• Korean Journal of Food Science and Technology
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• v.28 no.4
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• pp.786-789
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• 1996

An image processing system was built to evaluate the color properties of apple and meat. The system consisted of video camera, video card, 32 bit microcomputer and an optical illuminator. The operating software was developed to carry out capturing, analyzing, displaying and storing of the 8 bit digitized images of food. The images of apples at various maturing stages were investigated to obtain the color histogram of R, G, B and Hunter value. RGB histogram showed a major difference in G value, 35.01, the minor change in R value, 6.16, and the negligible difference in B value. The image of beef cut was separated into two parts, fat and lean tissue, by applying threshold value method based on the digital value of color. The threshold value for fat was over 240 and for lean under 230 in R value, respectively. The resulting non fat image showed 2% decreased color difference value, ${\Delta}E$, than whole meat cut.

• Journal of Multimedia Information System
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• v.4 no.4
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• pp.233-238
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• 2017

Automatic identification of disease in plants from their leaves is one of the most challenging task to researchers. Diseases among plants degrade their performance and results into a huge reduction of agricultural products. Therefore, early and accurate diagnosis of such disease is of the utmost importance. The advancement in deep Convolutional Neural Network (CNN) has change the way of processing images as compared to traditional image processing techniques. Deep learning architectures are composed of multiple processing layers that learn the representations of data with multiple levels of abstraction. Therefore, proved highly effective in comparison to many state-of-the-art works. In this paper, we present a plant disease identification methodology from their leaves using deep CNNs. For this, we have adopted GoogLeNet that is considered a powerful architecture of deep learning to identify the disease types. Transfer learning has been used to fine tune the pre-trained model. An accuracy of 85.04% has been recorded in the identification of four disease class in Apple plant leaves. Finally, a comparison with other models has been performed to show the effectiveness of the approach.

• Journal of Environmental Science International
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• v.11 no.4
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• pp.271-279
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• 2002

The purpose of this study is to focus on the townscape of Daegu based on the urban characteristics of the landscape cognition and images captured by citizens. The analysis was performed by the data obtained from questionnaires and interviews. This study methods were deals the cognition characteristics, landmark landscape, visual preference landscape, image and satisfaction. The results are summarized as follows: 1. The orders of cognition landscapes were estimated Apple＞ Weather＞ Texture＞ Mt.Palgong＞ Daegu Tower＞ Pretty Girl＞ Mt. Apsan＞ Dalsung Park＞ Conservative＞ Dongsung Road, etc. That is constructed Nonphysical elements(62.0%) and Physical elements(38.0%) 2. The orders of representative landscape(Landmark) in city were estimated Mt.Palgong＞ Daegu Tower＞ Gat Rock＞ Mt.Apsan＞ Dalsung Park, etc. As a whole middle and old people(over 30gen) preferenced as a history landscape or natural landscape, but youth people(10-20gen) preferenced as visible and interesting artifical places. 3. While the positive attitudes for the image of city were traditional(3.30), intimacy(3.58), and rest(2.90), the negative attitudes were unnewly(2.34), closing(2.37) and narrow(2.40). Also total satisfaction for that was estimated 5.51. 4. Psychological factors, related to the satisfaction of the image of city were composed of four factors, individuality character, pleasure character, amenity character, formation character. And the presumption formula of satisfaction was: Satisfaction = 5.477 ＋ 0.752(Individuality) ＋ 0.470(Pleasure) ＋ 0.413(Amenity) ＋ 0.241(Formation).

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11c
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• pp.513-520
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• 2000

An integrated grader which measures soluble solid content, color and weight of fresh apples was developed by NAMRI. The prototype grader consists of the near infrared spectroscopy and machine vision system. Image processing system and an algorithm to evaluate color were developed to speed up the color evaluation of apples. To avoid the light glare and specular reflection, an half-spherical illumination chamber was designed and fabricated to detect the color images of spherical-shaped apples more precisely. A color revision model based on neural network was developed. Near-infrared(NIR) spectroscopy system using NIR reflectance method developed by Lee et al(1998) of NAMRI was used to evaluate soluble solid content. In order to observe the performance of the grader, tests were conducted on conditions that there are 3 classes in weight sorting, 4 classes in combination of color and soluble solid content, and thus 12 classes in combined sorting. The average accuracy in weight, color and soluble solid content is more than about 90 % with the capacity of 3 fruits per second.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.3
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• pp.22-30
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• 2010

3D TV was been able to see in the market in early in this year. Tablet PC such as ipad by Apple and Galaxytab by Samsung were introduced recently. Those are possible by developing H/W and S/W of computer technology. The needs of interactive contents in many areas including education and entertainment area are increasing rapidly according to the various information devices are or will be in the market. Fore the more, GoogleTV and AppleTV are compete each other to dominate the world market in advance recently. CookTV tries to dominate in the domestic market by upgrading the current system. Diverse information devices are in the market means various size of displayers are able to be shown in our life. As TV is fused to computer, the displayer is substituted to TV's screen and the trend of TV is became bigger. The evolved TV is able to replace the computer by connecting to the network and people want to do interactions with contents by using the bidirectional communication. Therefore, it is expected to changing the human lifestyle. It is natural that contents for all members of family are needed, since TV's screen become bigger. It is required that the contents should guarantees the accessability of information to the all of family members and the easy interaction with contents. Our goal of experiment are to analyse the influence of interaction with contents as the size of images and to analyse a learning effect of contents quantitatively by applying a statistical method. Users interacted with contents without any difficulty when they met a same dimension and shape of objects as ame dimension and shape objects in their experiences or learning, was confirmed. And the learning effect were analysed and explained by applying the correlation.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.201-207
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• 2022

In this paper, the relationship between RGB, HSV, La^*b^* colors and sugar content was analyzed using linear regression on apples harvested in the same region. First, as a result of examining the correlation coefficient with sugar content according to each color level, it was found that the (+) region having a positive coefficient and a (-) region having a negative coefficient were separated according to the color level. Also, the correlation coefficient between color and sugar content, represented by the average value, was 0.342 in the La^*b^* color space, which was higher than the coefficient in the RGB and hsv space. That is, this means that the sugar content is related to the color in the La^*b^* space. Also, in the complex color composed of regions with high sugar content, it was found to be R²=0.3627, indicating that it is related to sugar content. In all nine color spaces, it was found to be R²=0.3668. In this case, it was found that the coefficients of v and b^* had an effect on the sugar content. Due to this, it was possible to confirm the validity of the empirical prediction that the higher the b^* representing yellow, the higher the sugar content.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.1
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• pp.15-21
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• 2023

This paper is a study on the development of an artificial intelligence model that recognizes a hole cup on a golf green. A CNN-based object detection algorithm was used to recognize the hole cup on the green. Also, Apple's CreateML was used to create a model of the object detection algorithm. This paper created a JSON file with 120 training images and annotations to meet the needs of CreateML. In addition, for more accurate learning, data amplification algorithm was used for learning data and 288 learning data were used for learning. By changing the Iterations, Batch size, and Grid size required by CreateML, we found parameter values that improve the performance of the model. A prototype app was developed by applying the developed model, and performance was measured on an actual golf course green using the prototype app. As a result of the measurement, it was found that the hole cup was accurately recognized within 10m, which is the typical golfer's putting distance.

• Journal of the Korean Society of Food Culture
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• v.25 no.6
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• pp.746-754
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• 2010

Rural tourism is primarily a domestic tourism activity with visitors traveling to non-urban areas. The development of local and regionally denominate food is a way to distinguish agricultural production and to promote rural tourism. Therefore, this study addressed how utilizing regional agricultural products results in increasing the intention of tourists to revisit an area. The purposes of this study were 1) to identify the image and motives for visiting Yesan, 2) to determine the importance of purchasing intention and the regional menu produced from local agricultural/special products, and 3) to identify the impact of purchasing local agricultural/special products and regional menus on the intention to revisit. A total of 202 usable questionnaires were collected at Ducksan Hotsprings and Suduck Temple in Yean area, which are known tourist attractions. The major findings obtained were as follows: First, Yesan was considered a relaxing place ($3.46{\pm}1.09$), which was the highest ranked image score for a tourist attraction. Second, the highest ranked motive for visiting Yesan was to rest ($3.77{\pm}1.18$). According to these findings, Yesan is a relaxing place, as it is a rural area with no known defined attractions. Third, most tourists (78.7%) recognized the apple as a local agricultural/special product. The intentions to purchase local agricultural/special products and the need for regional dishes in the local restaurant was higher than average. Tourists showed interests ($3.88{\pm}1.16$) in eating regional dishes made with local agricultural/special products at the restaurants. Fourth, a significant impact of purchasing local agricultural/special products and the regional menu was observed on the intention to revisit (p<0.000). The results indicate that it is very important to develop proper regional menus that concur with images of the location and the regional farming products.

• Korean Journal of Remote Sensing
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• v.33 no.5_1
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• pp.559-570
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• 2017

A partial least squares regression (PLSR) model was developed to map the internal soluble solids content (SSC) of apples using a ground-based hyperspectral scanner that could simultaneously acquire outdoor data and capture images of large quantities of apples. We evaluated the applicability of various preprocessing techniques to construct an optimal prediction model and calculated the optimal band through a variable importance in projection (VIP)score. From the 515 bands of hyperspectral images extracted at wavelengths of 360-1019 nm, 70 reflectance spectra of apples were extracted, and the SSC ($^{\circ}Brix$) was measured using a digital photometer. The optimal prediction model wasselected considering the root-mean-square error of cross-validation (RMSECV), root-mean-square error of prediction (RMSEP) and coefficient of determination of prediction $r_p^2$. As a result, multiplicative scatter correction (MSC)-based preprocessing methods were better than others. For example, when a combination of MSC and standard normal variate (SNV) was used, RMSECV and RMSEP were the lowest at 0.8551 and 0.8561 and $r_c^2$ and $r_p^2$ were the highest at 0.8533 and 0.6546; wavelength ranges of 360-380, 546-690, 760, 915, 931-939, 942, 953, 971, 978, 981, 988, and 992-1019 nm were most influential for SSC determination. The PLSR model with the spectral value of the corresponding region confirmed that the RMSEP decreased to 0.6841 and $r_p^2$ increased to 0.7795 as compared to the values of the entire wavelength band. In this study, we confirmed the feasibility of using a hyperspectral scanner image obtained from outdoors for the SSC measurement of apples. These results indicate that the application of field data and sensors could possibly expand in the future.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.30-43
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• 2018

Many studies have been carried out to retrieve images using colors, shapes, and textures which are characteristic of images. In addition, there is also progress in research related to the disease images of the crop. In this paper, to be a help to identify the disease occurred in crops grown in the agricultural field, we propose a similarity-based crop disease search system using the diseases image of horticulture crops. The proposed system improves the similarity retrieval performance compared to existing ones through the combination descriptor without using a single descriptor and applied the weight based calculation method to provide users with highly readable similarity search results. In this paper, a total of 13 Descriptors were used in combination. We used to retrieval of disease of six crops using a combination Descriptor, and a combination Descriptor with the highest average accuracy for each crop was selected as a combination Descriptor for the crop. The retrieved result were expressed as a percentage using the calculation method based on the ratio of disease names, and calculation method based on the weight. The calculation method based on the ratio of disease name has a problem in that number of images used in the query image and similarity search was output in a first order. To solve this problem, we used a calculation method based on weight. We applied the test image of each disease name to each of the two calculation methods to measure the classification performance of the retrieval results. We compared averages of retrieval performance for two calculation method for each crop. In cases of red pepper and apple, the performance of the calculation method based on the ratio of disease names was about 11.89% on average higher than that of the calculation method based on weight, respectively. In cases of chrysanthemum, strawberry, pear, and grape, the performance of the calculation method based on the weight was about 20.34% on average higher than that of the calculation method based on the ratio of disease names, respectively. In addition, the system proposed in this paper, UI/UX was configured conveniently via the feedback of actual users. Each system screen has a title and a description of the screen at the top, and was configured to display a user to conveniently view the information on the disease. The information of the disease searched based on the calculation method proposed above displays images and disease names of similar diseases. The system's environment is implemented for use with a web browser based on a pc environment and a web browser based on a mobile device environment.