• Proceedings of the Korean Society of Crop Science Conference
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• 1996.05a
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• pp.40-41
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• 1996

• Proceedings of the Korean Society of Crop Science Conference
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• 2007.11a
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• pp.227.2-227.2
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• 2007

• Proceedings of the Korean Society of Crop Science Conference
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• 2004.04a
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• pp.86-87
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• 2004

• Proceedings of the Korean Society of Crop Science Conference
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• 2004.04a
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• pp.88-89
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• 2004

• Proceedings of the Korean Society of Crop Science Conference
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• 1996.10a
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• pp.54-55
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• 1996

• 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.

• 한국약용작물학회:학술대회논문집
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• 2008.11a
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• pp.479-480
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• 2008

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

• Korean Journal of Agricultural Science
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• v.39 no.3
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• pp.407-412
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• 2012

Soil texture has an important influence on agriculture such as crop selection, movement of nutrient and water, soil electrical conductivity, and crop growth. Conventionally, soil texture has been determined in the laboratory using pipette and hydrometer methods requiring significant amount of time, labor, and cost. Recently, in-situ soil texture classification systems using optical diffuse reflectometry or mechanical resistance have been reported, especially for precision agriculture that needs more data than conventional agriculture. This paper is a part of overall research to develop an in-situ soil texture classification system using image processing. Issues investigated in this study were effects of sensor travel speed and light source and intensity on image quality. When travel speed of image sensor increased from 0 to 10 mm/s, travel distance and number of pixel were increased to 3.30 mm and 9.4, respectively. This travel distances were not negligible even at a speed of 2 mm/s (i.e., 0.66 mm and 1.4), and image degradation was significant. Tests for effects of illumination intensity showed that 7 to 11 Lux seemed a good condition minimizing shade and reflection. When soil water content increased, illumination intensity should be greater to compensate decrease in brightness. Results of the paper would be useful for construction, test, and application of the sensor.

• Korean Journal of Medicinal Crop Science
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• v.10 no.1
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• pp.58-67
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• 2002

Pen-tsao-kang-mu(Bon-cho-kang-mok), chinese medicinal plant book, was written by Si-jin Lee, 1578, China. The subject of this study were 210 articles of grain part in Pen-tsao-kang-mu. Among them only 193 articles were able to be identified by authority of several references. By Engler's system they were classified into 4 divisions, 5 classes, 3 subclasses, 28 orders, 17 suborders, 46 families, 95 genera, 100 species, 11 varieties and 1 form, and were confirmed 112 kinds of original plants. Among the divisions, angiospermae was the most numerous division with 107 kinds(91.98%) and the second division was gymnospermae with 3 kinds(2.70%). The next were phaeophyta and fungi with 1 kind(0.90%) on each. Other 17 articles were unable to be classified because of their ambiguous name.