• Journal of the Korean Society for Nondestructive Testing
• /
• v.35 no.1
• /
• pp.1-11
• /
• 2015

In this study, an online seed potato sorting system using a visible and near infrared (40 1100 nm) transmittance spectral technique and statistical model was evaluated for the nondestructive determination of infected and sound seed potatoes. Seed potatoes that had been artificially infected with Pectobacterium atrosepticum, which is known to cause a soil borne disease infection, were prepared for the experiments. After acquiring transmittance spectra from sound and infected seed potatoes, a determination algorithm for detecting infected seed potatoes was developed using the partial least square discriminant analysis method. The coefficient of determination($R^2_p$) of the prediction model was 0.943, and the classification accuracy was above 99% (n = 80) for discriminating diseased seed potatoes from sound ones. This online sorting system has good potential for developing a technique to detect agricultural products that are infected and contaminated by pathogens.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.34 no.1
• /
• pp.31-37
• /
• 2014

The aim of this study was the non-destructive evaluation of bloody eggs using VIS/NIR spectroscopy. The bloody egg samples used to develop the sorting mode were produced by injecting chicken blood into the edges of egg yolks. Blood amounts of 0.1, 0.7, 0.04, and 0.01 mL were used for the bloody egg samples. The wavelength range for the VIS/NIR spectroscopy was 471 to 1154 nm, and the spectral resolution was 1.5nm. For the measurement system, the position of the light source was set to $30^{\circ}$, and the distance between the light source and samples was set to 100 mm. The minimum exposure time of the light source was set to 30 ms to ensure the fast sorting of bloody eggs and prevent heating damage of the egg samples. Partial least squares-discriminant analysis (PLS-DA) was used for the spectral data obtained from VIS/NIR spectroscopy. The classification accuracies of the sorting models developed with blood samples of 0.1, 0.07, 0.04, and 0.01 mL were 97.9%, 98.9%, 94.8%, and 86.45%, respectively. In this study, a novel nondestructive sorting technique was developed to detect bloody brown eggs using spectral data obtained from VIS/NIR spectroscopy.

• Proceedings of the Korean Society for Agricultural Machinery Conference
• /
• 1996.06c
• /
• pp.171-190
• /
• 1996

Quality evaluation of agricultural products has been a subject of interest to many researches for many years. As a results, several nondestructive techniques for quality evaluation of agricultural products have been developed. These methods are based on the detection of various physical properties that correlate well with certain quality factors of the products. This paper presents an overview of various quality evaluation techniques that are based on one of the following properties : density, firmness , vibration characteristic , X-ray and gamma ray transmission, optical reflectance and transmission , electrical properties, aromatic volatile emission, and nuclear magnetic resonance (NMR). The sophistication of nondestructive methods has evolved rapidly with modern technologies. The use of various modern image acquisition techniques, such as solid state TV camera, line-scan camera, X-ray scanning , ultrasonic scanning and NMR imaging, in conjunction with image-processing te hniques has provided new opportunities for researchers to develop many new and improved techniques for nondestructive quality evaluation of agricultural products.

• Korean Journal of Agricultural Science
• /
• v.39 no.1
• /
• pp.111-116
• /
• 2012

Nondestructive evaluation of seed viability is one of the highly demanding technologies for seed production industry. Conventional seed sorting technologies, such as tetrazolium and standard germination test are destructive, time consuming, and labor intensive methods. Near infrared spectroscopy technique has shown good potential for nondestructive quality measurements for food and agricultural products. In this study, FT-NIR spectroscopy was used to classify normal and artificially aged lettuce seeds. The spectra with the range of 1100~2500 nm were scanned for lettuce seeds and analyzed using the principal component analysis(PCA) method. To classify viable seeds from nonviable seeds, a calibration modeling set was developed with a partial least square(PLS) method. The calibration model developed from PLS resulted in 98% classification accuracy with the Savitzky-Golay $1^{st}$ derivative preprocessing method. The prediction accuracy for the test data set was 93% with the MSC(Multiplicative Scatter Correction) preprocessing method. The results show that FT-NIR has good potential for discriminating non-viable lettuce seeds from viable ones.

• Journal of Biosystems Engineering
• /
• v.29 no.2
• /
• pp.167-174
• /
• 2004

Density is a physical property which contains information relating to the internal quality of fruits and vegetables, and can be used as an index for nondestructive quality evaluation. Density sorting has been employed by farmers for some agricultural products since ancient times. In this study, an automatic density measuring system based on the platform scale or water displacement method was developed for density sorting of watermelon. It consisted of water tan, load cell, net tray, electric motor, limit switch, control system and its program. The resolution of density was 0.001 g/㎤. In order to calibrate and evaluate the accuracy, the density was measured using a balloon kept in cold water. It showed 1.002 g/㎤ which almost correspond to real density of water. Test results with 6 watermelons and 3 replications showed that the standard deviations of the dens were 0.001∼0.004 g/㎤. The relationship between density and internal quality of watermelon was investigated using the system. The densities of hollow watermelons were less than 0.950 g/㎤, it was apparent that the density of the watermelon was related to the degree of hollowness. But the soluble solid contents and internal defects could not be estimated from the density.

• Korean Journal of Agricultural Science
• /
• v.37 no.2
• /
• pp.303-308
• /
• 2010

Surface color is the most important quality factor for the grade evaluation of cherry tomato. Color is one of the representative indicators for the maturity which is closely related to the internal quality of cherry tomato, such as firmness, sugar content, and acidity. This study was carried out to investigate the relationship between surface color and internal quality of cherry tomatoes harvested from both hydroponic and soil culture at different ripening stages. To calculate the color values of cherry tomatoes an automatic color imaging system was constructed. A specially designed image processing algorithm for the color measurement was developed. The color values of L*, a*, b* were calculated from the initial color values of RGB and then compared with the internal quality. Statistical analyses indicated that the internal quality was more highly correlated with the surface color than size of cherry tomatoes. Color image features were also investigated to detect external damage of cherry tomatoes. The value of (R value - R mean value)/R mean value was the most effective image feature for the detection of damaged areas on the surface of cherry tomatoes. The results of this study demonstrated the feasibility of color sorting process as an alternative of the conventional drum type size sorting system for cherry tomato industry.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.36 no.1
• /
• pp.35-44
• /
• 2016

Seed viability is one of the most important parameters that is directly related with seed germination performance and seedling emergence. In this study, a hyperspectral imaging (HSI) system having a range of 1000-2500 nm was used to classify viable watermelon seeds from nonviable seeds. In order to obtain nonviable watermelon seeds, a total of 96 seeds were artificially aged by immersing the seeds in hot water ($25^{\circ}C$) for 15 days. Further, hyperspectral images for 192 seeds (96 normal and 96 aged) were acquired using the developed HSI system. A germination test was performed for all the 192 seeds in order to confirm their viability. Spectral data from the hyperspectral images of the seeds were extracted by selecting pixels from the region of interest. Each seed spectrum was averaged and preprocessed to develop a classification model of partial least square discriminant analysis (PLS-DA). The developed PLS-DA model showed a classification accuracy of 94.7% for the calibration set, and 84.2% for the validation set. The results demonstrate that the proposed technique can classify viable and nonviable watermelon seeds with a reasonable accuracy, and can be further converted into an online sorting system for rapid and nondestructive classification of watermelon seeds with regard to viability.

• Journal of Biosystems Engineering
• /
• v.37 no.6
• /
• pp.411-419
• /
• 2012

Purpose: Nondestructive evaluation of seed viability is a highly demanded technique in the seed industry. In this study, hyperspectral imaging system was used for discrimination of viable and non-viable radish seeds. Method: The spectral data with the range from 400 to 1000 nm measured by hyperspectral reflectance imaging system were used. A calibration and a test models were developed by partial least square discrimination analysis (PLS-DA) for classification of viable and non-viable radish seeds. Either each data set of visible (400~750 nm) and NIR (750~1000 nm) spectra and the spectra of the combined spectral ranges were used for developing models. Results: The discrimination accuracy of calibration was 84% for visible range and 76.3% for NIR range. The discrimination accuracy of test was 84.2% for visible range and 75.8% for NIR range. The discrimination accuracies of calibration and test with full range were 92.2% and 92.5%, respectively. The resultant images based on the optimal PLS-DA model showed high performance for the discrimination of the nonviable seeds from the viable seeds with the accuracy of 95%. Conclusions: The results showed that hyperspectral reflectance imaging has good potential for discriminating nonviable radish seeds from massive amounts of viable seeds.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.32 no.5
• /
• pp.518-525
• /
• 2012

In this study, the feasibility of hyperspectral reflectance imaging technique was investigated for the discrimination of viable and non-viable lettuce seeds. The spectral data of hyperspectral reflectance images with the spectral range between 750 nm and 1000 nm were used to develop PLS-DA model for the classification of viable and non-viable lettuce seeds. The discrimination accuracy of the calibration set was 81.6% and that of the test set was 81.2%. The image analysis method was developed to construct the discriminant images of non-viable seeds with the developed PLS-DA model. The discrimination accuracy obtained from the resultant image were 91%, which showed the feasibility of hyperspectral reflectance imaging technique for the mass discrimination of non-viable lettuce seeds from viable ones.

• Journal of Biosystems Engineering
• /
• v.38 no.4
• /
• pp.312-317
• /
• 2013

Purpose: Conventional methods used to evaluate seeds viability are destructive, time consuming, and require the use of chemicals, which are not feasible to implement to process plant in seed industry. In this study, the effectiveness of Fourier transform near infrared (FT-NIR) spectroscopy to differentiate between viable and nonviable watermelon seeds was investigated. Methods: FT-NIR reflectance spectra of both viable and non-viable (aging) seeds were collected in the range of 4,000 - 10,000 $cm^{-1}$ (1,000 - 2,500 nm). To differentiate between viable and non-viable seeds, a multivariate classification model was developed with partial least square discrimination analysis (PLS-DA). Results: The calibration and validation set derived from the PLS-DA model classified viable and non-viable seeds with 100% accuracy. The beta coefficient of PLS-DA, which represented spectral difference between viable and non-viable seeds, showed that change in the chemical component of the seed membrane (such as lipids and proteins) might be responsible for the germination ability of the seeds. Conclusions: The results demonstrate the possibility of using FT-NIR spectroscopy to separate seeds based on viability, which could be used in the development of an online sorting technique.