• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.1
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• pp.16-25
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• 2020

Vegetation segmentation in a field color image is a process of distinguishing vegetation objects of interests like crops and weeds from a background of soil and/or other residues. The performance of the process is crucial in automatic precision agriculture which includes weed control and crop status monitoring. To facilitate the segmentation, color indices have predominantly been used to transform the color image into its gray-scale image. A thresholding technique like the Otsu method is then applied to distinguish vegetation parts from the background. An obvious demerit of the thresholding based segmentation will be that classification of each pixel into vegetation or background is carried out solely by using the color feature of the pixel itself without taking into account color features of its neighboring pixels. This paper presents a new pixel-based segmentation method which employs a multi-layer perceptron neural network to classify the gray-scale image into vegetation and nonvegetation pixels. The input data of the neural network for each pixel are 2-dimensional gray-level values surrounding the pixel. To generate a gray-scale image from a raw RGB color image, a well-known color index called Excess Green minus Excess Red Index was used. Experimental results using 80 field images of 4 vegetation species demonstrate the superiority of the neural network to existing threshold-based segmentation methods in terms of accuracy, precision, recall, and harmonic mean.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.123-123
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• 2017

In spite of the potential medicinal significance and a wide range of pharmacologic properties of Platycodon grandiflorum, the molecular mechanism of its roots is still unknown. The present study was conducted to profile proteins from 3, 4 and 5 months aged diploid and tetraploid roots of Platycodon grandiflorum using high throughput proteome approach. Two-dimensional gels stained with CBB, a total of 68 differential expressed proteins were identified from the diploid root out of 767 protein spots using image analysis by Progenesis SameSpot software. Out of total differential expressed spots, 29 differential expressed protein spots (${\geq}2-fold$) were analyzed using LTQ-FTICR MS whereas a total of 24 protein spots were up-regulated and 5 protein spots were down-regulated. On the contrary, in the case of tetraploid root, a total of 86 differential expressed proteins were identified from tetraploid root out of 1033 protein spots of which a total of 39 differential expressed protein spots (${\geq}2-fold$) were analyzed using LTQ-FTICR MS whereas a total of 21 protein spots were up-regulated and a total of 18 protein spots were down-regulated. It was revealed that the identified proteins from the explants were mainly associated with the nucleotide binding, oxidoreductase activity, transferase activity. Taken together, the identified proteins may be helpful to identify key candidate proteins for genetic improvement of plants.

• Agricultural and Biosystems Engineering
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• v.7 no.1
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• pp.18-26
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• 2006

In-field site-specific nitrogen (N) management increases crop yield, reduces N application to minimize the risk of nitrate contamination of ground water, and thus reduces farming cost. Real-time N sensing and fertilization is required for efficient N management. An 'on-the-go' site-specific N management system was developed and evaluated for the supplemental N application to com (Zea mays L.). This real-time N sensing and fertilization system monitored and assessed N fertilization needs using a vision-based spectral sensor and controlled the appropriate variable N rate according to N deficiency level estimated from spectral signature of crop canopies. Sensor inputs included ambient illumination, camera parameters, and image histogram of three spectral regions (red, green, and near-infrared). The real-time sensor-based supplemental N treatment improved crop N status and increased yield over most plots. The largest yield increase was achieved in plots with low initial N treatment combined with supplemental variable-rate application. Yield data for plots where N was applied the latest in the season resulted in a reduced impact on supplemental N. For plots with no supplemental N application, yield increased gradually with initial N treatment, but any N application more than 101 kg/ha had minimal impact on yield.

• Korean Journal of Soil Science and Fertilizer
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• v.50 no.3
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• pp.162-178
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• 2017

Remote sensing can be used to provide information about the monitoring of crop growth condition. Recently Unmanned Aerial Vehicle (UAV) technology offers new opportunities for assessing crop growth condition using UAV imagery. The objective of this study was to assess weather UAV aerial images are suitable for the monitoring of highland Kimchi cabbage. This study was conducted using a fixed-wing UAV (Model : Ebee) with Cannon S110, IXUS/ELPH camera during farming season from 2015 to 2016 in the main production area of highland Kimchi cabbage, Anbandegi, Maebongsan, and Gwinemi. The Normalized Difference Vegetation Index (NDVI) by using UAV images was stable and suitable for monitoring of Kimchi cabbage situation. There were strong relationships between UAV NDVI and the growth parameters (the plant height and leaf width) ($R^2{\geq}0.94$). The tendency of UAV NDVI according to Kimchi cabbage growth was similar in the same area for two years (2015~2016). It means that if UAV image may be collected several years, UAV images could be used for estimation of the stage of growth and situation of Kimchi cabbage cultivation.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.66 no.4
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• pp.452-458
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• 2021

Roots are the most important organs in plants that absorb nutrients and moisture from the soil. However, owing to difficulties in root data collection, root research is still poorly conducted as compared to shoot research. Recent advancements in crop phenotyping, through advanced imagery data, are rapidly increasing, and artificial intelligence has been applied in various crop root research. Depending on the purpose, different root analysis methods have been developed that measure roots directly in soil or after separation from the soil. Each method has its advantages and disadvantages; therefore, it can be used in accordance with the research interest. Therefore, this review introduces root analysis methods that use imagery systems to help domestic researchers precisely study plant roots or root architecture.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.807-821
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• 2020

The objective of this study is to evaluate the applicability of representative spatio-temporal fusion models developed for the fusion of mid- and low-resolution satellite images in order to construct a set of time-series high-resolution images for crop monitoring. Particularly, the effects of the characteristics of input image pairs on the prediction performance are investigated by considering the principle of spatio-temporal fusion. An experiment on the fusion of multi-temporal Sentinel-2 and RapidEye images in agricultural fields was conducted to evaluate the prediction performance. Three representative fusion models, including Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), SParse-representation-based SpatioTemporal reflectance Fusion Model (SPSTFM), and Flexible Spatiotemporal DAta Fusion (FSDAF), were applied to this comparative experiment. The three spatio-temporal fusion models exhibited different prediction performance in terms of prediction errors and spatial similarity. However, regardless of the model types, the correlation between coarse resolution images acquired on the pair dates and the prediction date was more significant than the difference between the pair dates and the prediction date to improve the prediction performance. In addition, using vegetation index as input for spatio-temporal fusion showed better prediction performance by alleviating error propagation problems, compared with using fused reflectance values in the calculation of vegetation index. These experimental results can be used as basic information for both the selection of optimal image pairs and input types, and the development of an advanced model in spatio-temporal fusion for crop monitoring.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.49 no.1
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• pp.41-45
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• 2004

We have developed and tested a new method for nondestructive estimation of chlorophyll- and nitrogen-contents in rye leaf. It was found that the relation-ships among nitrogen, chlorophyll content and fresh weight were significantly positive correlated. Nitrogen and chlorophyll content were positively correlated whereas correlation coefficients among R, G, R-B and G-B on the basis of photo-numerical values were negative. We have found that R/(R-B) obtained from data of digital camera is the best criterion to estimate the chlorophyll content of leaves. The regression curves of the relation between R/(R-B) and chlorophyll content were also calculated from the data collected on cloudy days. The coefficients of determination ($\textrm{r}^2$) were ranged from 0.33 to 0.99. In this study, the accuracy in estimating chlorophyll content from the color data of digital camera image could be improved by correcting with R, G, and B values. It is suggested that, for practical purposes, the image values estimated with sufficient accuracy using a portable digital camera can be applied for determining chlorophyll content and nitrogen status in plant leaves.

• Journal of The Korean Society of Agricultural Engineers
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• v.58 no.1
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• pp.91-97
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• 2016

The objective of this study was to classify between radish and Chinese cabbage in autumn using hyperspectral images. The hyperspectral images were acquired by Compact Airborne Spectrographic Imager (CASI) with 1m spatial resolution and 48 bands covering the visible and near infrared portions of the solar spectrum from 370 to 1044 nm with a bandwidth of 14 nm. An object-based technique is used for classification of radish and Chinese cabbage. It was found that the optimum parameter values for image segmentation were scale 400, shape 0.1, color 0.9, compactness 0.5 and smoothness 0.5. As a result, the overall accuracy of classification was 90.7 % and the kappa coefficient was 0.71. The hyperspectral images can be used to classify other crops with higher accuracy than radish and Chines cabbage because of their similar characteristic and growth time.

• Journal of the Korean Institute of Landscape Architecture
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• v.27 no.4
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• pp.131-136
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• 1999

Aerial videography techniques have been used to inventory conditions associated with grassland, forests, and agricultural crop production. Most recently, aerial videography has been used to verity satellite image classifications as part of the natural ecosystem survey. The objectives of this study were: (1) to use aerial video images of the study area, one part of Taean Seashore National Park, for the accuracy assessment, and (2) to determine the suitability of aerial videography as an accuracy assessment, of the land cover classification with Landsat-TM data. Video images were collected twice, summer and winter seasons, and divided into two kinds of images, wide angle and narrow angle images. Accuracy assessment methods include the calculation of the error matrix, the overall accuracy and kappa coefficient of agreement. This study indicates that aerial videography is an effective tool for accuracy assessment of the satellite image classifications of which features are relatively large and continuous. And it would be possible to overcome the limits of the present natural ecosystem survey method.

• Journal of Korea Water Resources Association
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• v.53 no.6
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• pp.463-467
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• 2020

The main feature of this study is to automatically synthesize square images by sending aerial photographs and images from unmanned aerial vehicles (drons). It may be applicable to the cloud server, and to apply analytical algorithms for the suitable purpose of image processing. Drone imaging analysis is a process that can be used in various fields such as finding contaminated area of green algae, monitoring forest fire, and managing crop cultivation.