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

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.89-89
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• 2022

Soybean (Glycine max (L.) Merr.) is a representative food resource. To preserve the integrity of soybean, it is necessary to protect soybean yield and seed quality from threats of various pests and diseases. Riptortus pedestris is a well-known insect pest that causes the greatest loss of soybean yield in South Korea. This pest not only directly reduces yields but also causes disorders and diseases in plant growth. Unfortunately, no resistant soybean resources have been reported. Therefore, it is necessary to identify the distribution and movement of Riptortus pedestris at an early stage to reduce the damage caused by insect pests. Conventionally, the human eye has performed the diagnosis of agronomic traits related to pest outbreaks. However, due to human vision's subjectivity and impermanence, it is time-consuming, requires the assistance of specialists, and is labor-intensive. Therefore, the responses and behavior patterns of Riptortus pedestris to the scent of mixture R were visualized with a 3D model through the perspective of artificial intelligence. The movement patterns of Riptortus pedestris was analyzed by using time-series image data. In addition, classification was performed through visual analysis based on a deep learning model. In the object tracking, implemented using the YOLO series model, the path of the movement of pests shows a negative reaction to a mixture Rina video scene. As a result of 3D modeling using the x, y, and z-axis of the tracked objects, 80% of the subjects showed behavioral patterns consistent with the treatment of mixture R. In addition, these studies are being conducted in the soybean field and it will be possible to preserve the yield of soybeans through the application of a pest control platform to the early stage of soybeans.

• Journal of Bio-Environment Control
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• v.25 no.2
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• pp.106-110
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• 2016

In this study, a planting density-growth-harvest (PGH) chart was developed to easily read the growth and harvest factors such as crop growth rate, relative growth rate, shoot fresh weight, shoot dry weight, harvesting time, marketable rate, and marketable yield of common ice plant (Mesembryanthemum crystallinum L.). The plants were grown in a nutrient film technique (NFT) system in a closed-type plant factory using fluorescent lamps with three-band radiation under a light intensity of $140{\mu}mol{\cdot}m^{-2}{\cdot}s^{-1}$ and a photoperiod of 12 h. Growth and yield were analyzed under four planting densities ($15{\times}10cm$, $15{\times}15cm$, $15{\times}20cm$, and $15{\times}25cm$). Shoot fresh and dry weights per plant increased at a higher planting density until reached an upper limit and yield per area was also same tendency. Crop growth rate, relative growth rate and lost time were described using quadratic equation. A linear relationship between shoot dry weight and fresh weights was observed. PGH chart was constructed based on the growth data and making equations. For instance, with within row spacing (= 20 cm) and fresh weight per plant at harvest (= 100 g), we can estimate all the growth and harvest factors of common ice plant. The planting density, crop growth rate, relative growth rate, lost time, shoot dry weight per plant, harvesting time, and yield were $33plants/m^2$, $20g{\cdot}m^{-2}{\cdot}d^{-1}$, $0.27g{\cdot}g^{-1}{\cdot}d^{-1}$, 22 days, 2.5 g/plant, 26 days after transplanting, and $3.2kg{\cdot}m^{-2}$, respectively. With this chart, we could easily obtain the growth factors such as planting density, crop growth rate, relative growth rate, lost time and the harvest factors such as shoot fresh and dry weights, harvesting time, marketable rate, and marketable yield with at least two parameters, for instance, planting distance and one of harvest factors of plant. PGH charts will be useful tools to estimate the growth and yield of crops and to practical design of a closed-type plant production system.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.59 no.1
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• pp.89-96
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• 2014

We aimed to evaluate the effects of climatic elements on potato yield and create a model with climatic elements for estimating the potato yield, using the results of the regional adjustment tests of potato. We used 86 data of the yield data of a potato variety, Sumi, from 17 regions over 11 years. According to the results, the climatic elements showed significant level of correlation coefficient with marketable yield appeared to be almost every climatic elements except wind velocity, which was daily average air temperature (Tave), daily minimum air temperature (Tmin), daily maximum air temperature(Tmax), daily range of air temperature (Tm-m), precipitation (Prec.), relative humidity (R.H.), sunshine hours (S.H.) and days of rain over 0.1 mm (D.R.) depending on the periods of days after planting or before harvest. The correlations between these climatic elements and marketable yield of potato were stepwised using SAS, statistical program, and we selected a model to predict the yield of marketable potato, which was $y=7.820{\times}Tmax_-1-6.315{\times}Prec_-4+128.214{\times}DR_-8+91.762{\times}DR_-3+643.965$. The correlation coefficient between the yield derived from the model and the real yield of marketable yield was 0.588 (DF 85).

• Smart Media Journal
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• v.9 no.4
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• pp.176-186
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• 2020

The size and shape of crops are diverse, and the growing environment is also different. Therefore, when one uses a drone to spray pesticides, the characteristics of each crop must be considered, and flight conditions such as the flight height and forwarding velocity of the drone should be changed. The droplet flow of pesticides is affected by various flight conditions, and a large change occurs in the sprayed area. As a result, an uneven distribution of liquid may be formed at the wake, and the transport efficiency will be decreased as well as there would be a risk of toxic scatter. Therefore, this paper analyzes the degree of distribution of pesticides to the crops through numerical analysis when pesticide is sprayed onto the selected three crops with different characteristics by using agricultural drones with different flight conditions. On the purpose of establishing a guideline for spraying pesticides using a drone in accordance with the characteristics of crops, this paper compares the amount of pesticides distributed in the crops at the wake of nozzle flow using the figure of merit, and the sum of transported liquid rate divided by the root mean square of the probability density function.

• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.3
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• pp.183-196
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• 2010

Increasing human activity due to rapid economic growth and land use change alters the patterns of the Asian monsoon, which is key to crop yields in Asia. In this study, we tested the performance of regional climate model (RegCM3) by simulating important components of Indian summer monsoon, including land-ocean contrast, low level jet (LLJ), Tibetan high and upper level Easterly Jet. Three contrasting rain years (1994: excess year, 2001: normal year, 2002: deficient year) were selected and RegCM3 was integrated at 60 km horizontal resolution from April 1 to October 1 each year. The simulated fields of circulations and precipitation were validated against the observation from the NCEP/NCAR reanalysis products and Global Precipitation Climatology Centre (GPCC), respectively. The important results of RegCM3 simulations are (a) LLJ was slightly stronger and split into two branches during excess rain year over the Arabian Sea while there was no splitting during normal and deficient rain years, (b) huge anticyclone with single cell was noted during excess rain year while weak and broken into two cells in deficient rain year, (c) the simulated spatial distribution of precipitation was comparable to the corresponding observed precipitation of GPCC over large parts of India, and (d) the sensitivity experiment using NIMBUS-7 SMMR snow data indicated that precipitation was reduced mainly over the northeast and south Peninsular India with the introduction of 0.1 m of snow over the Tibetan region in April.

• Journal of Korea Water Resources Association
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• v.50 no.6
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• pp.387-395
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• 2017

Sea level rise, accompanied by climate change, is expected to exacerbate seawater intrusion in the coastal groundwater system. As the salinity of saturated groundwater increases, salinity can increase even in the unsaturated soil above the groundwater surface, which may cause crop damage in the agricultural land. The other adverse impact of sea level rise is reduced unsaturated soil thicknesses. In this study, a composite model to assess impacts of sea level rise in coastal agricultural land is proposed. The composite model is based on the combined applications of a three dimensional model for simulating saltwater intrusion into the groundwater and a vertical one dimensional model for simulating unsaturated zone flow and transport. The water level and salinity distribution of groundwater are calculated using the three dimensional seawater intrusion model. At some uppermost nodes, where salinity are higher than the reference value, of the 3D mesh one dimensional unsaturated zone modeling is conducted along the soil layer between the ground water surface and the ground surface. A particular location is judged salinized when the concentration at the root-zone depth exceeds the tolerable salinity for ordinary crops. The developed model is applied to a hypothetical agricultural reclamation land. IPCC RCP 4.5 and 8.5 scenarios were used as sea level rise data. Results are presented for 2050 and 2100. As a result of the study, it is predicted that by 2100 in the climate change scenario RCP 8.5, there will be 7.8% increase in groundwater saltwater-intruded area, 6.0% increase of salinized soil area, and 1.6% in increase in water-logging area.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.485-496
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• 2022

Modern agriculture is being transformed into smart agriculture to maximize production efficiency along with changes in the 4th industrial revolution. However, rural areas in Korea are facing challenges of aging, low fertility, and population outflow, making it difficult to transition to smart agriculture. Among ICT technologies, simulation allows users to observe or experience the results of their choices through imitation or reproduction of reality. The combination of the three-dimension (3D) model and the greenhouse simulator enable a 3D experience by virtual greenhouse for fruits and vegetable cultivation. At the same time, it is possible to visualize the greenhouse under various cultivation or climate conditions. The objective of this study is to apply the greenhouse climate management model for simulation development that can visually see the state of the greenhouse environment under various micrometeorological properties. The numerical solution with the mathematical model provided a dynamic change in the greenhouse environment for a particular greenhouse design. Light intensity, crop transpiration, heating load, ventilation rate, the optimal amount of CO₂ enrichment, and daily light integral were calculated with the simulation. The results of this study are being built so that users can be linked through a web page, and software will be designed to reflect the characteristics of cladding materials and greenhouses, cultivation types, and the condition of environmental control facilities for customized environmental control. In addition, environmental information obtained from external meteorological data, as well as recommended standards and set points for each growth stage based on experiments and research, will be provided as optimal environmental factors. This simulation can help growers, students, and researchers to understand the ICT technologies and the changes in the greenhouse microclimate according to the growing conditions.