• Journal of Biosystems Engineering
• /
• v.30 no.3 s.110
• /
• pp.172-178
• /
• 2005

Grading and sorting an indeterminate form of agricultural products such as sweet potatoes and potatoes are a labor intensive job because its shape and size are various and complicate. It costs a great deal to sort sweet potato in an indeterminate forms. There is a great need for an automatic grader fur the potatoes. Machine vision is the promising solution for this purpose. The optical indices for qualifying weight and appearance quality such as shape, color, defects, etc. were obtained and an on-line sorting system was developed. The results are summarized as follows. Sorting system combined with an on-line inspection device was composed of 5 sections, human inspection, feeding, illumination chamber, image processing & control, and grading & discharging. The algorithms to compute geometrical parameters related to the external guality were developed and implemented for sorting the deformed sweet potatoes. Grading accuracy by image processing was $96.4\%$ and the processing capacity was 10,800 pieces per hour.

• Proceedings of the Korean Society for Agricultural Machinery Conference
• /
• 2001.02a
• /
• pp.264-272
• /
• 2001

• Proceedings of the Korean Society for Agricultural Machinery Conference
• /
• 1993.10a
• /
• pp.1243-1253
• /
• 1993

Visual features of a mushroom(Lentinus Edodes L) are critical in sorting and grading as most agricultural products are. Because of its complex and various visual features, grading and sorting of mushrooms have been done manually by the human expert. Though actions involved in human grading looks simple, a decision making undereath the simple action comes form the results of the complex neural processing of the visual image. And processing details involved in the visual recognition of the human brain has not been fully investigated yet. Recently, however, an artificial neural network has drawn a great attention because of its functional capability as a partial substitute of the human brain. Since most agricultural products are not uniquely defined in its physical properties and do not have a well defined job structure, a research of the neuro-net based human like information processing toward the agricultural product and processing are widely open and promising. In this pape , neuro-net based grading and sorting system was developed for a mushroom . A computer vision system was utilized for extracting and quantifying the qualitative visual features of sampled mushrooms. The extracted visual features and their corresponding grades were used as input/output pairs for training the neural network and the trained results of the network were presented . The computer vision system used is composed of the IBM PC compatible 386DX, ITEX PFG frame grabber, B/W CCD camera , VGA color graphic monitor , and image output RGB monitor.

• Agricultural and Biosystems Engineering
• /
• v.1 no.2
• /
• pp.100-105
• /
• 2000

Quality sorting of cut flowers is very essential to increase the value of products. There are many factors that determine the quality of cut flowers such as length, thickness, and straightness of stem, and color and maturity of bud. Among these factors, the straightness of stem and the maturity of bud are generally considered to be more difficult to evaluate. A prototype grading and sorting machine for cut flowers was developed and tested for a rose variety. The machine consisted of a chain-drive feed mechanism, a pneumatic discharge system, and a grading system utilizing color image processing and neural network. Artificial neural network algorithm was utilized to grade cut roses based on the straightness of stem and maturity of bud. Test results showed 89% agreement with human expert for the straightness of stem and 90% agreement for the maturity of bud. Average processing time for evaluating straightness of the stem and maturity of the bud were 1.01 and 0.44 second, respectively. Application of neural network eliminated difficulties in determining criteria of each grade category while maintaining similar level of classification error.

• 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.

• Proceedings of the KIEE Conference
• /
• 2003.11b
• /
• pp.231-234
• /
• 2003

This paper describes the structure, operation, image processing, and decision making techniques of a color machine vision system designed for the automatic sorting of soybeans. The system consists of feeder, conveyor belt, line-scan camera, lights. ejector, and a PC Unlike manufactured goods, agricultural products including soybeans have quite uneven features. The criteria for sorting good and bad beans also vary depending on inspectors. We tackle these problem by letting the system learn the inspecting parameters from good samples selected manually by a machine user before running the system for sorting. Real-time processing has another importance In the design. Four parallel DSPs are employed to increase the processing speed. When the designed system was tested with real soybeans and the result was successful.

• 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 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.

• Journal of Biosystems Engineering
• /
• v.19 no.2
• /
• pp.124-137
• /
• 1994

Visual features of a mushroom(Lentinus Edodes, L.) are critical in grading and sorting as most agricultural products are. Because of its complex and various visual features, grading and sorting of mushrooms have been done manually by the human expert. To realize the automatic handling and grading of mushrooms in real time, the computer vision system should be utilized and the efficient and robust processing of the camera captured visual information be provided. Since visual features of a mushroom are distributed over the front and back sides, recognizing sides and states of the stalk including the stalk orientation from the captured image is a prime process in the automatic task processing. In this paper, the efficient and robust recognition process identifying the front and back side and the state of the stalk was developed and its performance was compared with other recognition trials. First, recognition was tried based on the rule set up with some experimental heuristics using the quantitative features such as geometry and texture extracted from the segmented mushroom image. And the neural net based learning recognition was done without extracting quantitative features. For network inputs the segmented binary image obtained from the combined type automatic thresholding was tested first. And then the gray valued raw camera image was directly utilized. The state of the stalk seriously affects the measured size of the mushroom cap. When its effect is serious, the stalk should be excluded in mushroom cap sizing. In this paper, the stalk removal process followed by the boundary regeneration of the cap image was also presented. The neural net based gray valued raw image processing showed the successful results for our recognition task. The developed technology through this research may open the new way of the quality inspection and sorting especially for the agricultural products whose visual features are fuzzy and not uniquely defined.

• 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.