Moisture Dependent Physical Properties of Dragon’s Head Seeds (Lallemantia iberica)

Physical properties of Dragon's head seed, and other grains, are necessary for the design of equipment handling, transporting, processing and storing of the crop. A range of physical properties of Dragon's head (Lallemantia iberica) were determined as a function of moisture content. As the moisture content increased from 7.18 to 48.46 % (d.b.), the average length, width, thickness and the geometric mean diameter varied from 4.48 to 4.97 mm, 1.69 to 1.85 mm, 1.16 to 1.35 mm and 1.97 to 2.31 mm, respectively. In the same moisture range, studies on Dragon's head showed that, sphericity, surface area, thousand seed mass and true density increased from 41.73 to 46.50 %, 12.16 to 16.79 mm2 and 9.75 to 11.17 g and 837.2 to 1047 kg/m3, respectively. As the moisture content increased from 7.18 to 48.46 % (d.b.), bulk density decreased from 584.23 to 438.26 kg/m3 whereas the angle of repose and porosity increased from 27.16 to 43.33º and 30.17 to 58.12 %, respectively. The static coefficient of friction of Dragon's head seeds increased linearly against surfaces of three structural materials, namely, glass (0.25-0.63), plywood (0.42-0.70), and galvanized iron (0.32-0.64) as the moisture content increased from 7.18 % to 48.46 % (d.b.)

Virtual phenomics - use of robots and drones in combination with genomics accelerate genetic gains in wheat breeding

Wheat breeding is a tedious process that usually takes 10-15 years and depends heavily on the ability to identify superior progeny lines by visual inspection and manual scoring of traits. Two emerging technologies are now offering potential for more precise selection and faster genetic gains: genomic prediction of breeding values based on genome-wide SNP markers and use of high throughput phenotyping technologies. In the innovation project “Reliable and efficient high-throughput phenotyping to accelerate genetic gains in Norwegian plant breeding (virtual phenomics; vPheno), 2017-2022” we are combining multispectral imaging with genomic prediction. This is a collaborative project between the industry partners Graminor AS and Making View AS and world-leading research groups in genetics, robotics and image analysis at the Norwegian University of Life Sciences, Boston University and the International Maize and Wheat Improvement Center (CIMMYT) in Mexico. In order to follow the growth of the plants during the season and calculating vegetation indices that can be used to predict grain yield, the project makes use of drones fitted with multispectral camera that are flown at weekly interval during the field season. In addition, a custom-built field robot is being used for gathering close-up images of field plots that will be used for counting the number of heads per square meter and other plant features that cannot be reliably recognized from drone images. One major use of the data is to improve the precision of genomic prediction models, the other is to enable plant breeders to visit field trials in "virtual reality", by integrating information from the drone and robot images with other available data on the field plots (grain yield, disease resistance, quality traits, marker data etc.). A prototype of the VR tool will be presented along with the progress on improving grain yield prediction by use of the multispectral drone images.Supporting documentatio

Determination of moisture content and contaminated blank dried figs (Ficus carica L.) using dielectric property and artificial neural network

Abstract Dried figs are a garden produce that must be graded after harvesting. Moisture levels and contaminated blank are two of the most critical effective elements on the marketability of dried figs, and they are highly related to fig quality. In the present research, an intelligent system was employed to classify dried figs based on moisture content levels and infected blank fruits. Capacitance characteristics, average diameter, and fruit area were all taken into account in this study. The dried fig dielectric constant was measured at six different frequency levels: 12, 22, 32, 42, 52, and 62 MHz. The best frequency was then chosen using the improved distance evaluation feature selection approach. Image processing was also used to determine the average diameter and area of the figures. Following that, the dielectric constant of the most effective frequency, the average diameter, and the area of the fruit were used as input parameters in the artificial neural network classification model to classify and describe the moisture and porosity level of the dried fig. The most essential dielectric constant information relating to moisture and porosity level was at frequencies of 22 and 52 MHz, respectively. Finally, classification accuracy of 95.7% for moisture and 91.3% for porosity level was attained. The results demonstrated the excellent performance and capabilities of the proposed approach for rating the internal quality of dried figs

Application of Image Analysis Combined with Computational Expert Approaches for Shrimp Freshness Evaluation

This study was aimed to evaluate the freshness and quality of cultured shrimp (litopenaeus vannamei) during 9 days of storage on ice (i.e., at a temperature of 0°C) using image processing technique. A lighting chamber was used to provide uniform conditions for illumination. The shrimp freshness was evaluated using computer vision technique through color changes of head, legs and tail of the harvested shrimps. Thirty-six color parameters of the images such as mean and variance of red (r), green (g), blue (b), lightness hue (h), saturation (s), value (v), luma information (i and y), the luma component (y), chroma component (cr), lightness (L*), redness (a*), yellowness (b*), chroma (c), and hue (h) were analyzed. Some parameters, such as b*, from side pictures and r mean, b variance, v mean, y mean, b* mean and (L*) mean from top pictures changed with a rather similar trend during the storage period. Different computational expert approaches such as linear discriminant analysis, quadratic discriminant analy..

Detection of Honey Adulteration using Hyperspectral Imaging

This study investigates the application of hyperspectral imaging system and data mining based classifiers for honey adulteration detection. Hyperspectral images from pure and adulterated samples were captured in using a VIS-NIR hyperspectral camera (400 – 1000 nm). After preprocessing the images, five different data mining based techniques, including artificial neural network (ANN), support vector machine (SVM), Linear discriminant analysis (LDA), Fisher and Parzen classifiers were applied for supervised image classification. Classifier test results show the highest classification accuracy of 95% for ANN classifier. Other classifiers including SVM with radial basis kernel function (92%), LDA (90%), Fisher (89 %), and Parzen with 84% correct classification rate also showed acceptable results. This research shows the capability of hyperspectral imaging for honey authentication

Sequential forward selection and support vector regression in comparison to LASSO regression for spring wheat yield prediction based on UAV imagery

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VIS/NIR imaging application for honey floral origin determination

Nondestructive methods are of utmost importance for honey characterization. This study investigates the potential application of VIS-NIR hyperspectral imaging for detection of honey flower origin using machine learning techniques. Hyperspectral images of 52 honey samples were taken in transmittance mode in the visible/near infrared (VIS-NIR) range (400–1000 nm). Three different machine learning algorithms were implemented to predict honey floral origin using honey spectral images. These methods, included radial basis function (RBF) network, support vector machine (SVM), and random forest (RF). Principal component analysis (PCA) was also exploited for dimensionality reduction. According to the obtained results, the best classifier (RBF) achieved a precision of 94% in a fivefold cross validation experiment using only the first two PCs. Mapping of the classifier results to the test set images showed 90% accuracy for honey images. Three types of honey including buckwheat, rapeseed and heather were classified with 100% accuracy. The proposed approach has great potential for honey floral origin detection. As some other honey properties can also be predicted using image features, in addition to floral origin detection, this method may be applied to predict other honey characteristics.</p

Application of Image Analysis Combined with Computational Expert Approaches for Shrimp Freshness Evaluation

This study was aimed to evaluate the freshness and quality of cultured shrimp (litopenaeus vannamei) during 9 days of storage on ice (i.e., at a temperature of 0°C) using image processing technique. A lighting chamber was used to provide uniform conditions for illumination. The shrimp freshness was evaluated using computer vision technique through color changes of head, legs and tail of the harvested shrimps. Thirty-six color parameters of the images such as mean and variance of red (r), green (g), blue (b), lightness hue (h), saturation (s), value (v), luma information (i and y), the luma component (y), chroma component (cr), lightness (L*), redness (a*), yellowness (b*), chroma (c), and hue (h) were analyzed. Some parameters, such as b*, from side pictures and r mean, b variance, v mean, y mean, b* mean and (L*) mean from top pictures changed with a rather similar trend during the storage period. Different computational expert approaches such as linear discriminant analysis, quadratic discriminant analy..