• 한국작물학회지
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• 제49권spc1호
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• pp.243-249
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• 2004

• 농업과학연구
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• 제39권3호
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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.

• 한국농공학회논문집
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• 제48권7호
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• pp.17-22
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• 2006

Potential application topics of KOMPSAT-3 image in the field of precision agriculture are suggested. The topics can be categorized as fundamental and applied ones that have contents of static and dynamic characteristics respectively. As fundamental topics, precision information of agriculture that is related to farmland and its crop attributes, precision information of rural infrastructure that is related to rural village and its facilities, precision information of stream environment that is related to rural water resources and its facilities, and precision information of eco-environment that is especially related to riparian ecology and environmental status are included. As applied topics, precision rural water resources that has thematic contents of continuous and event-based runoff, spatial and temporal soil moisture and evapotranspiration, precision agricultural watershed environment that has the contents of spatial and temporal soil loss, sediment and pollutants transport, and precision temporal and spatial crop growth that has the contents of temporal crop texture, spectral reflectance, leaf area index, spatial crop protein information.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume I
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• pp.432-435
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• 2006

Potential application topics of KOMPSAT-3 image in the field of precision agriculture are suggested. The topics can be categorized as fundamental and applied ones that have contents of static and dynamic characteristics respectively. As fundamental topics, precision information of agriculture that is related to farmland and its crop attributes, precision information of rural infrastructure that is related to rural village and its facilities, precision information of stream environment that is related to rural water resources and its facilities, and precision information of eco-environment that is especially related to riparian ecology and environmental status are included. As applied topics, precision rural water resources that has thematic contents of continuous and event-based runoff, spatial and temporal soil moisture and evapotranspiration, precision agricultural watershed environment that has the contents of spatial and temporal soil loss, sediment and pollutants transport, and precision temporal and spatial crop growth that has the contents of temporal crop texture, spectral reflectance, leaf area index, spatial crop protein information.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.504-507
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• 2009

Color chart area is automatically extracted in image that captured a crop such as fruits with the color chart, and an approximation formula is obtained for the change in feature value of the color indexes. Comparison is made with the color value of the crop area, and the growing degree is assessed according to the correlation. Using a compact PC equipped with the program, image of fruits is captured, and the output value obtained by the system is compared to the rating by expert. In the automatic recognition of the color chart out of doors, the complete color indexes is correctly acquired in 22 of 29 images. And indoors, they are correctly acquired in all of 34 images. In the color value judgment of the Japanese pear, indoors, 32 of 34 images is within 1.0 of the judgment error (compared the value read off by experts), the average error is about 0.5. These results indicate a practicable value.

• Journal of Biosystems Engineering
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• 제36권3호
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• pp.180-186
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• 2011

The objective of this research was to establish the estimation method of growth information on potato using Multi-Spectral Image Sensor (MSIS) and Global Positioning System (GPS). And growth estimation map for determining a prescription map over the entire field was generated. To determine the growth model, 10 ground-truth points of areas of $4m^2$ each were selected and investigated. The growth information included stem number, crop height and SPAD value. In addition, images information involving the ground-truth points were also taken by an unmanned helicopter, and reflectance value of Green, Red, and NIR bands were calculated with image processing. Then, growth status of potato was modeled by multi-regression analysis using these reflectance value of Green, Red, and NIR. As a result, potato growth information could be detected by analyzing Green, Red, and NIR images. Stem number, crop height and SPAD value could be estimated with $R^2$ values of 0.600, 0.657 and 0.747 respectively. The generated GIS map would describe variability of the potato growth in a whole field.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2007년도 6th OSONG INTERNATIONAL BIO-SYMPOSIUM
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• pp.204.1-204.1
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• 2007

• 한국작물학회:학술대회논문집
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• 한국작물학회 2023년도 춘계학술대회
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• pp.118-118
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• 2023

• 대한원격탐사학회지
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• 제20권3호
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• pp.175-188
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• 2004

The objectives of this study were to estimate leaf area index (LAI) as a function of image-derived vegetation indices, and to compare measured and estimated LAI to the results of crop model simulation. Soil moisture, crop phenology, and LAI data were obtained several times during the 2001 growing season at monitoring sites established in two central Missouri experimental fields, one planted to com (Zea mays L.) and the other planted to soybean (Glycine max L.). Hyper- and multi-spectral images at varying spatial. and spectral resolutions were acquired from both airborne and satellite platforms, and data were extracted to calculate standard vegetative indices (normalized difference vegetative index, NDVI; ratio vegetative index, RVI; and soil-adjusted vegetative index, SAVI). When comparing these three indices, regressions for measured LAI were of similar quality $(r^2$ =0.59 to 0.61 for com; $r^2$ =0.66 to 0.68 for soybean) in this single-year dataset. CERES(Crop Environment Resource Synthesis)-Maize and CROPGRO-Soybean models were calibrated to measured soil moisture and yield data and used to simulate LAI over the growing season. The CERES-Maize model over-predicted LAI at all corn monitoring sites. Simulated LAI from CROPGRO-Soybean was similar to observed and image-estimated LA! for most soybean monitoring sites. These results suggest crop growth model predictions might be improved by incorporating image-estimated LAI. Greater improvements might be expected with com than with soybean.

• 농촌계획
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• 제28권1호
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• pp.57-69
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• 2022

Crop classification is very important for estimating crop yield and figuring out accurate cultivation area. The purpose of this study is to classify crops harvested in fall in Idam-ri, Goesan-gun, Chungcheongbuk-do by using unmanned aerial vehicle (UAV) images and support vector machine (SVM) model. The study proceeded in the order of image acquisition, variable extraction, model building, and evaluation. First, RGB and multispectral image were acquired on September 13, 2021. Independent variables which were applied to Farm-Map, consisted gray level co-occurrence matrix (GLCM)-based texture characteristics by using RGB images, and multispectral reflectance data. The crop classification model was built using texture characteristics and reflectance data, and finally, accuracy evaluation was performed using the error matrix. As a result of the study, the classification model consisted of four types to compare the classification accuracy according to the combination of independent variables. The result of four types of model analysis, recursive feature elimination (RFE) model showed the highest accuracy with an overall accuracy (OA) of 88.64%, Kappa coefficient of 0.84. UAV-based RGB and multispectral images effectively classified cabbage, rice and soybean when the SVM model was applied. The results of this study provided capacity usefully in classifying crops using single-period images. These technologies are expected to improve the accuracy and efficiency of crop cultivation area surveys by supplementing additional data learning, and to provide basic data for estimating crop yields.