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In this research, a multispectral fluorescence-based imaging algorithm was developed to detect frass contamination on mature Campari tomatoes. Tomato images were acquired using a hyperspectral fluorescence line-scan imaging system with violet LED excitation, then analyzed for wavelength selection. The fluorescence intensities at five wavelengths, 515 nm, 640 nm, 664 nm, 690 nm, and 724 nm, were used to compute three simple ratio functions to detect frass contamination. The contamination spots were created on the tomato surfaces using four low-concentration frass dilutions. The algorithms detected over 99% of the 0.2 kg/L and 0.1 kg/L frass contamination spots and successfully differentiated these spots from tomato skin surfaces, stem scars, and stems. However, differentiation of the 0.05 kg/L and 0.02 kg/L frass contamination spots was more difficult. Adjusting the algorithm to successfully detect 95% of the 0.05 kg/L spots also resulted in false-positive pixel detections occurring on 28% of the tomatoes. This study demonstrates that a simple multispectral fluorescence imaging algorithm based on violet LED excitation could be useful for rapid postharvest detection of frass contamination on tomatoes in processing lines.

Purpose: A multispectral algorithm for detection and differentiation of defective (defects on apple skin) and normal Red Delicious apples was developed from analysis of a series of hyperspectral line-scan images. Methods: A fast line-scan hyperspectral imaging system mounted on a conventional apple sorting machine was used to capture hyperspectral images of apples moving approximately 4 apples per second on a conveyor belt. The detection algorithm included an apple segmentation method and a threshold function, and was developed using three wavebands at 676 nm, 714 nm and 779 nm. The algorithm was executed on line-by-line image analysis, simulating online real-time line-scan imaging inspection during fruit processing. Results: The rapid multispectral algorithm detected over 95% of defectiv e apples and 91% of normal apples investigated. Conclusions: The multispectral defect detection algorithm can potentially be used in commercial apple processing lines.

This research developed and evaluated three multispectral algorithms derived from hyperspectral line-scan fluorescence imaging using violet LED excitation for the detection of fecal contamination on Golden Delicious apples. The algorithms utilized the fluorescence intensities at four wavebands, 680, 684, 720, and 780 nm, for computation of simple functions for effective detection of contamination spots created on the apple surfaces using four concentrations of aqueous fecal dilutions. The algorithms detected more than 99% of the fecal spots. The effective detection of feces showed that a simple multispectral fluorescence imaging algorithm based on violet LED excitation may be appropriate to detect fecal contamination on high-speed apple processing lines. This fast and non-destructive method for detection of fecal contamination can be implemented in the food and agricultural industries to help in risk reduction and food safety assurance for preventing or minimizing the potential foodborne illness.

A line-scan spectral imaging system was developed for online identification of wholesome and unwholesome freshly slaughtered chicken carcasses on commercial processing lines. Hyperspectral images acquired by the imaging system for 5,549 wholesome and 93 unwholesome chicken carcasses on a 140 bird per minute (bpm) processing line were analyzed to optimize region of interest size and location and determine key wavebands by which to implement online multispectral inspection based on single-waveband intensity and a two-waveband ratio. Multispectral inspection algorithms were developed for real-time online chicken inspection. The imaging system inspected over 100,000 chickens on the processing line during continuous operation and accurately identified over 99% of wholesome and over 96% of unwholesome chickens. A system of this type can perform food safety inspection tasks accurately and consistently on high-speed processing lines (e.g., at least 140 bpm), to help poultry processors improve production efficiency and satisfy increasing consumer demand for poultry products. PRACTICAL APPLICATIONS The line-scan spectral imaging system was designed as a tool to assist poultry processors in meeting the requirements of the HACCP-Based Inspection Models Project as implemented by Food Safety and Inspection Service (FSIS). The system is particularly well suited for presorting poultry carcasses on high-speed processing lines by removing systemically diseased birds prior to the inspection stations. This can increase efficiency and reduce cross-contamination risks by minimizing the presence and unnecessary processing of unwholesome birds on the processing line. Real-time data collection and prompt removal of unwholesome poultry carcasses can enhance the ability of FSIS to certify U.S. poultry products for both domestic and export markets. In addition, the spectral imaging methodology of this system has the potential for easy adaptation to other high-speed food processing tasks, particularly those involving automated inspection for quality indicators.

Foodborne diseases are of serious concern to public health. It is necessary to develop fast and reliable non-destructive detection methods to improve food product monitoring for the food industry. This research was conducted to investigate hyperspectral fluorescence imaging using violet/blue LED excitation to develop a multispectral algorithm for detection of fecal contamination on Golden Delicious apples. From the hyperspectral image data, four wavebands, 680, 684, 720, and 780 nm, were selected for potential use in a multispectral detection algorithm. The algorithm could detect 96–100% of different dilutions of feces on apples. The highly successfully detection of feces showed that a simple multispectral fluorescence imaging algorithm based on violet/blue LED excitation can be used to detect fecal contamination on apple processing lines.

A spectral line-scan imaging system was developed for automated online wholesomeness inspection of broilers and evaluated in a commercial chicken processing plant. Real-time online hyperspectral images acquired by the system on a 140 bird-per-minute processing line were analyzed to optimize Region of Interest (ROI) size and location and to determine key wavebands by which to implement online high-speed multispectral inspection. Multispectral imaging algorithms were implemented to automatically recognize individual carcasses entering and exiting the field of view, to locate the ROI on the bird, and to determine the condition for each carcass as being wholesome or unwholesome. The high accuracy obtained from the in-plant evaluation results showed that the system can effectively perform food safety inspection tasks on high-speed processing lines. The system is being adapted for commercial use in pre-sorting chicken during initial processing operations, to help poultry processors improve production efficiency and satisfy increasing consumer demand for poultry products.

A machine vision system was developed and evaluated for the automation of online inspection to differentiate freshly slaughtered wholesome chickens from systemically diseased chickens. The system consisted of an electron-multiplying charge-coupled-device (EMCCD) camera used with an imaging spectrograph and controlled by a computer to obtain line-scan images quickly on a chicken processing line of a commercial poultry plant. The system scanned chicken carcasses on an eviscerating line operating at a speed of 140 chickens per minute. An algorithm was implemented in the system to automatically recognize individual carcasses entering and exiting the field of view, to locate the region of interest (ROI) of each chicken, to extract useful spectra from the ROI as inputs to the differentiation method, and to determine the condition for each carcass as being wholesome or systemically diseased. The system can acquire either hyperspectral or multispectral images without any cross-system calibration. The essential spectral features were selected from hyperspectral images of chicken samples. The differentiation of chickens on the processing line was then carried out using multispectral imaging. The high accuracy obtained from the evaluation results showed that the machine vision system can be applied successfully to automatic online inspection for chicken processing.

We have recently developed a rapid online line-scan imaging system capable of both hyperspectral Vis/NIR reflectance and fluorescence in the Vis with UV-A excitation. The hyperspectral online line-scan system was integrated with a commercial apple-sorting machine and evaluated to inspect apples for fecal contamination and defects at a processing line speed of over three apples per second. Results showed that fluorescence imaging (using a two-band ratio) could achieve detection of fecal spots on artificially contaminated apples with a 100% detection rate and no false positives regardless of the presence of defects. A NIR two-band reflectance ratio coupled with a simple classification method based on the mean intensity and homogeneity of the ratio achieved a 99.5% apple defect classification accuracy with a false positive rate of only 2%. The presented NIR processing regime overcame the presence of stem/calyx on apples that typically has been a problematic source for false positives in the detection of defects. The most significant and important outcome of this investigation is a line-scan inspection system that can potentially provide the capability for current sorting mechanisms, such as by size and color, as well as additional sorting for quality and safety attributes of food products. This line-scan based online imaging system offers great potential as a value-added dynamic inspection system due to its capability for multi-tasking to meet a variety of inspection objectives. A multi-tasking inspection system that can meet current industry sorting needs with the added benefit of safety inspection without requiring significant modification of existing infrastructure or incurring significant costs may lead the apple industry to consider adopting voluntary measures to further enhance safe production and processing of fruits.

The USDA Agricultural Research Service has developed a fast line-scan imaging system for differentiating wholesome and systemically diseased fresh chickens. The imaging system was used to acquire hyperspectral line-scan images of 250 chicken carcasses on a laboratory processing line moving at 70 birds per minute. A method appropriate for line-scan imaging was developed for automated sensing of birds and locating the Region of Interest (ROI) within the line-scan images most suited for differentiation. From analysis of wholesome and systemically diseased chicken spectra in the ROI, four key wavelengths for differentiating between wholesome and systemically diseased chickens were selected: 424, 465, 515, and 546 nm. The key wavelengths and their ratios with a reference wavelength (689 nm) were investigated for a fuzzy logic based differentiation algorithm. Classification using the key wavelengths correctly identified 98 and 95% of wholesome and systemically diseased chickens for model development, and 98 and 93% of wholesome and systemically diseased chickens for model testing. Although band ratios reduced variation within each chicken category, the resulting classification accuracies were not significantly improved over those for classification by key wavelengths.

This paper reports the chemometric analysis of near-infrared spectra drawn from hyperspectral images to develop, evaluate, and compare statistical models for the detection of beef in fish meal. There were 40 pure-fish meal samples, 15 pure-beef meal samples, and 127 fish/beef mixture meal samples prepared for hyperspectral line-scan imaging by a machine vision system. Spectral data for 3600 pixels per sample, in which individual spectra was obtain, were retrieved from the region of interest (ROI) in every sample image. The spectral data spanning 969 nm to 1551 nm (across 176 spectral bands) were analyzed. Statistical models were built using the principal component analysis (PCA) and the partial least squares regression (PLSR) methods. The models were created and developed using the spectral data from the purefish meal and pure-beef meal samples, and were tested and evaluated using the data from the ROI in the mixture meal samples. The results showed that, with a ROI as large as 3600 pixels to cover sufficient area of a mixture meal sample, the success detection rate of beef in fish meal could be satisfactory 99.2% by PCA and 98.4% by PLSR.

A fuzzy logic-based algorithm was developed for a hyperspectral line-scan imaging system for differentiation of wholesome and systemically diseased fresh chickens. The hyperspectral imaging system consisted of an electron-multiplying charge-coupled-device (EMCCD) camera and an imaging spectrograph. The imaging system acquired line-scan images of chicken carcasses as they passed through the pixel-wide vertical linear field of view. The chickens were hung on a closed-loop laboratory processing line moving at a speed of 70 birds per minute. The use of light-emitting-diode (LED) line lights was selected following comparative evaluation of LED and quartz-tungsten-halogen (QTH) line lights. From analysis of wholesome and systemically diseased chicken spectra, four key wavelengths for differentiating between wholesome and systemically diseased chickens were selected: 413, 472, 515, and 546 nm; a reference wavelength at 626 nm was also selected. The ratio of relative reflectance between each key wavelength and the reference wavelength was calculated for use as input image features. A fuzzy logic-based algorithm utilising the image features was developed to identify individual pixels on the chicken surface exhibiting symptoms of systemic disease. Two differentiation methods utilising the fuzzy logic-based algorithm were tested using two separate image sets, the first containing 65 wholesome and 74 systemically diseased chickens, and the second containing 48 wholesome and 42 systemically diseased chickens. The first method achieved 100% accuracy in identifying chickens in both image sets. The second method achieved 96% and 100% accuracy in identifying chickens in the first and second image sets, respectively.

Hyperspectral images of plots, cropped with silage or grain corn and cultivated with conventional tillage, reduced tillage, or no till, were classified using the classification and regression tree (C&RT) approach, an innovative intelligent computational algorithm in data mining. Each tillage/cropping combination was replicated three times, for a total of 18 plots. Five hyperspectral reflectance measurements per plot were taken randomly to obtain a total of 90 measurements. Images were taken on June 30, August 5, and August 25, 2000 to reflect three stages of crop development. Each measurement consisted of reflectances in 71 wave bands ranging from 400 to 950 nm. C&RT models were developed separately for the three observation dates, using the 71 reflectances as inputs to classify the image according to: (a) tillage practice, (b) residue level, (c) cropping practices, (d) tillage/cropping (residue) combination. C&RT models could generally distinguish tillage practices with a classification accuracy of 0.89 and residue levels with a classification accuracy of 0.98.

This paper introduces data mining technology designed to classify agricultural fields under different manure/fertiliser application strategies. During the summer of 2000, airborne hyperspectral data were collected three times at two field sites in southwestern Quebec, Canada. One field site contained 24 plots (20 m by 24 m) that were amended with manure treatments and planted with maize and soya beans. The second field site contained 18 plots (18·5 m by 75 m) that received chemical fertilisers and were planted with maize. Reflectances of 71 wave bands of hyperspectral data (400 nm for violet to 940 nm for near infrared) were collected from 5 subplots within each of the 42 plots. The decision-tree algorithm of data mining technology was used to distinguish between manure and chemical fertiliser treatments. The decision-tree algorithm divides the data to reduce the deviance, and classifies them into the pre-defined categories as many tree branches. The success of the classification rate was as high as 91% for the early planting season, 99% for the mid-planting season, and 95% for the late planting season. The accuracy of the results demonstrates that data mining technology could be used for remote-sensing imagery classification of fertiliser applications.

This paper reports the development of a multispectral algorithm, using the line-scan hyperspectral imaging system, to detect fecal contamination on leafy greens. Fresh bovine feces were applied to the surfaces of washed loose baby spinach leaves. A hyperspectral line-scan imaging system was used to acquire hyperspectral fluorescence images of the contaminated leaves. Hyperspectral image analysis resulted in the selection of the 666 nm and 688 nm wavebands for a multispectral algorithm to rapidly detect feces on leafy greens, by use of the ratio of fluorescence intensities measured at those two wavebands (666 nm over 688 nm). The algorithm successfully distinguished most of the lowly diluted fecal spots (0.05 g feces/ml water and 0.025 g feces/ml water) and some of the highly diluted spots (0.0125 g feces/ml water and 0.00625 g feces/ml water) from the clean spinach leaves. The results showed the potential of the multispectral algorithm with line-scan imaging system for application to automated food processing lines for food safety inspection of leafy green vegetables.

In this research, a multispectral algorithm derived from hyperspectral line-scan fluorescence imaging under violet LED excitation was developed for the detection of frass contamination on mature tomatoes. The algorithm utilized the fluorescence intensities at two wavebands, 664 nm and 690 nm, for computation of the simple ratio function for effective detection of frass contamination. The contamination spots were created on the tomato surfaces using four concentrations of aqueous frass dilutions. The algorithms could detect more than 99% of the 0.2 g/ml and 0.1 g/ml frass contamination spots and successfully differentiated these spots from clean tomato surfaces. The results demonstrated that the simple multispectral fluorescence imaging algorithms based on violet LED excitation can be appropriate to detect frass on tomatoes in high-speed post-harvest processing lines.

Korla fragrant pears are small oval pears characterized by light green skin, crisp texture, and a pleasant perfume for which they are named. Anatomically, the calyx of a fragrant pear may be either persistent or deciduous; the deciduouscalyx fruits are considered more desirable due to taste and texture attributes. Chinese packaging standards require that packed cases of fragrant pears contain 5% or less of the persistent-calyx type. Near-infrared hyperspectral imaging was investigated as a potential means for automated sorting of pears according to calyx type. Hyperspectral images spanning the 992-1681 nm region were acquired using an EMCCD-based laboratory line-scan imaging system. Analysis of the hyperspectral images was performed to select wavebands useful for identifying persistent-calyx fruits and for identifying deciduous-calyx fruits. Based on the selected wavebands, an image-processing algorithm was developed that targets automated classification of Korla fragrant pears into the two categories for packaging purposes.

This research developed and evaluated the multispectral algorithm derived from hyperspectral line-scan imaging using the machine vision system with an electron-multiplying-charge-coupled-device camera and an imaging spectrograph for the detection of defect Red Delicious apples and distinguish them from normal ones. The algorithm utilized the fluorescence intensities at three wavebands, 676 nm, 714 nm and 779 nm, for computation of a simple threshold function for effective detection of defect apples. The algorithm detected more than 95% of defect apples. The effective detection of defect apples showed that a simple multispectral detection algorithm can be appropriate to be implemented on fast-speed apple processing lines to help in risk reduction and food safety assurance for preventing or minimizing the potential foodborne illness.

This research developed and evaluated the multispectral algorithms derived from hyperspectral line-scan fluorescence imaging under violet LED excitation for detection of fecal contamination on Golden Delicious apples. The algorithms utilized the fluorescence intensities at four wavebands, 680 nm, 684 nm, 720 nm, and 780 nm, for computation of simple functions for effective detection of contamination spots created on the apple surfaces using four concentrations of aqueous fecal dilutions. The algorithms detected more than 99% of the fecal spots. The effective detection of feces showed that a simple multispectral fluorescence imaging algorithm based on violet LED excitation may be appropriate to detect fecal contamination on fast-speed apple processing lines.

A line-scan machine vision system and multispectral inspection algorithm was developed and evaluated for differentiation of wholesome and systemically diseased chickens on a high-speed processing line. The inspection system acquires line-scan images of chicken carcasses on a 140 bird/min processing line and is able to automatically detect individual birds entering and exiting the field of view of the camera, locate a specified region of interest for spectral image analysis, and produce a decision output for each bird. The same spectral line-scan imaging system was used for hyperspectral data acquisition-analysis to develop the multispectral detection and differentiation algorithm and for multispectral implementation of the algorithm for real-time on-line inspection on the processing line. Results showed that effective multispectral inspection could be achieved by analysis of a selected region of interest across the breast area from images at the 580- and 620-nm wavebands. Overall system performance was evaluated during two 8-h shifts in which the system inspected over 100,000 chickens, with system results compared with Food Safety and Inspection Service inspector tallies of wholesome and systemically diseased birds for that same time period. During system verification, the system accurately classified wholesome and systemically diseased chickens that were observed by a veterinarian posted beside the system to perform real-time identifications of the same birds. The high accuracy of the results demonstrated that the spectra line-scan imaging system and multispectral detection and differentiation algorithm can be effectively used for on-line high-speed presorting applications for young broiler chickens.

This paper reported the development of hyperspectral fluorescence imaging system using ultraviolet-A excitation (320-400 nm) for detection of bovine fecal contaminants on the abaxial and adaxial surfaces of romaine lettuce and baby spinach leaves. Six spots of fecal contamination were applied to each of 40 lettuce and 40 spinach leaves. In this study, the wavebands at 666 nm and 680 nm were selected by the correlation analysis. The two-band ratio, 666 nm / 680 nm, of fluorescence intensity was used to differentiate the contaminated spots from uncontaminated leaf area. The proposed method could accurately detect all of the contaminated spots.

A line-scan spectral imaging system was developed for online identification of wholesome and unwholesome freshly slaughtered chicken carcasses on commercial processing lines. Hyperspectral images acquired by the imaging system for 5,549 wholesome and 93 unwholesome chicken carcasses on a 140 bird per minute (bpm) processing line were analyzed to optimize region of interest size and location and determine key wavebands by which to implement online multispectral inspection based on single-waveband intensity and a two-waveband ratio. Multispectral inspection algorithms were developed for real-time online chicken inspection. The imaging system inspected over 100,000 chickens on the processing line during continuous operation and accurately identified over 99% of wholesome and over 96% of unwholesome chickens. A system of this type can perform food safety inspection tasks accurately and consistently on high-speed processing lines (e.g., at least 140 bpm), to help poultry processors improve production efficiency and satisfy increasing consumer demand for poultry products. PRACTICAL APPLICATIONS The line-scan spectral imaging system was designed as a tool to assist poultry processors in meeting the requirements of the HACCP-Based Inspection Models Project as implemented by Food Safety and Inspection Service (FSIS). The system is particularly well suited for presorting poultry carcasses on high-speed processing lines by removing systemically diseased birds prior to the inspection stations. This can increase efficiency and reduce cross-contamination risks by minimizing the presence and unnecessary processing of unwholesome birds on the processing line. Real-time data collection and prompt removal of unwholesome poultry carcasses can enhance the ability of FSIS to certify U.S. poultry products for both domestic and export markets. In addition, the spectral imaging methodology of this system has the potential for easy adaptation to other high-speed food processing tasks, particularly those involving automated inspection for quality indicators.

A machine vision system was developed and evaluated for the automation of online inspection to differentiate freshly slaughtered wholesome chickens from systemically diseased chickens. The system consisted of an electron-multiplying charge-coupled-device (EMCCD) camera used with an imaging spectrograph and controlled by a computer to obtain line-scan images quickly on a chicken processing line of a commercial poultry plant. The system scanned chicken carcasses on an eviscerating line operating at a speed of 140 chickens per minute. An algorithm was implemented in the system to automatically recognize individual carcasses entering and exiting the field of view, to locate the region of interest (ROI) of each chicken, to extract useful spectra from the ROI as inputs to the differentiation method, and to determine the condition for each carcass as being wholesome or systemically diseased. The system can acquire either hyperspectral or multispectral images without any cross-system calibration. The essential spectral features were selected from hyperspectral images of chicken samples. The differentiation of chickens on the processing line was then carried out using multispectral imaging. The high accuracy obtained from the evaluation results showed that the machine vision system can be applied successfully to automatic online inspection for chicken processing.

A machine vision system was developed and evaluated for automation of online inspection to differentiate freshly slaughtered wholesome chickens from systemically diseased ones. The system consisted of an electron-multiplying charge-coupled-device (EMCCD) camera and an imaging spectrograph to obtain line-scan images quickly in a processing line of a poultry plant. The system scanned the surfaces of freshly slaughtered chicken carcasses on an eviscerating line at a speed of 140 chickens per minute. An algorithm was implemented in the system to automatically recognize carcasses entering and exiting the field of view, to locate the region of interest (ROI) of the chicken surface, to extract useful spectra from the ROI as inputs to the differentiation method, and to determine the chicken condition for each carcass. The system can take either hyperspectral or multispectral images without any cross-system calibration. The essential spectral features were selected from hyperspectral images of chicken samples. The differentiation of chickens on the processing line was carried out using multispectral imaging. The high accuracy obtained from the evaluation results showed that the machine vision system can be applied successfully to automatic online inspection for chicken processing.

A hyperspectral-multispectral line-scan imaging system was developed for differentiation of wholesome and systemically diseased chickens. In-plant testing was conducted for chickens on a commercial evisceration line moving at a speed of 70 birds per minute. Hyperspectral image data was acquired for a calibration data set of 543 wholesome and 64 systemically diseased birds and for a testing data set of 381 wholesome and 100 systemically diseased birds. The calibration data set was used to develop the parameters of the imaging system for conducting multispectral inspection based on fuzzy logic detection algorithms using selected key wavelengths. Using a threshold of 0.4 for fuzzy output decision values, multispectral classification was able to achieve 90.6% accuracy for wholesome birds and 93.8% accuracy for systemically diseased birds in the calibration data set and 97.6% accuracy for wholesome birds and 96.0% accuracy for systemically diseased birds in the testing data set. By adjusting the classification threshold, 100% accuracy was achieved for systemically diseased birds with a decrease in accuracy for wholesome birds to 88.7%. This adjustment shows that the system can be feasibly adapted as needed for implementation for specific purposes, such as paw harvesting operations or prescreening for food safety inspection. This line-scan imaging system is ideal for directly implementing multispectral classification methods developed from hyperspectral image analysis.

During in-plant testing of a hyperspectral line-scan imaging system, images were acquired of wholesome and systemically diseased chickens on a commercial processing line moving at a speed 70 birds per minute. A fuzzy logic based algorithm using four key wavelengths, 468 nm, 501 nm, 582 nm, 629 nm, was developed using image data from the validation set of images of 543 wholesome and 66 systemically diseased chickens. A classification method using the fuzzy logic based algorithm was then tested on the testing set of images of 457 wholesome and 37 systemically diseased chickens, as well as 80 systemically diseased chickens that were imaged off-shift during breaks between normal processing shifts of the chicken plant. The classification method correctly identified 89.7% of wholesome chicken images and 98.5% of systemically diseased chicken images in the validation set. For the testing data set, the method correctly classified 96.7 % of 457 wholesome chicken images and 100% of 37 systemically diseased chicken images. The 80 images acquired off-shift were also 100% correctly identified.

We have developed nondestructive opto-electronic imaging techniques for rapid assessment of safety and wholesomeness of foods. A recently developed fast hyperspectral line-scan imaging system integrated with a commercial apple-sorting machine was evaluated for rapid detection of animal feces matter on apples. Apples obtained from a local orchard were artificially contaminated with cow feces. For the online trial, hyperspectral images with 60 spectral channels, reflectance in the visible to near infrared regions and fluorescence emissions with UV-A excitation, were acquired from apples moving at a processing sorting-line speed of three apples per second. Reflectance and fluorescence imaging required a passive light source, and each method used independent continuous wave (CW) light sources. In this paper, integration of the hyperspectral imaging system with the commercial applesorting machine and preliminary results for detection of fecal contamination on apples, mainly based on the fluorescence method, are presented.

An online line-scan imaging system was developed for differentiation of wholesome and systemically diseased chickens. The hyperspectral imaging system used in this research can be directly converted to multispectral operation and would provide the ideal implementation of essential features for data-efficient high-speed multispectral classification algorithms. The imaging system consisted of an electron-multiplying charge-coupled-device (EMCCD) camera and an imaging spectrograph for line-scan images. The system scanned the surfaces of chicken carcasses on an eviscerating line at a poultry processing plant in December 2005. A method was created to recognize birds entering and exiting the field of view, and to locate a Region of Interest on the chicken images from which useful spectra were extracted for analysis. From analysis of the difference spectra between wholesome and systemically diseased chickens, four wavelengths of 468 nm, 501 nm, 582 nm and 629 nm were selected as key wavelengths for differentiation. The method of locating the Region of Interest will also have practical application in multispectral operation of the line-scan imaging system for online chicken inspection. This line-scan imaging system makes possible the implementation of multispectral inspection using the key wavelengths determined in this study with minimal software adaptations and without the need for cross-system calibration.

A hyperspectral line-scan imaging system for automated inspection of wholesome and diseased chickens was developed and demonstrated. The hyperspectral imaging system consisted of an electron-multiplying charge-coupled-device (EMCCD) camera and an imaging spectrograph. The system used a spectrograph to collect spectral measurements across a pixel-wide vertical linear field of view through which moving chicken carcasses passed. After a series of image calibration procedures, the hyperspectral line-scan images were collected for chickens on a laboratory simulated processing line. From spectral analysis, four key wavebands for differentiating between wholesome and systemically diseased chickens were selected: 413 nm, 472 nm, 515 nm, and 546 nm, and a reference waveband, 622 nm. The ratio of relative reflectance between each key wavelength and the reference wavelength was calculated as an image feature. A fuzzy logic-based algorithm utilizing the key wavebands was developed to identify individual pixels on the chicken surface exhibiting symptoms of systemic disease. Two differentiation methods were built to successfully differentiate 72 systemically diseased chickens from 65 wholesome chickens.

The hyperspectral imagery data were collected from corn and soybean fields from 2001 to 2002 after the seeding, using different hand-held hyperspectral sensors. In data collection, the reflections of visible and near-infrared wavebands were taken from several spots of the fields. The site-specific crop species and yields were recorded for each spot of the fields. Several algorithms were developed and applied to process the data for proper wavelength selection and yield prediction. The algorithms present promising potential for the application in precision agriculture.

This paper introduces data mining technology designed to classify agricultural fields under different manure/fertilizer application strategies. During the summer of 2000, airborne hyperspectral data was collected three times at two field sites in southwestern Quebec, Canada. One field site contained 24 plots (20 m x 24 m) that were amended with manure treatments and planted with corn and soybeans. The second field site contained 18 plots (18.5 m x 75 m) that received chemical fertilizers and were planted with corn. Reflectances of 72 wave bands of hyperspectral data (400 nm for violet to 950 nm for near infrared) were collected from five subplots within each of the 42 plots. The decision tree algorithm of data mining technology was used to distinguish between manure and chemical fertilizer treatments. The success of the classification rate was as high as 91% for the early planting season, 99% for the mid planting season, and 95% for the late planting season. The accuracy of the results, demonstrates that data mining technology could be used for remote sensing imagery classification of fertilizer applications.