A Novel Method for Color Measurement of Cotton Fiber

Měření barevnosti bavlněných vláken je velmi zajímavou vlastností těchto vláken a hraje důležitou roli v klasifikování bavlny. Obecně užívané parametry barevnosti bavlněných vláken jsou Rd (odrazivost) a +b (žlutost). Tyto parametry jsou měřeny pomocí HVI (High Volume Instrument). Bavlněné barevné standardy jsou keramické dlaždice a bavlněné vzorky produkované USDA. Cílem výzkumu je využití LED světelných zdrojů při měření barevnosti bavlny. Běžně používané světelné zdroje pro měření barevnosti jsou xenony a žárovky. LED zdroje mají potenciální výhody oproti běžně užívaným světelným zdrojům, protože jsou energeticky účinnější, umožňují delší pracovní dobu, jsou bezpečnější a šetrnější k životnímu prostředí. Bezkontaktní metoda měření se používá ze specifické měřící vzdálenosti. Tato metoda umožňuje měření barevnosti bavlny s velkou přesností vůči minimální ploše měřeného povrchu. Hodnoty barevnosti a jasu měřené bezkontaktní metodou měření jsou hypoteticky uspořádané jako vizuální hodnocení. Bezkontaktní metoda se také využívá pro hodnocení barevných změn. Barevnost bavlny je navíc ovlivněna i tím, vyskytují-li se na jeho povrchu odpadové částice. Tyto částice ovlivňují i instrumentální měření bavlněných vzorků. V bavlnářském průmyslu se k hodnocení bavlny používá vizuální technika zahrnující vizuální posudky. Toto hodnocení je spolehlivější v tom smyslu, že lidský zrak nebere v potaz částice na povrchu vláken a dává tak barevnost pouze v oblasti bavlny. Další užívanou možností pro hodnocení barevnosti bavlny je obrazová analýza, která umožňuje odstranit zbytky částic na povrchu a poskytuje tak barevnost čisté bavlny. Podle průmyslového hlediska jsou však vizuální posudky a přístrojové hodnocení v poměrně vysoké neshodě. Nicméně bylo vynaloženo značné úsilí pro snížení této neshody mezi způsoby hodnocení. Přesto je konečné hodnocení prováděno na základě vizuálního hodnocení. Pro techniku prahování v rámci obrazové analýzy se používají tři hodnoty, a to hodnoty světlosti L*, čistoty C* a odstínu H* bavlny. Vizuální hodnocení se provádí vůči standardům USDA pro barevné třídění bavlny. Standardy USDA se používají pro hodnocení barevnosti bavlny. Vizuální hodnocení je porovnáváno technikou prahování. Bylo dosaženo uspokojivých výsledků s jasným snížením neshody mezi vizuálním a instrumentálním hodnocením. Cíle výzkumu je dosaženo vytvořením zlepšeného systému pro měření barevnosti pro třídění bavlny.The color measurement of the cotton fiber is very important property of the cotton fiber and it plays important role in grading of the cotton. Globally used color parameters of the cotton fiber are Rd and +b. These parameters are measured by HVI (High volume instrument). Cotton color standards are ceramic tiles and cotton samples which are provided by USDA. The focus of the research is the utilization of the LEDs as a light source in the cotton color measurement system. Conventional lighting used for cotton color measurement is xenon and incandescent. LEDs have potential benefits over the conventional lighting system as these are more energy efficient, offers more working hours, safer and environment friendly. Non-contact method is used from a specific distance. This method enables to measure the cotton color with immense precision due to the minimum area of the surface used for the measurement. The chromaticity and luminance values measured through the no-contact method are hypothetically arrangement of visual assessment. Non-contact method is also used for the evaluation of the color variation. Cotton color representation can be misleading in a way that the surface of the cotton sample contains the trash particles. As far as the instrumental measurement of cotton color is concerned the presence of these trash particles is a big obstacle in the way of exact measurement of cotton sample. But, cotton industry also uses visual inspection technique for the color measurement of cotton. This technique involves the human assessment. It is more reliable in a sense that the human assessment does not take into consideration the trash particles and gives the color values only of the cotton region. Image processing technique is used in my research work which enables us to eliminate the trash particles from the surface of the cotton and gives only the color of the cotton region. According to the industrial point of view the disagreement between the visual assessment of cotton color and instrumental assessment of the cotton color measurement is quite high. Although, lots of efforts have been made to minimize this disagreement but still, the final grading is performed on the basis of visual assessment. Thresholding technique is used for the trash segmentation. Three regions L* (Lightness), C*(Chroma), H* (Hue) is used for the thresholding technique. This Visual assessment is performed according to the USDA standards for the cotton color grading. USDA cotton samples are also used for the assessment of the cotton color. And the visual assessment is compared with the thresholding technique. Satisfactory results are obtained with a clear reduction of the disagreement between visual assessment and instrumental measurement. The objective of the research is achieved by developing an improved color measurement system for cotton grading

Using Computer Vision to Build a Predictive Model of Fruit Shelf-life

Computer vision is becoming a ubiquitous technology in many industries on account of its speed, accuracy, and long-term cost efficacy. The ability of a computer vision system to quickly and efficiently make quality decisions has made computer vision a popular technology on inspection lines. However, few companies in the agriculture industry use computer vision because of the non-uniformity of sellable produce. The small number of agriculture companies that do utilize computer vision use it to extract features for size sorting or for a binary grading system: if the piece of fruit has a certain color, certain shape, and certain size, then it passes and is sold. If any of the above criteria are not met, then the fruit is discarded. This is a highly wasteful and relatively subjective process. This thesis proposes a process to undergo to use computer vision techniques to extract features of fruit and build a model to predict shelf-life based on the extracted features. Fundamentally, the existing agricultural processes that do use computer vision base their distribution decisions on current produce characteristics. The process proposed in this thesis uses current characteristics to predict future characteristics, which leads to more informed distribution decisions. By modeling future characteristics, the process proposed will allow fruit characterized as “unfit to sell” by existing standards to still be utilized (i.e. if the fruit is too ripe to ship across the country, it can still be sold locally) which decreases food waste and increases profit. The process described also removes the subjectivity present in current fruit grading systems. Further, better informed distribution decisions will save money in storage costs and excess inventory. The proposed process consists of discrete steps to follow. The first step is to choose a fruit of interest to model. Then, the first of two experiments is performed. Sugar content of a large sample of fruit are destructively measured (using a refractometer) to correlate sugar content to a color range. This step is necessary to determine the end-point of data collection because stages of ripeness are fundamentally subjective. The literature is consulted to determine “ripe” sugar content of the fruit and the first experiment is undertaken to correlate a color range that corresponds to the “ripe” sugar content. This feature range serves as the end-point of the second experiment. The second experiment is large-scale data collection of the fruit of interest, with features being recorded every day, until the fruit reaches end-of-life as determined by the first experiment. Then, computer vision is used to perform feature extraction and features are recorded over each sample fruit’s lifetime. The recorded data is then analyzed with regression and other techniques to build a model of the fruit’s shelf-life. The model is finally validated. This thesis uses bananas as a proof of concept of the proposed process

Computer Vision System as a Tool to Estimate Pork Marbling

Currently pork marbling is assessed subjectively in the industry, because of the limited methods and tools that are suitable for the industry. In this dissertation, we are devoted to develop a computer vision system for objective measurement of pork which suits the industrial needs. Experiment 1 examined the possibility of using computer vision system (CVS) to predict marbling in a lab-based experiment using pork samples that were already trimmed of subcutaneous fat and connective tissue. Experiment 2 an industrial scale CVS was built to predict the 3rd and 10th rib pork chop’s marbling. Experiment 3 the industrial scale CVS was tested in the meat plant and images of whole boneless pork loin were collected. The CVS predicted marbling were compared with subjective marbling score using crude fat percentage (CF%) as standard. In experiment 1 subjective marbling score had a correlation of 0.81 with CF% while CVS had a 0.66 correlation. CVS has shown an accuracy of 63% for stepwise regression model and 75% for support vector machine model. These results indicate that CVS has the potential to be used as an tool to predict pork intramuscular fat (IMF)%. In experiment 2 the accuracy of CVS predicting pork chop CF% was 68.6% and subjective marbling was 70.1%. A drop of accuracy in predicting anterior chop CF% for both CVS and objective marbling score was observed when compared to posterior chop, this suggest that there is a discrepancy in accuracy between the anatomy location of samples collected. In experiment 3 the accuracy of CVS predicting boneless whole loin was 58.6% and subjective marbling score was 53.3%. In this research, CVS has demonstrated a consistency of accuracies using different pork samples. CVS has shown higher accuracy when predicting whole boneless loin IMF% when compared to subjective assessment.National Pork BoardColeman Natura

What does beef taste like?

Today, there is no general terminology to describe the sensory properties of beef. Hence, the aim of this study was to develop a terminology to describe the sensory texture, flavour and appearance properties of beef. Attributes describing beef were chosen from earlier studies and a qualitative open discussion. The sensory quantitative method that were used to validate the words chosen from the open discussion were CATA, “Catch-all-that-apply” with 30 respondents. Colour- and pH measurements were also performed to see if there were any significant differences between the meat samples and if there were any correlations between pH and colour values and the generated terminology. The muscle that was analyzed were Strip Loin (M. longissimus dorsi) from four cattle. The terminology established by the 30 respondents generated 7 texture-, 7 flavour- and 9 appearance attributes. The most common flavour attributes, that over 50 percent of the panelists seemed to perceive in at least one of the four meat samples during the sensory method CATA, were umami, mineral, moderate meaty, nutty, buttery, mellow and mature. The texture and appearance attributes generated in the same way was juicy, tough, tender, firm, soft, dense- and porous fiber structure, clear and dark red, homogeneous colour, poorly-, moderate- and richly marbled, fibery, moist and smooth. The pH analysis showed no statistical difference between the samples. The colour analysis on the other hand did show a statistical difference. Any correlation between the terminology and the obtained values from pH and colour measurements could not be found.Idag finns det ingen generell terminologi för att beskriva de sensoriska egenskaperna för nötkött. Målet med denna studie var att utveckla en terminologi för att beskriva den sensoriska texturen, smaken och utseendet av nötkött. Attributen som valdes för att beskriva köttet hade sitt ursprung från tidigare studier och en kvalitativ öppen diskussion. Den sensoriska kvantitativa metoden som användes för att validera orden valda från den öppna diskussionen var CATA, ”Catch-all-that-apply” med 30 respondenter. pH- och färgmätningar utfördes även för att se om det var några signifikanta skillnader mellan proven och om det fanns några korrelationer mellan pH- och färgresultat samt den genererade terminologin. Muskeln som analyserades var ryggbiff (M. longissimus dorsi) från fyra nötkreaturdjur. Terminologin som fastställdes av de 30 respondenterna genererade 7 textur-, 7 smak- och 9 utseendeattribut. De vanligaste smakattributen som över 50 procent av panellisterna tyckte sig känna i minst ett av de fyra köttproven under den sensoriska CATA-metoden var umami, mineral, måttlig köttsmak, nötig, smörig, mogen och mustig. De texturattribut som genererades på samma sätt var saftig, seg, mör, fast, mjuk, tät- och gles/porös fiberstruktur. Utseendeattributen som genererades var klarröd, mörkröd, homogen färg, svagt-, måttligt-, och rikligt marmorerad, fuktig, fibrig och slät. pH analysen visade inte på någon statistiskt signifikant skillnad mellan köttet från de olika proverna, medan färganalysen gjorde det. Någon korrelation mellan terminologin och de erhållna pH- och färgvärdena kunde inte hittas

Bayesian Methods for Radiometric Calibration in Motion Picture Encoding Workflows

A method for estimating the Camera Response Function (CRF) of an electronic motion picture camera is presented in this work. The accurate estimation of the CRF allows for proper encoding of camera exposures into motion picture post-production workflows, like the Academy Color Encoding Specification (ACES), this being a necessary step to correctly combine images from different capture sources into one cohesive final production and minimize non-creative manual adjustments. Although there are well known standard CRFs implemented in typical video camera workflows, motion picture workflows and newer High Dynamic Range (HDR) imaging workflows have introduced new standard CRFs as well as custom and proprietary CRFs that need to be known for proper post-production encoding of the camera footage. Current methods to estimate this function rely on the use of measurement charts, using multiple static images taken under different exposures or lighting conditions, or assume a simplistic model of the function’s shape. All these methods become problematic and tough to fit into motion picture production and post-production workflows where the use of test charts and varying camera or scene setups becomes impractical and where a method based solely on camera footage, comprised of a single image or a series of images, would be advantageous. This work presents a methodology initially based on the work of Lin, Gu, Yamazaki and Shum that takes into account edge color mixtures in an image or image sequence, that are affected by the non-linearity introduced by a CRF. In addition, a novel feature based on image noise is introduced to overcome some of the limitations of edge color mixtures. These features provide information that is included in the likelihood probability distribution in a Bayesian framework to estimate the CRF as the expected value of a posterior probability distribution, which is itself approximated by a Markov Chain Monte Carlo (MCMC) sampling algorithm. This allows for a more complete description of the CRF over methods like Maximum Likelihood (ML) and Maximum A Posteriori (MAP). The CRF function is modeled by Principal Component Analysis (PCA) of the Database of Response Functions (DoRF) compiled by Grossberg and Nayar, and the prior probability distribution is modeled by a Gaussian Mixture Model (GMM) of the PCA coefficients for the responses in the DoRF. CRF estimation results are presented for an ARRI electronic motion picture camera, showing the improved estimation accuracy and practicality of this method over previous methods for motion picture post-production workflows

Pixel classification methods for identifying and quantifying leaf surface injury from digital images

Plants exposed to stress due to pollution, disease or nutrient deficiency often develop visible symptoms on leaves such as spots, colour changes and necrotic regions. Early symptom detection is important for precision agriculture, environmental monitoring using bio-indicators and quality assessment of leafy vegetables. Leaf injury is usually assessed by visual inspection, which is labour-intensive and to a consid- erable extent subjective. In this study, methods for classifying individual pixels as healthy or injured from images of clover leaves exposed to the air pollutant ozone were tested and compared. RGB images of the leaves were acquired under controlled conditions in a laboratory using a standard digital SLR camera. Different feature vectors were extracted from the images by including different colour and texture (spa- tial) information. Four approaches to classification were evaluated: (1) Fit to a Pattern Multivariate Image Analysis (FPM) combined with T2 statistics (FPM-T2) or (2) Residual Sum of Squares statistics (FPM-RSS), (3) linear discriminant analysis (LDA) and (4) K-means clustering. The predicted leaf pixel classifications were trained from and compared to manually segmented images to evaluate classification performance. The LDA classifier outperformed the three other approaches in pixel identification with significantly higher accuracy, precision, true positive rate and F-score and significantly lower false positive rate and computation time. A feature vector of single pixel colour channel intensities was sufficient for capturing the information relevant for pixel identification. Including neighbourhood pixel information in the feature vector did not improve performance, but significantly increased the computation time. The LDA classifier was robust with 95% mean accuracy, 83% mean true positive rate and 2% mean false positive rate, indicating that it has potential for real-time applications.Opstad Kruse, OM.; Prats Montalbán, JM.; Indahl, UG.; Kvaal, K.; Ferrer Riquelme, AJ.; Futsaether, CM. (2014). Pixel classification methods for identifying and quantifying leaf surface injury from digital images. Computers and Electronics in Agriculture. 108:155-165. doi:10.1016/j.compag.2014.07.010S15516510

Prediction of meat tenderness using high resolution imaging

2010 Fall.Includes bibliographical references.Tenderness plays an important role in the sensory attributes of beef products. The objective of this study was to obtain the highest quality and resolution images of cross-sections of beef Longissimus dorsi surfaces that could likely be replicated in a commercial environment; and, to develop algorithms and regression equations that predict aged beef shear force. Fifty carcasses were identified at each of three commercial beef processing facilities in Colorado, Nebraska and Texas (total N = 150). A-maturity carcasses were selected to fill an equal distribution over the entire range of beef marbling scores; 1/3 of carcasses represented marbling scores from Practically Devoid 00 to Slight 40, 1/3 from Slight 50 to Small 90 and 1/3 from Modest 00 or higher. Carcasses derived from cattle supplemented with Zilpaterol hydrochloride (n = 25, based on harvest facility records) were identified as such. Samples were excised from the Longissimus muscle immediately posterior to the 12 th /13 th rib interface and imaged using the Tenera Technology High Resolution Imaging System; in addition, reflectance measurements (L*, a*, b*) were obtained. Samples were aged for either 7 or 14 days prior to freezing. Steaks were fabricated from frozen samples for Warner-Bratzler shear force (WBSF) determination. Images were analyzed using the custom developed Tenera Technology ZARMT software program, generating 10 output variables (diaSml, propSml, diaLrg, propLrg, ratDia, ratProp, medDia, medProp, diaNormMax and propNormMax) thought to represent ultra-structural characteristics of muscle such as fiber diameter, proportion of large versus small fibers and predominant size of muscle fiber within a given sample, which have previously been associated with beef tenderness (Hiner et al., 1953; Tuma et al., 1962; Herring et al., 1965; Cooper et al., 1968). In 14d aged steaks from harvest facility one, the use of high resolution variables explained an additional 11% of the variation in WBSF value over the use of marbling and color variables alone. Within harvest facility two and three, high resolution variables allowed for explanation of an additional 25% and 17% of the variation in 14d WBSF respectively. For samples aged 7d, high resolution variables allowed for explanation of an additional 8%, 14% and 34% of the variation in WBSF values of steaks from harvest facility one, two and three respectively. Fourteen days postmortem, inclusion of high resolution variables improved classification of "tender" steaks (WBSF less than or equal to 3.7, Platter et al., 2003a) 40%, -3% and 7% from harvest facility one, two and three respectively. Classification of "tough" steaks (WBSF greater than 3.7, Platter et al., 2003a) within steaks aged 14d was improved by -10%, 0% and 0% through use of high resolution variables. In classification of "tough" versus "tender" steaks 7d postmortem, equations containing high resolution variables correctly classified an additional 6%, 14.3% and 7.1% of "tender" steaks and 0%, -5.9% and 9.1% of "tough" steaks from harvest facility one, two and three respectively. Compared with the use of marbling and reflectance measurements alone, the use of high resolution variables improved the ability to explain WBSF at 7d and 14d, as well as in the designation of "tough" and "tender" steaks/carcasses, suggesting this technology, or one measuring similar traits could improve the assurance of tender beef products at the consumer level

Review: computer vision applied to the inspection and quality control of fruits and vegetables

This is a review of the current existing literature concerning the inspection of fruits and vegetables with the application of computer vision, where the techniques most used to estimate various properties related to quality are analyzed. The objectives of the typical applications of such systems include the classification, quality estimation according to the internal and external characteristics, supervision of fruit processes during storage or the evaluation of experimental treatments. In general, computer vision systems do not only replace manual inspection, but can also improve their skills. In conclusion, computer vision systems are powerful tools for the automatic inspection of fruits and vegetables. In addition, the development of such systems adapted to the food industry is fundamental to achieve competitive advantages