption. The refractive index, a fundamental parameter, was extracted via transmission correlation function analysis without using index-matching fluid. The variation in refractive index also produced various small shifts in the oscillatory feature of the intensity spatial correlation function at distance shorter than the transport mean free path. The optical system was calibrated with porous silicate slabs containing different water contents and also with a solid alumina slab. The result suggested that the selective scattering/absorption of myoglobin and mitochondria in the dark tissues is consistent with the transmission data. The refractive index was similar for dark and white tissues at the He-Ne wavelength and suggested that the index could serve as a marker for quality control. Application to chicken lunchmeat samples revealed that higher protein and lower carbohydrate would shift the correlation toward smaller distance. The pure fat refractive index was different from that of the meat tissue. Application of refractive index as a fat marker is also discussed

Firmness of apple fruit is an important quality attribute, which varies greatly in the same lot of fruit due to such factors as climatic condition, cultural practice, harvest time or maturity level, and postharvest handling and storage. This research developed a compact multispectral imaging system with a low cost digital camera and a liquid crystal tunable filter (LCTF), and proposed a modified Lorentzian distribution (MLD) function to describe scattering profiles acquired from Red Delicious apples. The LCTF, which allows for the rapid, vibration-less selection of any wavelength in the visible/near-infrared range, was used to find optimal wavelengths over the spectral region between 650 nm and 1,000 nm for predicting apple fruit firmness. Radial scattering profiles were described accurately by the MLD function with four profile parameters for wavelengths between 650 nm and 1000 nm at an interval of 10 nm. Multi-linear regression (MLR) and cross-validation were performed on relating MLD parameters to fruit firmness. The prediction model gave good firmness predictions with the correlation coefficient (r) of 0.82 and the standard error of validation (SEV) of 6.64 N, which were considerably better than those obtained with visible/near-infrared spectroscopy.

Recently, the imaging research group at Russell Research Center, ARS in Athens, Georgia has developed a real-time multispectral imaging system for fecal and ingesta contaminant detection on broiler carcasses. The prototype system includes a common aperture camera with three optical trim filters (515.4, 566.4 and 631-nm wavelength), which were selected by visible/NIR spectroscopy and validated by a hyperspectral imaging system. The preliminary results showed that the multispectral imaging technique can be used effectively for detecting feces (from duodenum, ceca, and colon) and ingesta on the surface of poultry carcasses with a processing speed of 140 birds per minute. The accuracy for the detection of fecal and ingesta contaminates was 96%. However, the system contains many false positives including scabs, feathers, and boundaries. This paper demonstrates calibration of common aperture multispectral imaging hardware and real-time multispectral image processing software. The software design, especially the Unified Modeling Language (UML) design approach was used to develop real-time image processing software for on-line application. The UML models including class, object, activity, sequence, and collaboration diagram were discussed. Both hardware and software for a real-time fecal and ingesta contaminant detection were tested at the pilot-scale poultry processing line.

The Agricultural Research Service (ARS) has developed a hyperspectral imaging system to detect fecal contaminants on poultry carcasses. The system operates from about 400 to 1000 nm, but only a few wavelengths are used in a real-time multispectral system. ARS has reported that the ratio of reflectance images at 565 nm and 517 nm was able to identify fecal contaminants. However, this ratio alone also misclassified numerous non-fecal carcass features (false positives). Recent modifications to the system, including improved lighting, new camera, new spectrograph, and a new algorithm with an additional wavelength, have increased fecal detection accuracy while reducing the number of false positives. The new system was used to collect hyperspectral data on 56 stationary poultry carcasses. Carcasses were contaminated with both large and small spots of feces from the duodenum, ceca, and colon, and ingesta from the crop. A total of 1030 contaminants were applied to the carcasses. The new algorithm correctly identified over 99% of the contaminants with only 25 false positives. About a quarter of the carcasses had at least one false positive.

A simple multispectral classification method for the identification of systemically diseased chickens was developed and tested between two different imaging systems. An image processing algorithm was developed to define and locate the region of interest (ROI) as classification areas on the image. The average intensity was calculated for each classification area of the chicken image. A decision tree algorithm was used to determine threshold values for each classification areas. The wavelength of 540 nm was used for image differentiation purpose. There were 164 wholesome and 176 systemically diseased chicken images collected using the first imaging system, and 332 wholesome and 318 systemically diseased chicken images taken by the second imaging system. The differentiation thresholds, generated by the decision tree method, based on the images from the first imaging system were applied to the images from the second imaging system, and vice versa. The accuracy from evaluation was 95.7% for wholesome and 97.7% of systemically diseased chickens for the first image batch, and 99.7% for wholesome and 93.5% for systemically diseased chickens for the second image batch. The result showed that using single wavelength and threshold, this simple classification method can be used in automated on-line applications for chicken inspection.

We describe a fast method for dimensionality reduction and feature selection of ratio features for classification in hyperspectral data. The case study chosen is to discriminate internally damaged almond nuts from normal ones. For this case study, we find that using the ratios of the responses in several wavebands provides better features than a subset of waveband responses. We find that use of the Euclidean Minimum Distance metric gives slightly better results than the more conventional Spectral Angle Mapper distance metric in a nearest neighbor classifier.

USDA and the Institute for Technology Development are currently collaborating on a project using hyperspectral imagery to detect pathogens such as mycotoxin producing molds in grain products. The initial experiments are being implemented on corn kernels. When molds appear on corn, reflectance spectra from the molds and corn are mixed. Therefore, it is important to characterize the corn reflectance, which is the background reflectance in the image. The objective of this study was to qualitatively identify and quantify kernel signatures of several corn genotypes. Four different corn genotypes (genetically distinct corn lines) and four near isogenic corn lines were prepared at the USDA laboratory. The study used a visible-near-infrared hyperspectral imaging system for data acquisition. The imaging system utilizes focal plane pushbroom scanning for high spatial and high spectral resolution imaging. Procedures were developed for optimum image calibration and image processing. It was expected that the results would be useful for reducing the background influence of corn in mold detection and would also be applicable in corn genotype identification, especially among corn lines with different resistance levels to molds.

The presence of pits in processed cherry products causes safety concerns for consumers and imposes potential liability for the food industry. The objective of this research was to investigate a hyperspectral transmission imaging technique for detecting the pit in tart cherries. A hyperspectral imaging system was used to acquire transmission images from individual cherry fruit for four orientations before and after pits were removed over the spectral region between 450 nm and 1,000 nm. Cherries of three size groups (small, intermediate, and large), each with two color classes (light red and dark red) were used for determining the effect of fruit orientation, size, and color on the pit detection accuracy. Additional cherries were studied for the effect of defect (i.e., bruises) on the pit detection. Computer algorithms were developed using the neural network (NN) method to classify the cherries with and without the pit. Two types of data inputs, i.e., single spectra and selected regions of interest (ROIs), were compared. The spectral region between 690 nm and 850 nm was most appropriate for cherry pit detection. The NN with inputs of ROIs achieved higher pit detection rates ranging from 90.6% to 100%, with the average correct rate of 98.4%. Fruit orientation and color had a small effect (less than 1%) on pit detection. Fruit size and defect affected pit detection and their effect could be minimized by training the NN with properly selected cherry samples.

A rugged, filter-based fluorometer capable of time-resolved luminescence (TRL) measurements was designed, prototyped and tested for field applications. The instrument operation and data processing were controlled by a laptop computer running a custom LabVIEW program. A xenon flashlamp was used as the light source and a photomultiplier tube (PMT) as the photodetector. A gating technique was implemented to effectively overcome PMT saturation by intense xenon lamp flash so signal integrity was maintained even at very high gains, leading to improved sensitivity and reproducibility. The instrument was tested by TRL using tetracycline as a model analyte; and the signal was digitized at a 2-μs time resolution and a 12-bit amplitude resolution. Its performance was similar to or slightly better than that of a commercial fluorescence spectrophotometer. A 0-300 ppb linear dynamic range (r² = 0.9996) and a 0.025-ppb limit of detection (LOD) were achieved with a ≤5% relative standard deviation.

Pesticides are a key component in protecting crops and producing the quantity of food required by today's world population. However, since excessive concentrations pose a threat to human health, the USA sets strict tolerance levels to ensure public safety. Unfortunately, many other countries ignore these regulations and imported food exceeding these levels or contaminated with banned pesticides is a common occurrence. Furthermore, rapid chemical analysis of pesticide residues is unavailable, and only a very small fraction of foods are inspected. The greatest concern is fruit, for which an estimated 12 million tons were imported in 2003. In an effort to address this need, we have been developing a simple and rapid procedure to analyze for pesticides on fruit surfaces or in the juice of fruits. The procedure employs metal-doped sol-gel filled capillaries that both chemically extracts the pesticide and generates surface-enhanced Raman spectra when irradiated. The SERS-active capillaries, sensitivity, and preliminary fruit analyses are presented.

Multi-photon laser wave mixing is an unusually sensitive nonlinear spectroscopic method that offers excellent sensitivity levels in the detection of enzyme activity levels. Coherent interactions between two crossed laser beams inside an absorbing medium generate dynamic gratings which in turn diffract off incoming photons to create a coherent laser-like signal beam. Since the signal beam is generated only in the presence of an absorbing medium, it yields very low background noise levels. Enzyme activity is measured by monitoring the change in the signal intensity as the absorbing compound is consumed or produced as a result of the enzyme catalyzed reactions. Optical setup for this one-color multi-photon method is simple compared to other multi-photon methods. Unlike fluorescence methods, wave mixing offers virtually 100% signal collection efficiency. Wave mixing is also less susceptibility to quenching and it allows analysis of both fluorescing and non-fluorescing labels. Since the laser wave-mixing signal has a quadratic dependence on analyte absorptivity, this method is more sensitive to small changes in the composition of the solution as compared to conventional detection methods. In addition, the use of nanoliter or picoliter probe volumes allows sensitive detection of small samples with high spatial resolution. Thermal and other physical properties of condensed-phase analytes can be advantageously used to enhance the sharpness of the laser-induced gratings and, hence, the analytical signal.

Outbreaks of E. coli O157:H7 by the consumption of contaminated cantaloupes fruits have been documented. Pathogens harbored in the networked but porous veins in khaki colored skin are difficult to remove. Thus, sensitive and efficient methods are needed to detect the presence of E. coli O157:H7 in cantaloupes. In this work, known quantities of the E. coli were inoculated on cantaloupe skins or flesh at room temperature for 1 h. The contaminated samples were incubated in growth media at 37°C for 3.3h. The bacteria captured by magnetic beads coated with anti E. coli O157 antibodies were further sandwiched by second anti E. coli O157 antibodies containing peroxidase for chemiluminescent measurements of captured bacteria. Alternatively, the captured bacteria were treated with electron-shuttering reagent to detect the cellular level of NAD(P)H via bioluminescence. The detected enzyme activity (peroxidase) and the NAD(P)H were used to measure the presence of the pathogen. The results indicated both the chemiluminescence and the fluorescence methods, in 96 well microplate format, could be applied to detect the E. coli contamination of cantaloupes.

Our laboratory has been utilizing fluorescence techniques as a potential means for detection of quality and wholesomeness of food products. A system with a short pulse light source such as a laser coupled with a gated detector can be used to harvest fluorescence in ambient light conditions from biological samples with relatively low fluorescence yields. We present a versatile multispectral laser-induced fluorescence (LIF) imaging system capable of ns-scale time resolved fluorescence. The system is equipped with a tunable pulse laser system that emits in the visible range from 410 nm to 690 nm. Ns-scale, time-dependent multispectral fluorescence emissions of apples contaminated with a range of diluted cow feces were acquired. Four spectral bands, F670, F680, F685 and F730, centered near the emission peak wavelengths of the major constituents responsible for the red fluorescence emissions from apples artificially contaminated with cow feces were examined to determine a suitable single red fluorescence band and optimal ns-gate window for detection of fecal contamination on apples. The results based on the ns decay curves showed that 670 nm with 10 nm full width at half maximum (FWHM) at a gate-delay of 4 ns from the laser excitation peak provided the greatest differences in time-dependent fluorescence responses between feces contaminated spots and apples surfaces.

The analysis of the vibration responses of a fruit is suggested to measure firmness non-destructively. A wooden ball excited the fruits and the response signals were captured using an accelerometer sensor. The method has been well studied and understood on ellipsoidal shaped fruit (watermelon). In this work, using the finite element simulations, the applicability of the method on watermelon was investigated. The firmness index is dependent on the mass, density, and natural frequency of the lowest spherical modes (under free boundary conditions). This developed index extends the firmness estimation for fruits or vegetables from a spherical to an ellipsoidal shape. The mode of Finite element analysis (FEA) of watermelon was generated based on measured geometry, and it can be served as a theoretical reference for predicting the modal characteristics as a function of design parameters such as material, geometrical, and physical properties. It was found that there were four types of mode shapes. The 1st one was first-type longitudinal mode, the 2nd one was the second-type longitudinal mode, the 3rd one was breathing mode or pure compression mode, and the fourth was flexural or torsional mode shape. As suggested in many references, the First-type spherical vibration mode or oblate-Prolate for watermelon is the lowest bending modes, it's most likely related to fruit firmness. Comparisons of finite element and experimental modal parameters show that both results were agreed in mode shape as well as natural frequencies. In order to measure the vibration signal of the mode, excitation and sensors should be placed on the watermelon surface far away from the nodal lines. The excitation and the response sensors should be in accordance with vibration directions. The correlations between the natural frequency and firmness was 0.856, natural frequency and Young's modulus was 0.800, and the natural frequency and stiffness factor (SF) was 0.862. The stiffness factor (SF) is adequate expression for the Modulus of Elastic (MOE), and adopted in the evaluation of their firmness.

The objective of this research is to develop a digital image analysis algorithm for detection of diseased rice seeds based on color features. The rice seeds used for this study involved five varieties of Jinyou402, Shanyou10, Zhongyou207, Jiayou99 and IIyou3207. Images of rice seeds were acquired with a color machine vision system. Each original RGB image was converted to HSV color space and preprocessed to show, as hue in the seed region while the pixels value of background was zero. The hue values were scaled so that they varied from 0.0 to 1.0. Then six color features were extracted and evaluated for their contributions to seed classification. Determined using Blocks method, the mean hue value shows the strongest classification ability. Parzen windowing function method was used to estimate probability density distribution and a threshold of mean hue was drawn to classify normal seeds and diseased seeds. The average accuracy of test data set is 95% for Jinyou402. Then the feature of hue histogram was extracted for diseased seeds and partitioned into two clusters of spot diseased seeds and severe diseased seeds. Desired results were achieved when the two cancroids locations were used to discriminate the disease degree. Combined with the two features of mean hue and histogram, all seeds could be classified as normal seeds, spot diseased seeds and severe diseased seeds. Finally, the algorithm was implemented for all the five varieties to test the adaptability.

The laws of gray distortion of machine vision system were discussed, and a method for gray calibration was presented. Five standard templates with unanimous gray value were used as the research objects. The average gray values of X direction and Y direction of the standard template images were obtained according to row and column. The gray distortion models were developed with moving average model of two image pixels. The models of five standard templates were developed separately, and the correlation coefficients of each model were above 0.96. The parameters of the gray distortion model were independent to the templates themselves. The gray calibration models of row and column were developed based on the gray distortion models separately, and the image gray values of other templates were proportion to the true value after gray calibration with the gray calibration models. The test verified the method.

An array biosensor developed for simultaneous analysis of multiple samples has been utilized to develop assays for toxins and pathogens in a variety of foods. The biochemical component of the multi-analyte biosensor consists of a patterned array of biological recognition elements immobilized on the surface of a planar waveguide. A fluorescence assay is performed on the patterned surface, yielding an array of fluorescent spots, the locations of which are used to identify what analyte is present. Signal transduction is accomplished by means of a diode laser for fluorescence excitation, optical filters and a CCD camera for image capture. A laptop computer controls the miniaturized fluidics system and image capture. Results for four mycotoxin competition assays in buffer and food samples are presented.

In this study, we developed a nondestructive way to analyze water and chlorophyll content in tomato leaves. A total of 200 leaves were collected as experimental materials, 120 of them were used to form a calibration data set. Drying chest, SPAD meter and NIR spectrometer were used to get water content, chlorophyll content and spectrums of tomato leaves respectively. The Fourier Transform Infrared (FTNIR) method with a smart Near-IR Updrift was used to test spectrums, and partial least squares (PLS) technique was used to analyze the data we get by normal experimentation and near infrared spectrometer, set up a calibration model to predict the leaf water and chlorophyll content based on the characteristics of diffuse reflectance spectrums of tomato leaves. Three different mathematical treatments were used in spectrums processing: different wavelength range, different smoothing points, first and second derivative. We can get best prediction model when we select full range (800-2500nm), 3 points for spectrums smoothing and spectrums by baseline correction, the best model of chlorophyll content has a root mean square error of prediction (RMSEP) of 8.16 and a calibration correlation coefficient (R²) value of 0.89452 and the best model of water content has a root mean square error of prediction (RMSEP) of 0.0214 and a calibration correlation coefficient (R²) value of 0.91043.

Soil texture is an important physical property of soil that affects many agricultural activities. It describes soil composition in terms of the relative proportion of three typical sized particles, i.e., clay, silt and sand. Traditional soil texture analysis methods involve inefficient physical and chemical processing procedures. To improve the efficiency for the analysis, previously we proposed a wavelet frame based image analysis system that related textural patterns observed at soil surface to the particle compositions. The system was capable of differentiating between 33 soil samples in terms of three categories with a 91% success rate. However, it required image acquisition under two camera settings. In this paper, we further our investigation with an improved image analysis approach, in which Gabor wavelets are utilized to generate textural features. Experiments showed that a combination of analysis results from two groups of Gabor wavelets yielded a 91% classification accuracy. Although the accuracy remained unchanged, the Gabor wavelet based system provided improved efficiency and flexibility over the previous system in that it needs only one set of images acquired under a fixed camera setting. Moreover, an improved consistency between individual classification votes was observed with the new system, indicating a greater potential for a finer categorization of soil textures.

A real time machine vision system for fruit size inspection was developed, which solved the problems such as fast processing the large amount of image information, improving system performance for real time dynamic image capture and processing capability, increasing precision of detection etc. For each fruit, four images were caught, and from which all the quality information of the whole surface were collected. Images were grabbed with a CCD camera (TMC-7DSP) and a frame grabber (Matrox Meteor II/MC), which is described in RGB space. The value of R/B was used as an index for image binary threshold after blurred image restoration. Median filter was used to denoise before edge detecting with Laplace Operator. A sphere fruit size-inspecting model was set up with a set of standard ball to calibrate the fruit size after the relative size of fruit, which was obtained with the method of partition edge point sets. The absolute error of the system was less than 1.1 mm and inspecting rate was over 31 fruits per second. That was this method can obtain fair inspecting speed, small absolute error, and filled the requirement of fruit automatic fruit sorting. But something is need to be paid attention, if shadow being in this vision system, it will arise big error when use partitions edge point, so it is needed to avoid the shadow.

Hyperspectral images of cucumbers were acquired before and during cold storage treatments as well as during subsequent room temperature (RT) storage to explore the potential for the detection of chilling induced damage in whole cucumbers. Region of interest (ROI) spectral features of chilling injured areas, resulting from cold storage treatments at 0°C or 5°C, showed a reduction in reflectance intensity during multi-day post chilling periods of RT storage. Large spectral differences between good-smooth skins and chilling injured skins occurred in the 700-850 nm visible/NIR region. A number of data processing methods, including simple spectral band algorithms, second difference, and principal component analysis (PCA), were attempted to discriminate the ROI spectra of good cucumber skins from those of chilling injured skins. Results revealed that using either a dual-band ratio algorithm (Q_811/756) or a PCA model from a narrow spectral region of 733-848 nm could detect chilling injured skins with a success rate of over 90%. Furthermore, the dual-band algorithm was applied to the analysis of images of cucumbers at different conditions, and the resultant images showed more correct identification of chilling injured spots than other processing methods.