• Korean Journal of Remote Sensing
• /
• v.36 no.5_1
• /
• pp.667-677
• /
• 2020

Crop water stress can be detected based on soil moisture content, crop physiological characteristics and remote-sensing technology. The detection of crop water stress is an important issue for the accurate assessment of yield decline. The crop water stress index (CWSI) has been introduced based on the difference between leaf and air temperature. In this paper, drone-based thermal infrared image was used to map of crop water stress in water control plot (WCP) and water deficit plot (WDP) over spring chinese cabbage fields. The spatial distribution map of CWSI was in strong agreement with the abnormal growth response factors (plant height, plant diameter, and measured value by chlorophyll meter). From these results, CWSI can be used as a good method for evaluation of crop abnormal growth monitoring.

• Korean Journal of Remote Sensing
• /
• v.22 no.3
• /
• pp.183-197
• /
• 2006

Combinations of visible and near-infrared (NIR) bands in an image are widely used for estimating vegetation vigor and productivity. Using this approach to understand within-field grain crop variability could allow pre-harvest estimates of yield, and might enable mapping of yield variations without use of a combine yield monitor. The objective of this study was to estimate within-field variations in crop yield using vegetation indices derived from hyperspectral images. Hyperspectral images were acquired using an aerial sensor on multiple dates during the 2003 and 2004 cropping seasons for corn and soybean fields in central Missouri. Vegetation indices, including intensity normalized red (NR), intensity normalized green (NG), normalized difference vegetation index (NDVI), green NDVI (gNDVI), and soil-adjusted vegetation index (SAVI), were derived from the images using wavelengths from 440 nm to 850 nm, with bands selected using an iterative procedure. Accuracy of yield estimation models based on these vegetation indices was assessed by comparison with combine yield monitor data. In 2003, late-season NG provided the best estimation of both corn $(r^2\;=\;0.632)$ and soybean $(r^2\;=\;0.467)$ yields. Stepwise multiple linear regression using multiple hyperspectral bands was also used to estimate yield, and explained similar amounts of yield variation. Corn yield variability was better modeled than was soybean yield variability. Remote sensing was better able to estimate yields in the 2003 season when crop growth was limited by water availability, especially on drought-prone portions of the fields. In 2004, when timely rains during the growing season provided adequate moisture across entire fields and yield variability was less, remote sensing estimates of yield were much poorer $(r^2<0.3)$.

• Korean Journal of Medicinal Crop Science
• /
• v.19 no.6
• /
• pp.508-513
• /
• 2011

The bark of the root and stem of Ulmus davidiana var. japonica has been used as a traditional Korean medicine to treat inflammatory disorders. This plant reportedly shows antioxidant, anticancer, and anti-inflammatory effects. In this study, we investigated the protective effects of Ulmus davidiana var. japonica ethanolic extract (UDE) on UVB irradiation-induced wrinkle in hairless mice. We evaluated for their free radical-scavenging activities against 1,1-diphenyl-2-picrylhydrazyl (DPPH) and the anti-elastase activities, and for their anti-matrix metalloproteinase-1 (MMP-1) activity in human skin fibroblast cells. In the wrinkle measurement and image analysis of skin replicas, the results showed that UDE significantly inhibited wrinkle formation caused by chronic UVB irradiation. These results suggest that UDE has anti-wrinkle activity.

• Smart Media Journal
• /
• v.5 no.1
• /
• pp.49-54
• /
• 2016

Medical image processing is a compulsory process to diagnose many kinds of disease. Therefore, an automatic algorithm for this task is highly demanded as an important part to construct a computer-aided diagnosis system. In this paper, we introduce an automatic method to segment the liver region from 3D abdominal CT images using Otsu method. First, we choose a 2D slice which has most liver information from the whole 3D image. Secondly, on the chosen slice, we enhanced the image based on its intensity using Otsu method with multiple thresholds and use the threshold to enhance the whole 3D image. Then, we apply a liver mask to mark the candidate liver region. After that, we execute the Otsu method again to segment the liver region from the chosen slice and propagate the result to the whole 3D image. Finally, we apply preprocessing on the frontal side of 3D images to crop only the liver region from the image.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.25 no.4
• /
• pp.337-345
• /
• 2023

Estimating the rice heading date is one of the most crucial agricultural tasks related to productivity. However, due to abnormal climates around the world, it is becoming increasingly challenging to estimate the rice heading date. Therefore, a more objective classification method for estimating the rice heading date is needed than the existing methods. This study, we aimed to classify the rice heading stage from various images using a CNN classification model. We collected top-view images taken from a drone and a phenotyping tower, as well as slanted-view images captured with a RGB camera. The collected images underwent preprocessing to prepare them as input data for the CNN model. The CNN architectures employed were ResNet50, InceptionV3, and VGG19, which are commonly used in image classification models. The accuracy of the models all showed an accuracy of 0.98 or higher regardless of each architecture and type of image. We also used Grad-CAM to visually check which features of the image the model looked at and classified. Then verified our model accurately measure the rice heading date in paddy fields. The rice heading date was estimated to be approximately one day apart on average in the four paddy fields. This method suggests that the water head can be estimated automatically and quantitatively when estimating the rice heading date from various paddy field monitoring images.

• Proceedings of the Korean Society of Crop Science Conference
• /
• 2017.06a
• /
• pp.200-200
• /
• 2017

Salinity stress is one of the most important abiotic stresses and severely impairs plant growth and production. Root is the first site for nutrient accumulation like as $Na^+$ in the plant. To investigate the response of wheat root under salinity stress, we executed the characterization of morphology and analysis of metabolites. Wheat seeds cv. Keumgang (Korean cultivar) were grown on the moist filter paper in Petri dish. After 5 days, seedlings were transferred to hydroponic apparatus at 1500 LUX light intensity, at $20^{\circ}C$ with 70% relative humidity in a growth chamber. Seedlings (5-day-old) were exposed to 50mM, 75mM, 100mM NaCl for 5 days. Ten-day-old seedlings were used for morphological characterization and metabolite analysis. Root and leaf length became shorter in high NaCl concentration compared to following NaCl treatment. For confirmation of salt accumulation, wheat roots were stained with $CoroNa^+$ Green AM, and fluoresce, and the image was taken by confocal microscopy. $Na^+$ ion accumulation rate was higher at 100mM compared to the untreated sample. Furthermore, to analyze metabolites in the wheat root, samples were extracted by $D_2O$ solvent, and extracted sample was analyzed by 1H NMR spectroscopy. Fourteen metabolites were identified in wheat roots using NMR spectroscopy. Methanol and ethanol were up-regulated, whereas formate, aspartate, aminobutyrate, acetate and valine were down-regulated under salinity stress on roots of wheat. Fumarate had no change, while glucose, betaine, choline, glutamate and lactate were unevenly affected during salinity stress.

• Proceedings of the Korean Society for Agricultural Machinery Conference
• /
• 2000.11b
• /
• pp.287-294
• /
• 2000

Calculation of the flexural rigidity value (EI) is indispensable for prescription of deflection characteristics of crop stalks in harvesting□Conventionally□EI has been determined by either average EI of the whole stalk or average EI of each stems divided into node through the calculation method of cantilever with homogeneous section□However□deflection characteristics of crop stalks caused by mechanical operation such as combine harvester were not exactly presumed by these conventional EI through the experiment by authors. Further, actual EI of a stalk changes in company with a change of moisture contents as time passes during the experiment. Finally, efficient calculation method for determining EI is needed in order to improve these problems. In this study, mechanical model based on actual structure of the crop stalk with variety sectional area was proposed. This mechanical model is calculated by the theory of cantilever with continuous stages. Therefore, improvement of both calculating accuracy on EI and efficiency of measuring system was tried. At first, this calculation method was applied to piano wire of which EI was recognized in advance. As a result, EI calculated from this new method coincided approximately with piano wire's EI. Next, applying to crop stalks as same as piano wire, relationship between loads acting on crop stalks and deflection values calculated by EI using this new calculation method was exactly presumed in comparison with conventional method. Further, measuring time of deflection test was greatly reduced. Finally, new calculation method of EI will be available for estimating mechanical characteristics of so many kinds of crop stalks in harvesting operation. Further, in this study, new deflection test using image-processing apparatus by computer will be introduced.

• Korean Journal of Remote Sensing
• /
• v.40 no.1
• /
• pp.93-101
• /
• 2024

Accurate field crop classification is essential for various agricultural applications, yet existing methods face challenges due to diverse crop types and complex field conditions. This study aimed to address these issues by combining support vector machine (SVM) models with multi-seasonal unmanned aerial vehicle (UAV) images, texture information extracted from Gray Level Co-occurrence Matrix (GLCM), and RGB spectral data. Twelve high-resolution UAV image captures spanned March-October 2021, while field surveys on three dates provided ground truth data. We focused on data from August (-A), September (-S), and October (-O) images and trained four support vector classifier (SVC) models (SVC-A, SVC-S, SVC-O, SVC-AS) using visual bands and eight GLCM features. Farm maps provided by the Ministry of Agriculture, Food and Rural Affairs proved efficient for open-field crop identification and served as a reference for accuracy comparison. Our analysis showcased the significant impact of hyperparameter tuning (C and gamma) on SVM model performance, requiring careful optimization for each scenario. Importantly, we identified models exhibiting distinct high-accuracy zones, with SVC-O trained on October data achieving the highest overall and individual crop classification accuracy. This success likely stems from its ability to capture distinct texture information from mature crops.Incorporating GLCM features proved highly effective for all models,significantly boosting classification accuracy.Among these features, homogeneity, entropy, and correlation consistently demonstrated the most impactful contribution. However, balancing accuracy with computational efficiency and feature selection remains crucial for practical application. Performance analysis revealed that SVC-O achieved exceptional results in overall and individual crop classification, while soybeans and rice were consistently classified well by all models. Challenges were encountered with cabbage due to its early growth stage and low field cover density. The study demonstrates the potential of utilizing farm maps and GLCM features in conjunction with SVM models for accurate field crop classification. Careful parameter tuning and model selection based on specific scenarios are key for optimizing performance in real-world applications.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.42 no.3 s.303
• /
• pp.73-82
• /
• 2005

Recently, The wide range of the Internet applications and the related technology developments enabled the ease use of the digital multimedia contents (fixed images, movies, digital audios). However, due to the replay ability which the contents may be easily duplicated and not only the duplicates are capable of providing the same original quality. There are mainly the encipher techniques and the watermarking techniques which are studied and used as solutions for the above problem in order to protect the license holders' rights. To the protection of the IP(Intellectual Property) rights of the owner, digital watermarking is the technique that authenticates the legal copyrighter. This paper proposed the watermarking algorithms to watermark the 256 gray logo image and the color image by applying the wavelet transformation to the color stand-still images. The proposed algorithms conducted the watermark insertion at the LH frequency region among the wavelet transformation regions (LL, LH, HL, HH). The interleaving algorithms which applied in data communication was applied to the watermark. the amount of watermark increased which consequently caused the PSNR to decrease but this might provide the perseverance against the external attacks such as extraction, filtering, and crop.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.17 no.5
• /
• pp.97-102
• /
• 2017

Recently deep learning techniques such as convolutional neural networks (CNN) have been introduced to classify high-resolution remote sensing data. In this paper, we investigated the possibility of applying CNN to crop classification of farmland images captured by drones. The farming area was divided into seven classes: rice field, sweet potato, red pepper, corn, sesame leaf, fruit tree, and vinyl greenhouse. We performed image pre-processing and normalization to apply CNN, and the accuracy of image classification was more than 98%. With the output of this study, it is expected that the transition from the existing image classification methods to the deep learning based image classification methods will be facilitated in a fast manner, and the possibility of success can be confirmed.