• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1669-1683
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• 2021

AI intelligent agriculture and digital agriculture are important for the science of agriculture. Leaf chlorophyll contents(LCC) are one of the most important indicators to determine the growth status of vegetable crops. In this study, a support vector machine (SVM) regression model was produced using an unmanned aerial vehicle-based RGB camera and a multispectral (MSP) sensor for onions and garlic, and the LCC estimation applicability of the RGB camera was reviewed by comparing it with the MSP sensor. As a result of this study, the RGB-based LCC model showed lower results than the MSP-based LCC model with an average R² of 0.09, RMSE 18.66, and nRMSE 3.46%. However, the difference in accuracy between the two sensors was not large, and the accuracy did not drop significantly when compared with previous studies using various sensors and algorithms. In addition, the RGB-based LCC model reflects the field LCC trend well when compared with the actual measured value, but it tends to be underestimated at high chlorophyll concentrations. It was possible to confirm the applicability of the LCC estimation with RGB considering the economic feasibility and versatility of the RGB camera. The results obtained from this study are expected to be usefully utilized in digital agriculture as AI intelligent agriculture technology that applies artificial intelligence and big data convergence technology.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.4 s.316
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• pp.42-47
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• 2007

To reduce the chattering errors of reed switch sensors in the automatic remote measurement of water meter a reed switch sensor was analyzed and improved. The operation of reed switch sensors can be described as a mechanical contact switch by approximation of permanent magnet piece to generate an electrical pulse. The reed switch sensors are used mostly in measurement application to detect the rotational or translational displacement. To apply for water flow measurement devices, the reed switch sensors should keep high reliability. They are applied for the electronic digital type of water flow meters. The reed switch sensor is just mounted simply on the conventional mechanical type flow meter. A small magnet is attached on a pointer of the water meter counter rotor. Inside the reed sensor two steel leaf springs make mechanical contact and apart repeatedly as rotation of flow meter counter. The counting electrical contact pulses can be converted as the water flow amount. The MCU sends the digital flow rate data to the server using the wireless communication network. But the digital data is occurred difference or won by chattering noise. The reed switch sensor contains chattering error by it self at the force equivalent position. The vibrations such as passing vehicle near to the switch sensor installed location causes chattering. In order to reduce chattering error, most system uses just software methods, for example using filter algorithm and also statistical calibration methods. The chattering errors were reduced by changing leaf spring structure using mechanical characteristics.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.360-362
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• 2010

In this paper, the next generation of digital video media attention to the 3D digital stereoscopic images can be most easily produced and minimize the cost for technology that can be made three-dimensional imaging technique, one of the leaf anaglyph research on methods through the low cost of the optimal representation of the three-dimensional stereoscopic images, and enjoy watching, a technique is proposed.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.58-58
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• 2022

With the development of advanced technology, automation of agricultural work is spreading. In association with the 4th industrial revolution-based technology, research on field smart farm technology is being actively conducted. A state-of-the-art unmanned automated agricultural production demonstration complex was established in Naju-si, Jeollanam-do. For the operation of the demonstration area platform, it is necessary to build a sophisticated, advanced, and intelligent field smart farming model. For the operation of the unmanned automated agricultural production demonstration area platform, we are building data on the growth of soybean for smart cultivated crops and conducting research to determine the optimal time for agricultural work. In order to operate an unmanned automation platform, data is collected to discover digital factors for water management immediately after planting, water management during the growing season, and determination of harvest time. A subsurface drip irrigation system was established for smart water management. Irrigation was carried out when the soil moisture was less than 20%. For effective water management, soil moisture was measured at the surface, 15cm, and 30cm depth. Vegetation indices were collected using drones to find key factors in soybean production prediction. In addition, major growth characteristics such as stem length, number of branches, number of nodes on the main stem, leaf area index, and dry weight were investigated. By discovering digital factors for effective decision-making through data construction, it is expected to greatly enhance the efficiency of the operation of the unmanned automated agricultural production demonstration area.

• Journal of Food Hygiene and Safety
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• v.32 no.4
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• pp.306-313
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• 2017

This study systemically analyzed the data on the microbial levels in fresh vegetables in Korea to identify the points to control. We scanned the studies published between 2001 and 2015 in peer-reviewed research papers on the microbial levels in fresh vegetables produced in Korea. Plant products were categorized by using the US IFSAC (Interagency Food Safety Analytics Collaboration) category. The most consumed, the non-heat treated, the epidemiological foodborne diseases sources of fresh vegetable in foodservice (KCDC data) were identified by literature review. Articles were screened using National Digital Science Library (NDSL) search engine regarding to microbial hazards in plant products. Based on the total plate count number and coliforms on the 89 data cases from 26 published articles, the total plate count number was high in the order of sprouts, leaf and stem, bulbs and roots, vine-grown, solanaceous, melons, and pome. Escherichia coli was frequently detected in leaf and stem and sprouts products. Focused on the microbial data of leek, lettuce and cabbage, the levels of total plate count, coliforms and Bacillus cereus showed the levels of 4.15~7.69 log CFU/g, 1~6.99 log CFU/g, and 0.51~3.9 log CFU/g, respectively, by 33 published papers. The levels of environmental factors affecting the microbial safety of lettuce and leek before harvest were investigated. Manure, soil, hands, scale, gloves were the major potential microbial contamination points to control. In addition, GAP (good agricultural practice), microbial testing, and improvement of irrigation methods are required to provide the safer fresh produce.

• Journal of Biosystems Engineering
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• v.24 no.1
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• pp.25-30
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• 1999

The effectiveness of many greenhouse environment control methodologies depends on the growth information of crops. Acquisition of the growth information of crops requires a non-invasive and continuous monitoring method. Crop growth monitoring system using digital imaging technique was developed to conduct non-destructive and intact plant growth analyses. The monitoring system automatically measures crop growth information sends an appropriate control signal to the nutrient solution supplying system. To develop the monitoring system, a linear model that explains the relationship between the fresh weight and the top projected leaf area of a lettuce plant was developed from an experiment. The monitoring system was evaluated buy successive lettuce growing experiments. Results of the experiments showed that the developed system could estimate the fresh weight of lettuce from a lettuce image by using the linear model and generate an EC control signal according to the lettuce growth stage.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.81-81
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• 2022

Recently, many studies on big data based smart farming have been conducted. Research to quantify morphological characteristics using image data from various crops in smart farming is underway. Rice is one of the most important food crops in the world. Much research has been done to predict and model rice crop yield production. The number of productive tillers per plant is one of the important agronomic traits associated with the grain yield of rice crop. However, modeling the basic growth characteristics of rice requires accurate data measurements. The existing method of measurement by humans is not only labor intensive but also prone to human error. Therefore, conversion to digital data is necessary to obtain accurate and phenotyping quickly. In this study, we present an image-based method to predict leaf number and evaluate tiller number of individual rice crop using YOLOv5 deep learning network. We performed using various network of the YOLOv5 model and compared them to determine higher prediction accuracy. We ako performed data augmentation, a method we use to complement small datasets. Based on the number of leaves and tiller actually measured in rice crop, the number of leaves predicted by the model from the image data and the existing regression equation were used to evaluate the number of tillers using the image data.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.1
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• pp.13-19
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• 2020

This study compared RGB color images with canopy light interception (LI) and leaf area index (LAI) measurements for low cost and low labor. LAI and LI were measured from vertical gap fraction derived from top of digital image in soybean canopy cover (cv Daewonkong, Deapongkong and Pungsannamulkong). RGB color images, LAI, and LI were collected from V4.5 stage to R5stage. Image segmentation was based on excess green minus excess red index (ExG-ExR). There was a linear relationship between LAI measured with LI (r²=0.84). There was alinear relation ship between LI measured with canopy cover on image (CCI) (r²=0.94). There was a significant positive relationship(r²=0.74) between LAI and CCI at all grow ingseason. Therefore, it is expected that in the future, the RGB color image could be able to easily measure the LAI and the LI at low cost and low labor.

• Journal of Animal Science and Technology
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• v.51 no.1
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• pp.1-8
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• 2009

We generated 57,598 expressed sequence tags (ESTs) from 3 cDNA libraries of Hanwooo (Korean Cattle), fat, loin, liver. Liver, intermuscular fat and longissimus dorsi tissues were obtained from a 24-month-old Hanwoo steer immediately after slaughter. cDNA library was constructed according to the oligocapped method. The EST data were clustered and assembled into unique sequences, 4,759 contigs and 7,587 singletons. To carry out functional analysis, Gene Ontology annotation and identification of significant leaf nodes were performed that were detected by searching significant p-values from $2^{nd}$ level GO terms to leaf nodes using Bonferroni correction. We found that 13, 26 and 8 significant leaf nodes are unique in the transcripts according to 3 GO categories, molecular function, biological process and cellular component. Also digital gene expression profiling using the Audic's test was performed and tissue specific genes were detected in the above 3 libraries.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.248-251
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• 2002

The intensity modulated radiation therapy (IMRT) with a multileaf collimator (MLC) requires the conversion of a radiation fluence map into a leaf sequence file that controls the movement of the MLC during radiation treatment of patients. Patient dose verification is clinically one of the most important parts in the treatment delivery of the radiation therapy. The three dimensional (3D) reconstruction of dose distribution delivered to the target helps to verify patient dose and to determine the physical characteristics of beams used in IMRT. A new method is presented for the pretreatment dosimetric verification of two dimensional distributions of photon intensity by means of Beam Intensity Scanner System (BISS) as a radiation detector with a custom-made software for dose calculation of fluorescence signals from scintillator. The scintillator is used to produce fluorescence from the irradiation of 6MV photons on a Varian Clinac 21EX. The BISS reproduces 3D- relative dose distribution from the digitized fluoroscopic signals obtained by digital video camera-based scintillator(DVCS) device in the IMRT. For the intensity modulated beams (IMBs), the calculations of absorbed dose are performed in absolute beam fluence profiles which are used for calculation of the patient dose distribution. The 3D-dose profiles of the IMBs with the BISS were demonstrated by relative measurements of photon beams and shown good agreement with radiographic film. The mechanical and dosimetric properties of the collimating of dynamic and/or step MLC system alter the generated intensity. This is mostly due to leaf transmission, leaf penumbra and geometry of leaves. The variations of output according to the multileaf opening during the irradiation need to be accounted for as well. These phenomena result in a fluence distribution that can be substantially different from the initial and calculative intensity modulation and therefore, should be taken into account by the treatment planning for accurate dose calculations delivered to the target volume in IMRT.