• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.07a
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• pp.207-216
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• 2000

• Journal of Biosystems Engineering
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• v.40 no.4
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• pp.417-427
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• 2015

Purpose: This study investigated different band selection methods to classify spectrally similar data - obtained from aerial images of healthy citrus canopies and citrus greening disease (Huanglongbing or HLB) infected canopies - using small differences without unmixing endmember components and therefore without the need for an endmember library. However, large number of hyperspectral bands has high redundancy which had to be reduced through band selection. The objective, therefore, was to first select the best set of bands and then detect citrus Huanglongbing infected canopies using these bands in aerial hyperspectral images. Methods: The forward feature selection algorithm (FFSA) was chosen for band selection. The selected bands were used for identifying HLB infected pixels using various classifiers such as K nearest neighbor (KNN), support vector machine (SVM), naïve Bayesian classifier (NBC), and generalized local discriminant bases (LDB). All bands were also utilized to compare results. Results: It was determined that a few well-chosen bands yielded much better results than when all bands were chosen, and brought the classification results on par with standard hyperspectral classification techniques such as spectral angle mapper (SAM) and mixture tuned matched filtering (MTMF). Median detection accuracies ranged from 66-80%, which showed great potential toward rapid detection of the disease. Conclusions: Among the methods investigated, a support vector machine classifier combined with the forward feature selection algorithm yielded the best results.

• Magazine of the Korean Society of Agricultural Engineers
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• v.44 no.7
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• pp.1-13
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• 2002

Two global optimization methods, the SCE-UA method and the Annealing-simplex (A-S) method for calibrating a daily rainfall-runoff model, a Tank model, was compared with that of the Downhill Simplex method. The performance of the four objective functions, DRMS (daily root mean square), HMLE (heteroscedastic maximum likelihood estimator), ABSERR (mean absolute error), and NS (Nash-Sutcliffe measure), was tested and synthetic data and historical data were used. In synthetic data study. 100% success rates for all objective functions were obtained from the A-S method, and the SCE-UA method was also consistently able to obtain good estimates. The downhill simplex method was unable to escape from local optimum, the worst among the methods, and converged to the true values only when the initial guess was close to the true values. In the historical data study, the A-S method and the SCE-UA method showed consistently good results regardless of objective function. An objective function was developed with combination of DRMS and NS, which putted more weight on the low flows.

• Geomechanics and Engineering
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• v.33 no.2
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• pp.203-209
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• 2023

One major advantage of ground penetrating radar (GPR) over other field test methods is its ability to obtain subsurface images of roads in an efficient and non-intrusive manner. Not only can the strata of pavement structure be retrieved from the GPR scan images, but also various irregularities, such as cracks and internal cavities. This article introduces a deep learning-based approach, focusing on detecting subsurface cracks by recognizing their distinctive hyperbolic signatures in the GPR scan images. Given the limited road sections that contain target features, two data augmentation methods, i.e., feature insertion and generation, are implemented, resulting in 9,174 GPR scan images. One of the most popular real-time object detection models, You Only Learn One Representation (YOLOR), is trained for detecting the target features for two types of subsurface cracks: bottom cracks and full cracks from the GPR scan images. The former represents partial cracks initiated from the bottom of the asphalt layer or base layers, while the latter includes extended cracks that penetrate these layers. Our experiments show the test average precisions of 0.769, 0.803 and 0.735 for all cracks, bottom cracks, and full cracks, respectively. This demonstrates the practicality of deep learning-based methods in detecting subsurface cracks from GPR scan images.

• International Journal of Industrial Entomology and Biomaterials
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• v.44 no.2
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• pp.37-43
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• 2022

Silkworm food has been found to be effective for dementia, liver function, lowering blood sugar, and possesses antioxidant properties, which has been attracting attention as a health functional food. In this study, methods for enhancing the functionality of silkworms were explored and the production potential of high-functional silkworms was analyzed. For enhancing antioxidant activity, ascorbic acid, caffeic acid, laminarin, and glutathione were injected or fed to 5th instar silkworms, and the antioxidant activity of silkworm extract was comparatively analyzed. There was no significant change in polyphenol and flavonoid content, but it was confirmed that 2,2-diphenyl-1-picrylhydrazyl radical scavenging ability, superoxide dismutase-like activity, and reducing power were slightly increased after injection of ascorbic acid, caffeic acid, and glutathione. To confirm the increase in antioxidant efficacy through feeding, an inducer was mixed with sucrose and sprayed on mulberry leaves. As a result, the growth rate of silkworms improved and all indicators of antioxidant activity were improved in silkworms fed with ascorbic acid and glutathione. Considering these results, producing high-functional silkworms was deemed possible.

• Proceedings of the Plant Resources Society of Korea Conference
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• v.18 no.1
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• pp.82-83
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• 2005

• Proceedings of the Korean Society of Crop Science Conference
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• 2005.04a
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• pp.116-117
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• 2005

• 한국약용작물학회:학술대회논문집
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• 2011.04a
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• pp.226-227
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• 2011

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.375-382
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• 2001

The agricultural machine estimated sales survey is a survey to estimate annual sales quantities of eight major agricultural machines such as tracter, combine, etc. The purpose of this study is to design a multipurpose sample for the agricultural machine estimated sales survey. Main achievements of this study are to present an efficient stratification criterion and to suggest a reasonable estimation method by using the concept of post-stratification.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2003.10a
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• pp.83-86
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• 2003

To estimate the nutrient loads from an agricultural watershed, SWAT model and Unit Loading Factor method which was proposed by Ministry of Environment were applied for study watershed. The observed hydrologic and water quality data were compared with estimated methods for the Balhan HP#6 study watershed having an area of $3.86km^2$. The estimated nutrient loads were found to be similar values with the observed.